@Article{info:doi/10.2196/69936,
author="Wang, Tingting
and Jiang, Jinxia
and Song, Zihe
and Liu, Xianliang
and Zhong, Minhui
and Yu, Chan
and Zhang, Runa
and Duan, Xia",
title="Prevalence of Frailty and Its Predictors Among Patients With Cancer at the Chemotherapy Stage: Systematic Review",
journal="JMIR Cancer",
year="2025",
month="Jul",
day="24",
volume="11",
pages="e69936",
keywords="cancer",
keywords="frailty",
keywords="chemotherapy",
keywords="influencing factor",
keywords="systematic review",
abstract="Background: Chemotherapy causes physiological, psychological, and social impairments in patients with cancer. Frailty reduces the effectiveness of chemotherapy and increases the toxicity associated with radiotherapy and chemotherapy, the possibility of chemotherapy failure, and adverse outcomes. However, factors affecting chemotherapy-related frailty in patients with cancer remain unclarified. Objective: This systematic review aimed to identify risk factors driving frailty progression during chemotherapy in patients with cancer. Methods: A comprehensive systematic search was conducted on PubMed, Web of Science, Embase, China National Knowledge Infrastructure, China Science and Technology Journal Database (VIP), and SinoMed for observational studies (cohort, cross-sectional, or case-control) on factors affecting the debility-of-chemotherapy stage in patients with cancers between the inception of the database and February 2025, with an updated search executed in May 2025. Literature screening, quality evaluation using the Newcastle-Ottawa Scale and Agency for Healthcare Research and Quality checklist, and data extraction were conducted independently by 2 authors. Meta-analysis, effect size combination, sensitivity analysis, and publication bias analysis were performed using RevMan (version 5.4; The Cochrane Collaboration) and R (version 4.4.3; R Foundation). Results: The analysis comprised 14 studies (8 cross-sectional, 2 repeated cross-sectional, 3 cohort, and 1 mixed-design), including 3879 patients with cancer and 23 influencing factors. Methodological quality assessment using Agency for Healthcare Research and Quality (mean 8.8, SD 1.3, 95\% CI 7.9?9.7; SE 0.4) and Newcastle-Ottawa Scale (mean 8.0, SD 1.0, 95\% CI 6.7?9.3; SE 0.6) revealed 73\% (8/11) of cross-sectional studies as high-quality. The meta-analysis showed a 35\% (95\% CI 22\%?50\%) prevalence of frailty during chemotherapy in these patients. Cancer stage (odds ratio 1.99, 95\% CI 1.64?2.42), chemotherapy frequency (odds ratio 2.60, 95\% CI 1.83?3.70), transfer (odds ratio 2.18, 95\% CI 1.50?3.17), hemoglobin (odds ratio 0.29, 95\% CI 0.18?0.47), white blood cell (odds ratio 0.37, 95\% CI 0.21?0.65), comorbidity (odds ratio 1.93, 95\% CI 1.30?2.86), and hypoproteinemia (odds ratio 1.74, 95\% CI 1.31?2.30) were risk factors for frailty in patients at the chemotherapy stage. Conclusions: Frailty during chemotherapy was strongly associated with advanced cancer stage, frequent treatment cycles, metastasis, anemia, leukopenia, comorbidities, and hypoproteinemia. Clinically actionable findings emphasized hemoglobin and albumin monitoring as preventive targets, while heterogeneity in assessment tools and population bias limited generalizability. The integration of frailty screening into chemotherapy workflows is urgent to mitigate treatment-related functional decline. Trial Registration: PROSPERO CRD42024528132; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024528132 ",
doi="10.2196/69936",
url="https://cancer.jmir.org/2025/1/e69936"
}


@Article{info:doi/10.2196/65820,
author="Song, Gao
and Zhang, Cai-qiong
and Bai, Zhong-ping
and Li, Rong
and Cheng, Meng-qun",
title="Assisted Reproductive Technology and Risk of Childhood Cancer Among the Offspring of Parents With Infertility: Systematic Review and Meta-Analysis",
journal="JMIR Cancer",
year="2025",
month="Mar",
day="12",
volume="11",
pages="e65820",
keywords="assisted reproductive technology",
keywords="childhood cancer",
keywords="infertility",
keywords="subfertile",
keywords="risks",
keywords="systematic review",
abstract="Background: The relationship between assisted reproductive technology (ART) and childhood cancer risk has been widely debated. Previous meta-analyses did not adequately account for the impact of infertility, and this study addresses this gap. Objective: Our primary objective was to assess the relative risk (RR) of childhood cancer in infertile populations using ART versus non-ART offspring, with a secondary focus on comparing frozen embryo transfer (FET) and fresh embryo transfer (fresh-ET). Methods: A literature review was conducted through PubMed, Embase, Cochrane, and Web of Science, with a cutoff date of July 10, 2024. The study was registered with the International Platform of Registered Systematic Review and Meta-Analysis Protocols (INPLASY 202470119). Inclusion criteria were based on the PICOS (Population, Intervention, Comparison, Outcomes, and Study Design) framework: infertile or subfertile couples (population), ART interventions (in vitro fertilization [IVF], intracytoplasmic sperm injection [ICSI], FET, and fresh-ET), non-ART comparison, and childhood cancer risk outcomes. Data abstraction focused on the primary exposures (ART vs non-ART and FET vs fresh-ET) and outcomes (childhood cancer risk). The risk of bias was assessed using the Newcastle-Ottawa Quality Assessment Scale, and the evidence quality was evaluated with the Grading of Recommendations Assessment, Development, and Evaluation (GRADE). Pooled estimates and 95\% CIs were calculated using random effects models. Results: A total of 18 studies were included, published between 2000 and 2024, consisting of 14 (78\%) cohort studies and 4 (22\%) case-control studies, all of which were of moderate to high quality. The cohort studies had follow-up periods ranging from 3 to 18 years. Compared with non-ART conception, ART conception was not significantly associated with an increased risk of childhood overall cancer (RR 0.95, 95\% CI 0.71?1.27; GRADE quality: low to moderate). Subgroup analyses of IVF (RR 0.86, 95\% CI 0.59?1.25), ICSI (RR 0.76, 95\% CI 0.26?2.2), FET (RR 0.98, 95\% CI 0.54?1.76), and fresh-ET (RR 0.75, 95\% CI 0.49?1.15) showed similar findings. No significant differences were found for specific childhood cancers, including leukemia (RR 0.99, 95\% CI 0.79?1.24), lymphoma (RR 1.22, 95\% CI 0.64?2.34), brain cancer (RR 1.22, 95\% CI 0.73?2.05), embryonal tumors (RR 1, 95\% CI 0.63?1.58), retinoblastoma (RR 1.3, 95\% CI 0.73?2.31), and neuroblastoma (RR 1.02, 95\% CI 0.48?2.16). Additionally, no significant difference was observed in a head-to-head comparison of FET versus fresh-ET (RR 0.99, 95\% CI 0.86?1.14; GRADE quality: moderate). Conclusions: In conclusion, this study found no significant difference in the risk of childhood cancer between offspring conceived through ART and those conceived through non-ART treatments (such as fertility drugs or intrauterine insemination) in infertile populations. While infertility treatments may elevate baseline risks, our findings suggest that whether individuals with infertility conceive using ART or non-ART methods, their offspring do not face a significantly higher risk of childhood cancer. Further research, especially comparing infertile populations who conceive naturally, is needed to better understand potential long-term health outcomes. Trial Registration: INPLASY 202470119; https://inplasy.com/?s=202470119 ",
doi="10.2196/65820",
url="https://cancer.jmir.org/2025/1/e65820"
}


@Article{info:doi/10.2196/65984,
author="Chen, David
and Alnassar, Addeen Saif
and Avison, Elizabeth Kate
and Huang, S. Ryan
and Raman, Srinivas",
title="Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review",
journal="JMIR Cancer",
year="2025",
month="Mar",
day="28",
volume="11",
pages="e65984",
keywords="artificial intelligence",
keywords="chatbot",
keywords="data extraction",
keywords="AI",
keywords="conversational agent",
keywords="health information",
keywords="oncology",
keywords="scoping review",
keywords="natural language processing",
keywords="NLP",
keywords="large language model",
keywords="LLM",
keywords="digital health",
keywords="health technology",
keywords="electronic health record",
abstract="Background: Natural language processing systems for data extraction from unstructured clinical text require expert-driven input for labeled annotations and model training. The natural language processing competency of large language models (LLM) can enable automated data extraction of important patient characteristics from electronic health records, which is useful for accelerating cancer clinical research and informing oncology care. Objective: This scoping review aims to map the current landscape, including definitions, frameworks, and future directions of LLMs applied to data extraction from clinical text in oncology. Methods: We queried Ovid MEDLINE for primary, peer-reviewed research studies published since 2000 on June 2, 2024, using oncology- and LLM-related keywords. This scoping review included studies that evaluated the performance of an LLM applied to data extraction from clinical text in oncology contexts. Study attributes and main outcomes were extracted to outline key trends of research in LLM-based data extraction. Results: The literature search yielded 24 studies for inclusion. The majority of studies assessed original and fine-tuned variants of the BERT LLM (n=18, 75\%) followed by the Chat-GPT conversational LLM (n=6, 25\%). LLMs for data extraction were commonly applied in pan-cancer clinical settings (n=11, 46\%), followed by breast (n=4, 17\%), and lung (n=4, 17\%) cancer contexts, and were evaluated using multi-institution datasets (n=18, 75\%). Comparing the studies published in 2022?2024 versus 2019?2021, both the total number of studies (18 vs 6) and the proportion of studies using prompt engineering increased (5/18, 28\% vs 0/6, 0\%), while the proportion using fine-tuning decreased (8/18, 44.4\% vs 6/6, 100\%). Advantages of LLMs included positive data extraction performance and reduced manual workload. Conclusions: LLMs applied to data extraction in oncology can serve as useful automated tools to reduce the administrative burden of reviewing patient health records and increase time for patient-facing care. Recent advances in prompt-engineering and fine-tuning methods, and multimodal data extraction present promising directions for future research. Further studies are needed to evaluate the performance of LLM-enabled data extraction in clinical domains beyond the training dataset and to assess the scope and integration of LLMs into real-world clinical environments. ",
doi="10.2196/65984",
url="https://cancer.jmir.org/2025/1/e65984"
}


@Article{info:doi/10.2196/64208,
author="Liao, Wan-Chuen
and Angus, Fiona
and Conley, Jane
and Chen, Li-Chia",
title="The Efficacy of Digital Interventions on Adherence to Oral Systemic Anticancer Therapy Among Patients With Cancer: Systematic Review and Meta-Analysis",
journal="JMIR Cancer",
year="2025",
month="Apr",
day="16",
volume="11",
pages="e64208",
keywords="efficacy",
keywords="digital interventions",
keywords="oral systemic anticancer therapy",
keywords="medication adherence",
keywords="cancer",
keywords="oral",
keywords="patients with cancer",
keywords="therapy",
keywords="systematic review",
keywords="meta-analysis",
keywords="care plans",
keywords="medication",
keywords="treatments",
keywords="mobile app",
keywords="mobile applications",
keywords="mHealth",
keywords="multimedia platforms",
keywords="digital technology",
keywords="self-reported",
keywords="mobile phone",
abstract="Background: Digital interventions have been increasingly applied in multidisciplinary care plans to improve medication adherence to oral systemic anticancer therapy (SACT), the crucial lifesaving treatments for many cancers. However, there is still a lack of consensus on the efficacy of those digital interventions. Objectives: This systematic review and meta-analysis aimed to investigate the efficacy of digital interventions in improving adherence to oral SACTs in patients with cancer. Methods: This systematic review and meta-analysis followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement guidelines. The protocol has been registered at PROSPERO (no. CRD42024550203). Fully published, randomized controlled trials (RCTs) in English on adults with cancer assessing digital interventions for improving adherence to oral SACTs were retrieved from MEDLINE, Embase, APA PsycINFO, and CINAHL Plus up to May 31, 2024. Adherence measures compared between digital intervention users and nonusers were extracted. The proportions of poor adherence were synthesized using a random-effects model. The pooled results were reported as the odds ratio and 95\% CI. The heterogeneity was assessed with the I2 test (\%). The mean difference and 95\% CI were calculated from the mean adherence score and SD. A risk of bias assessment was conducted using version 2 of the Cochrane Risk of Bias Assessment Tool (RoB 2) for RCTs, which ensured that a quality assessment of all included studies was conducted as recommended by the Cochrane Collaboration. Results: This study included 13 RCTs on digital interventions for improving adherence to oral SACTs in patients with cancer. The 13 RCTs, published between 2016 and 2024, were conducted in the United States, South Korea, France, Egypt, Finland, Australia, Colombia, Singapore, and Turkey. The technologies used were mobile apps (n=4), reminder systems (n=4), telephone follow-ups (n=3), and interactive multimedia platforms (n=2). Adherence was measured by surveys (n=8), relative dose intensity (n=2), pill count (n=1), self-reported missed doses (n=1), a smart pill bottle (n=1), and urine aromatase inhibitor metabolite assays (n=1). Concerns regarding risk of bias primarily involved randomization, missing outcome data, and outcome measurement, including nonblinded randomization, subjective patient-reported data, and difficulties in distinguishing between missed appointments and actual medication nonadherence. Pooled results from 11 trials showed that digital technology users had significantly lower risk of poor adherence (odds ratio 0.60, 95\% CI 0.47?0.77). Two studies reported positive mean differences in adherence scores comparing digital intervention users and nonusers. However, due to considerable heterogeneity (I{\texttwosuperior}=73.1\%), it is difficult to make a definitive conclusion from the pooled results about the effect of digital interventions upon adherence to oral anticancer therapy. Conclusions: Digital intervention users exhibited significantly lower risk of poor oral SACTs adherence than nonusers. Acknowledging individual variation and tailoring digital technologies to prioritize patient needs is essential. Trial Registration: PROSPERO CRD42024550203; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024550203 ",
doi="10.2196/64208",
url="https://cancer.jmir.org/2025/1/e64208"
}


@Article{info:doi/10.2196/63964,
author="Mushcab, Hayat
and Al Ramis, Mohammed
and AlRujaib, Abdulrahman
and Eskandarani, Rawan
and Sunbul, Tamara
and AlOtaibi, Anwar
and Obaidan, Mohammed
and Al Harbi, Reman
and Aljabri, Duaa",
title="Application of Artificial Intelligence in Cardio-Oncology Imaging for Cancer Therapy--Related Cardiovascular Toxicity: Systematic Review",
journal="JMIR Cancer",
year="2025",
month="May",
day="9",
volume="11",
pages="e63964",
keywords="artificial intelligence",
keywords="cardiology",
keywords="oncology",
keywords="cancer therapy--induced",
keywords="cardiotoxicity",
keywords="cardiovascular toxicity",
keywords="machine learning",
keywords="imaging",
keywords="radiology",
abstract="Background: Artificial intelligence (AI) is a revolutionary tool yet to be fully integrated into several health care sectors, including medical imaging. AI can transform how medical imaging is conducted and interpreted, especially in cardio-oncology. Objective: This study aims to systematically review the available literature on the use of AI in cardio-oncology imaging to predict cardiotoxicity and describe the possible improvement of different imaging modalities that can be achieved if AI is successfully deployed to routine practice. Methods: We conducted a database search in PubMed, Ovid MEDLINE, Cochrane Library, CINAHL, and Google Scholar from inception to 2023 using the AI research assistant tool (Elicit) to search for original studies reporting AI outcomes in adult patients diagnosed with any cancer and undergoing cardiotoxicity assessment. Outcomes included incidence of cardiotoxicity, left ventricular ejection fraction, risk factors associated with cardiotoxicity, heart failure, myocardial dysfunction, signs of cancer therapy--related cardiovascular toxicity, echocardiography, and cardiac magnetic resonance imaging. Descriptive information about each study was recorded, including imaging technique, AI model, outcomes, and limitations. Results: The systematic search resulted in 7 studies conducted between 2018 and 2023, which are included in this review. Most of these studies were conducted in the United States (71\%), included patients with breast cancer (86\%), and used magnetic resonance imaging as the imaging modality (57\%). The quality assessment of the studies had an average of 86\% compliance in all of the tool's sections. In conclusion, this systematic review demonstrates the potential of AI to enhance cardio-oncology imaging for predicting cardiotoxicity in patients with cancer. Conclusions: Our findings suggest that AI can enhance the accuracy and efficiency of cardiotoxicity assessments. However, further research through larger, multicenter trials is needed to validate these applications and refine AI technologies for routine use, paving the way for improved patient outcomes in cancer survivors at risk of cardiotoxicity. Trial Registration: PROSPERO CRD42023446135; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023446135 ",
doi="10.2196/63964",
url="https://cancer.jmir.org/2025/1/e63964"
}


@Article{info:doi/10.2196/67131,
author="Shen, Tong Chun
and Shi, Jian
and Liu, Xia Feng
and Lu, Meng Xiao",
title="Internet-Based Cognitive Behavioral Therapy Interventions for Caregivers of Patients With Cancer: Scoping Review",
journal="JMIR Cancer",
year="2025",
month="Jun",
day="4",
volume="11",
pages="e67131",
keywords="cancer",
keywords="oncology",
keywords="caregivers",
keywords="informal caregivers",
keywords="internet",
keywords="scoping review",
keywords="cognitive behavioral therapy",
abstract="Background: Cancer imposes significant physical and emotional distress not only on patients, but also on their caregivers. In recent years, there has been a growing focus on the mental and physical well-being of caregivers. Among various psychological interventions, cognitive behavioral therapy (CBT) is widely recognized as one of the most effective approaches. However, traditional CBT is often limited by time and geographical constraints, resulting in delayed or inefficient support for caregivers. Internet-based cognitive behavioral therapy (ICBT) presents a valuable alternative for alleviating the caregiving burden and the negative emotions experienced by caregivers. Objectives: This study aimed to provide a scoping review of ICBT interventions for caregivers of patients with cancer, examining intervention content, outcome measures, and effectiveness and to offer insights and references for the development and clinical applications of ICBT programs tailored to caregivers of patients with cancer in China. Methods: Relevant literature was systematically searched in PubMed, Web of Science, Cochrane Library, CINAHL, Embase, China National Knowledge Infrastructure (CNKI), Wanfang Data, and VIP Chinese Journal Database. The search timeframe was from database inception to June 6, 2024. Inclusion criteria encompassed intervention studies that implemented cognitive behavioral therapy for caregivers of patients with cancer via the internet, WeChat (Tencent), or mobile electronic devices. This category includes both randomized and nonrandomized controlled trials. Results: A total of 12 studies met the criteria and were included in the review. The intervention content included the following components: treatment initiation and brief introduction (5/12, 41\%), cognitive education and restructuring (7/12, 58\%), emotional expression and coping (6/12, 50\%), cognitive restructuring and reinforcement (4/12, 33\%), behavioral training and activation (9/12, 75\%), problem-solving techniques (4/12, 33\%), communication (5/12, 41\%), and completion of treatment with follow-up consolidation (3/12, 25\%). The intervention duration typically ranged from 6 to 8 weeks. Outcome indicators encompassed feasibility and acceptability, anxiety, depression, caregiver burden, and quality of life. ICBT demonstrated positive effects for caregivers of patients with cancer. Most intervention programs were feasible and acceptable, with 2 out of 5 feasibility studies reporting recruitment rates below 50\%. Attrition rates across studies ranged from 3\% to 16\%, and caregivers expressed satisfaction with the information, quality, and skills provided. ICBT exhibits a moderate effect in diminishing negative emotions among caregivers and alleviating caregiver stress. However, its impact on improving quality of life is not statistically significant, underscoring the need for long-term follow-up. Conclusions: The implementation of ICBT for caregivers of patients with cancer has demonstrated beneficial outcomes, attributed to its practicality and flexibility, which contribute to its greater acceptance among caregivers. Nevertheless, there is significant heterogeneity in intervention format, duration, and outcome indicators. It is necessary to develop optimal intervention strategies and secure online platforms based on the cultural background in China to improve the quality of life of caregivers. ",
doi="10.2196/67131",
url="https://cancer.jmir.org/2025/1/e67131"
}


@Article{info:doi/10.2196/71596,
author="Deng, Ming Xin
and Hounsri, Kanokwan
and Lopez, Violeta
and Tam, Wai-San Wilson",
title="Family Experiences, Needs, and Perceptions in Home-Based Hospice Care for Patients With Terminal Cancer: Meta-Synthesis and Systematic Review",
journal="JMIR Cancer",
year="2025",
month="Jun",
day="19",
volume="11",
pages="e71596",
keywords="palliative",
keywords="hospice",
keywords="home care",
keywords="cancer",
keywords="meta-synthesis.",
abstract="Background: Home-based hospice care offers patients with terminal cancer the comfort of receiving care in a familiar environment while enabling family members to provide personalised support. Despite the critical role families play, the literature remains underexplored in terms of their experiences, needs, and perceptions. A robust qualitative synthesis is needed to inform improvements in palliative care services. Objective: This meta-synthesis aims to systematically review and synthesize qualitative evidence regarding the experiences, needs, and perceptions of family caregivers in home-based hospice care for patients with terminal cancer. The goal is identifying key themes that can improve caregiver support and service delivery. Methods: A systematic search was conducted across MEDLINE, Embase, Scopus, PsycINFO, CINAHL, Google Scholar, and relevant gray literature sources up to March 14, 2025. Studies were included if they focused on family caregivers' experiences in home-based hospice care settings, excluding those that addressed only patients or health care providers. Two independent reviewers performed study selection, data extraction, and quality assessment using the Critical Appraisal Skills Programme checklist. Data were synthesized using a 3-step thematic synthesis approach, and the confidence in the findings was assessed via the GRADE-CERQual (Grading of Recommendations Assessment, Development, and Evaluation--Confidence in the Evidence from Reviews of Qualitative Research) framework. Results: Five studies published between 1989 and 2022 from diverse geographical regions (including Asia and Western settings) met the inclusion criteria. Two major themes emerged: (1) being physically and emotionally present, where caregivers expressed a strong commitment to remain with their loved ones, providing emotional support and maintaining a sense of control; and (2) sharing responsibilities, which underscored the importance of both formal support from palliative care teams and informal support from family and friends in mitigating caregiver burden. These findings directly address the study's aims by illustrating how caregivers balance emotional commitment with the practical challenges of providing home-based care. Conclusions: Although family caregivers are dedicated to delivering high-quality, personalized care, they encounter significant emotional and logistical challenges. Variability in study settings, potential recall bias from retrospective interviews, and limited gray literature access may affect the generalizability of the findings. This meta-synthesis underscores the essential role of family involvement in home-based hospice care for patients with terminal cancer. The combined reliance on emotional commitment and shared responsibilities---with support from professional care teams---is vital for optimal care delivery. Future interventions should enhance formal and informal support systems to meet family caregivers' diverse needs better. Trial Registration: PROSPERO CRD42023486012; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023486012 ",
doi="10.2196/71596",
url="https://cancer.jmir.org/2025/1/e71596"
}


@Article{info:doi/10.2196/70275,
author="Virdee, S. Pradeep
and Collins, K. Kiana
and Smith, Friedemann Claire
and Yang, Xin
and Zhu, Sufen
and Roberts, Nia
and Oke, L. Jason
and Bankhead, Clare
and Perera, Rafael
and Hobbs, Richard F. D.
and Nicholson, D. Brian",
title="Clinical Prediction Models Incorporating Blood Test Trend for Cancer Detection: Systematic Review, Meta-Analysis, and Critical Appraisal",
journal="JMIR Cancer",
year="2025",
month="Jun",
day="27",
volume="11",
pages="e70275",
keywords="blood test",
keywords="hematologic tests",
keywords="trend",
keywords="prediction model",
keywords="primary health care",
keywords="cancer",
keywords="neoplasms",
keywords="systematic review",
abstract="Background: Blood tests used to identify patients at increased risk of undiagnosed cancer are commonly used in isolation, primarily by monitoring whether results fall outside the normal range. Some prediction models incorporate changes over repeated blood tests (or trends) to improve individualized cancer risk identification, as relevant trends may be confined within the normal range. Objective: Our aim was to critically appraise existing diagnostic prediction models incorporating blood test trends for the risk of cancer. Methods: MEDLINE and EMBASE were searched until April 3, 2025 for diagnostic prediction model studies using blood test trends for cancer risk. Screening was performed by 4 reviewers. Data extraction for each article was performed by 2 reviewers independently. To critically appraise models, we narratively synthesized studies, including model building and validation strategies, model reporting, and the added value of blood test trends. We also reviewed the performance measures of each model, including discrimination and calibration. We performed a random-effects meta-analysis of the c-statistic for a trends-based prediction model if there were at least 3 studies validating the model. The risk of bias was assessed using the PROBAST (prediction model risk of bias assessment tool). Results: We included 16 articles, with a total of 7 models developed and 14 external validation studies. In the 7 models derived, full blood count (FBC) trends were most commonly used (86\%, n=7 models). Cancers modeled were colorectal (43\%, n=3), gastro-intestinal (29\%, n=2), nonsmall cell lung (14\%, n=1), and pancreatic (14\%, n=1). In total, 2 models used statistical logistic regression, 2 used joint modeling, and 1 each used XGBoost, decision trees, and random forests. The number of blood test trends included in the models ranged from 1 to 26. A total of 2 of 4 models were reported with the full set of coefficients needed to predict risk, with the remaining excluding at least one coefficient from their article or were not publicly accessible. The c-statistic ranged 0.69?0.87 among validation studies. The ColonFlag model using trends in the FBC was commonly externally validated, with a pooled c-statistic=0.81 (95\% CI 0.77-0.85; n=4 studies) for 6-month colorectal cancer risk. Models were often inadequately tested, with only one external validation study assessing model calibration. All 16 studies scored a low risk of bias regarding predictor and outcome details. All but one study scored a high risk of bias in the analysis domain, with most studies often removing patients with missing data from analysis or not adjusting the derived model for overfitting. Conclusions: Our review highlights that blood test trends may inform further investigation for cancer. However, models were not available for most cancer sites, were rarely externally validated, and rarely assessed calibration when they were externally validated. Trial Registration: PROSPERO CRD42022348907; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022348907 ",
doi="10.2196/70275",
url="https://cancer.jmir.org/2025/1/e70275"
}


@Article{info:doi/10.2196/54154,
author="Komariah, Maria
and Maulana, Sidik
and Amirah, Shakira
and Platini, Hesti
and Rahayuwati, Laili
and Yusuf, Ah
and Firdaus, Hasymi Mohd Khairul Zul",
title="Benefits of Remote-Based Mindfulness on Physical Symptom Outcomes in Cancer Survivors: Systematic Review and Meta-Analysis",
journal="JMIR Cancer",
year="2025",
month="Jan",
day="16",
volume="11",
pages="e54154",
keywords="cancer",
keywords="physical symptoms",
keywords="mindfulness",
keywords="remote-based intervention",
keywords="quality of life",
abstract="Background: Many cancer survivors experience a wide range of symptoms closely linked to psychological problems, highlighting the need for psychological treatment, one of the most popular being mindfulness. The use of the internet has greatly increased in the last decade, and has encouraged the use of remote-based interventions to help people living with cancer access treatment remotely via devices. Objective: The primary aim of this study was to explore the efficacy of internet-based mindfulness interventions on the physical symptoms of people living with cancer, where physical symptoms are defined as distressing somatic experiences (eg fatigue, insomnia, and pain) regardless of the underlying cause. The secondary aim was to investigate interventions for the quality of life (QoL). Methods: This study followed the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) guidelines. Relevant articles were systematically searched using electronic databases, namely Scopus, Medline through PubMed, Cumulated Index in Nursing and Allied Health Literature (CINAHL) through EBSCOhost, and Cochrane Central Database. Randomized controlled and pilot trials involving adults and/or older adults with cancer and using remote-based mindfulness interventions compared to usual care were included. The quality of the trials included in this study was assessed using the revised Cochrane risk of bias, version 2.0. This study estimated the standardized mean difference (SMD) and mean difference (MD) with 95\% CI. The I2 test was used to identify potential causes of heterogeneity. Publication bias was assessed using contour-enhanced funnel plots and the Egger linear regression test to reveal a small study effect. Results: The initial search yielded 1985 records, of which 13 studies were ultimately included. After treatment, remote-based mindfulness significantly reduced fatigue (SMD ?0.94; 95\% CI: ?1.56 to ?0.33; P=.002), sleep disturbance (SMD ?0.36; 95\% CI: ?0.60 to ?0.12; P=.004), and improved physical function (SMD .25; 95\% CI: 0.09 to 0.41; P=.002) compared to that observed before treatment. However, compared with usual care, remote-based mindfulness showed a statistically significant reduction only in sleep disturbance (SMD: ?0.37; 95\% CI: ?0.58 to ?0.16; P=.0006) after treatment. Moreover, remote-based mindfulness was not statistically significant in reducing pain both within and between groups. Conclusions: Remote-based mindfulness shows promise in reducing sleep disturbances; however, its impact on fatigue, pain, and physical function may be limited. ",
doi="10.2196/54154",
url="https://cancer.jmir.org/2025/1/e54154"
}


@Article{info:doi/10.2196/50662,
author="Ng, Shin Krystal Lu
and Munisamy, Murallitharan
and Lim, Yin Joanne Bee
and Alshagga, Mustafa",
title="The Effect of Nutritional Mobile Apps on Populations With Cancer: Systematic Review",
journal="JMIR Cancer",
year="2025",
month="Feb",
day="5",
volume="11",
pages="e50662",
keywords="cancer",
keywords="mobile app",
keywords="nutrition",
keywords="body composition",
keywords="quality of life",
keywords="mobile health",
keywords="mHealth",
keywords="diet",
keywords="intervention",
keywords="mobile phone",
keywords="PRISMA",
abstract="Background: Limited access to nutrition support among populations with cancer is a major barrier to sustainable and quality cancer care. Increasing use of mobile health in health care has raised concerns about its validity and health impacts. Objective: This systematic review aimed to determine the effectiveness of commercial or cancer-specific nutritional mobile apps among people living with cancer. Methods: A systematic search of the CENTRAL, Embase, PubMed (MEDLINE), and Scopus databases was carried out in May 2024. All types of intervention studies were included, except observational studies, gray literature, and reference lists of key systematic reviews. Studies were eligible for inclusion if they involved (1) patients with or survivors of cancer and (2) nutrition-related mobile apps. Studies were excluded if the nutrition intervention was not delivered via mobile app or the app intervention was accompanied by dietary counseling. The review process was conducted based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The Risk of Bias 2 and Risk of Bias in Nonrandomized Studies tools were used to assess the study quality. The Cochrane Review Manager (version 5.4) software was used to synthesize the results of the bias assessment. Results: A total of 13 interventions were included, comprising 783 adults or teenagers with cancer. Most studies focused on breast cancer (6/13, 46\%), overweight (6/13, 46\%), and survivors (9/13, 69\%). Data on anthropometry and body composition (7/13, 54\%; 387 participants), nutritional status (3/13, 23\%; 249 participants), dietary intake (7/13, 54\%; 352 participants), and quality of life (6/13, 46\%; 384 participants) were gathered. Experimental groups were more likely to report significant improvements in body weight or composition, dietary compliance, nutritional status, and quality of life than control groups. Conclusions: Although mobile app platforms are used to deliver nutrition interventions, the evidence for long-term efficacy, particularly in populations with cancer, remains elusive. More robust randomized controlled trials with larger sample sizes, as well as more homogeneous population characteristics and outcome measures, are warranted. Trial Registration: PROSPERO CRD42023330575; https://tinyurl.com/55v56yaj ",
doi="10.2196/50662",
url="https://cancer.jmir.org/2025/1/e50662"
}


@Article{info:doi/10.2196/66087,
author="Richard, Vincent
and Gilbert, Allison
and Pizzolla, Emanuela
and Briganti, Giovanni",
title="Investigating the Complexity of Multidimensional Symptom Experiences in Patients With Cancer: Systematic Review of the Network Analysis Approach",
journal="JMIR Cancer",
year="2025",
month="Jul",
day="9",
volume="11",
pages="e66087",
keywords="network analysis",
keywords="symptoms",
keywords="cancer patients",
keywords="systematic review",
keywords="cancer treatment",
keywords="symptom management",
abstract="Background: Advances in therapies have significantly improved the outcomes of patients with cancer. However, multidimensional symptoms negatively impact patients' quality of life. Traditional symptom analysis methods fail to capture the dynamic and interactive nature of these symptoms, limiting progress in supportive care. Network analysis (NA) is a promising method to evaluate complex medical situations. Objective: We performed a systematic review to explore NA's contribution to understanding the complexity of symptom experiences in patients with cancer. Methods: The research question was as follows: ``In patients with cancer (population), what is the contribution of NA (intervention) to understanding the complexity of multidimensional symptom experiences (outcome)?'' The keywords ``network analysis'' AND ``symptoms'' AND ``cancer survivors'' OR ``cancer patients'' were searched in MEDLINE, Embase, Google Scholar, and Scopus between 2010 and 2024. Citations were extracted using Covidence software. Two reviewers independently screened the articles and resolved inclusion disagreements through consensus. Data were synthetized, and results have been narratively described. Bias analysis was performed using the Methodological Index for Non-Randomized Studies tool. Results: Among 764 articles initially identified, 22 were included. Studies evaluated mixed solid tumors (n=10), digestive tract cancers (n=4), breast cancer (n=3), head and neck cancer (n=2), gliomas (n=2), and mixed solid and hematological cancers (n=1). Twelve studies used general symptom assessment tools, whereas 10 focused on neuropsychological symptoms. Moreover, 1 study evaluated symptoms at diagnosis, 1 evaluated them during curative radiotherapy, 4 evaluated them during the perioperative period, 5 evaluated them during chemotherapy, 4 evaluated them during ongoing cancer therapies, and 7 evaluated them after acute treatments. Among these, 3 evaluated the longitudinal changes in symptom networks across chemotherapy cycles, and 1 evaluated changes during radiotherapy. Three studies investigated the associations between symptoms and biological parameters. Several NA approaches were used: network visualization (n=1), Bayesian network (n=1), pairwise Markov random field and IsingFit method (n=1), unregularized Gaussian graphical model (n=2), regularized partial correlation network (n=6), network visualization and community NA (n=1), network visualization and Walktrap algorithm (n=1), undirected network model with the Fruchterman-Reingold and edge-betweenness approaches (n=4), biased correlation and concise pattern diagram (n=1), extended Bayesian information criterion graphical LASSO method (n=3), cross-lagged panel network (n=1), and unspecified NA (n=3). Psychological symptoms, particularly anxiety, depression, and distress, were frequently identified as central and stably interconnected. Fatigue consistently emerged as a core symptom, closely linked to sleep disturbances, cognitive impairment, and emotional distress. Associations between symptoms and inflammatory biomarkers (eg, interleukin-6, C-reactive protein, and tumor necrosis factor-$\alpha$) suggest a biological basis for symptom interconnectivity. Conclusions: NA consistently identified core symptoms, particularly psychological symptoms and fatigue, and associations with inflammatory biomarkers. NA may deepen the understanding of symptom interconnectivity and guide more effective interventions. However, further longitudinal homogeneous studies using standardized methodologies are needed. ",
doi="10.2196/66087",
url="https://cancer.jmir.org/2025/1/e66087"
}


@Article{info:doi/10.2196/72862,
author="Ouellet, Steven
and Naye, Florian
and Supper, Wilfried
and Cachinho, Chlo{\'e}
and Gagnon, Marie-Pierre
and LeBlanc, Annie
and Laferri{\`e}re, Marie-Claude
and D{\'e}cary, Simon
and Sasseville, Maxime",
title="Digital Health Portals for Individuals Living With or Beyond Cancer: Patient-Driven Scoping Review",
journal="JMIR Cancer",
year="2025",
month="Jul",
day="18",
volume="11",
pages="e72862",
keywords="cancer",
keywords="oncology",
keywords="patient portal",
keywords="electronic health records",
keywords="online access",
keywords="patient records",
keywords="social determinants of health",
keywords="scoping review",
keywords="Preferred Reporting Items for Systematic Reviews and Meta-Analyses",
abstract="Background: Digital health portals are online platforms allowing individuals to access their personal information and communicate with health care providers. While digital health portals have been associated with improved health outcomes and more streamlined health care processes, their impact on individuals living with or beyond cancer remains underexplored. Objective: This scoping review aimed to (1) identify the portal functionalities reported in studies involving individuals living with or beyond cancer, as well as the outcomes assessed, and (2) explore the diversity of participant characteristics and potential factors associated with portal use. Methods: We conducted a scoping review in accordance with the JBI methodology (formerly the Joanna Briggs Institute) and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. We included primary research studies published between 2014 and 2024 that involved participants living with or beyond cancer, had access to personal health information, and assessed at least one outcome related to health or the health care system. We searched the Embase, Web of Science, MEDLINE (Ovid), and CINAHL Plus with Full Text databases. Five reviewers independently screened all titles, abstracts, and full texts in duplicate using Covidence. We extracted data on study design, participant characteristics, portal functionalities, outcomes assessed, and PROGRESS-Plus (place of residence; race, ethnicity, culture, or language; occupation; gender or sex; religion; education; socioeconomic status; and social capital--Plus) equity factors. Results: We included 44 studies; most were conducted in the United States (n=30, 68\%) and used quantitative (n=23, 52\%), mixed methods (n=11, 25\%), or qualitative (n=10, 23\%) designs. The most common portal features were access to test results (28/44, 64\%) and secure messaging (30/44, 68\%). Frequently reported services included appointment-related functions (19/44, 43\%), educational resources (13/44, 30\%), and prescription management features (11/44, 25\%). Behavioral and technology-related outcomes were the most frequently assessed (37/44, 84\%), followed by system-level (19/44, 43\%), psychosocial (16/44, 36\%), and clinical outcomes (5/44, 11\%). Overall, 43\% (19/44) of the studies addressed PROGRESS-Plus factors. Age was the most frequently reported (13/19, 68\%), followed by socioeconomic status (10/19, 53\%), race or ethnicity (7/19, 37\%), and gender or sex (7/19, 37\%). Social capital (2/19, 11\%), occupation (1/19, 5\%), and disability (1/19, 5\%) were rarely considered, and religion was not reported in any study. Conclusions: While digital health portals enhance patient engagement, their clinical impact and equity implications remain insufficiently evaluated. We found disparities in functionalities, outcomes, and PROGRESS-Plus representation. To promote equitable benefits, future studies should adopt inclusive designs and evaluation strategies that address diverse outcomes and integrate social determinants of health. ",
doi="10.2196/72862",
url="https://cancer.jmir.org/2025/1/e72862"
}


@Article{info:doi/10.2196/65848,
author="Coen, Emma
and Del Fiol, Guilherme
and Kaphingst, A. Kimberly
and Borsato, Emerson
and Shannon, Jackilen
and Smith, Hadley
and Masino, Aaron
and Allen, G. Caitlin",
title="Chatbot for the Return of Positive Genetic Screening Results for Hereditary Cancer Syndromes: Prompt Engineering Project",
journal="JMIR Cancer",
year="2025",
month="Jun",
day="10",
volume="11",
pages="e65848",
keywords="prompt engineering",
keywords="few-shot learning",
keywords="retrieval-augmented generation",
keywords="population screening program",
keywords="cancer",
keywords="genetics",
keywords="screening",
keywords="syndrome",
keywords="genomic",
keywords="counseling",
keywords="large language model",
keywords="LLM",
keywords="engineering",
keywords="chatbot",
keywords="prompt",
keywords="RAG",
keywords="mobile phone",
doi="10.2196/65848",
url="https://cancer.jmir.org/2025/1/e65848"
}


@Article{info:doi/10.2196/68516,
author="Liu, Darren
and Lin, Yufen
and Yan, Runze
and Wang, Zhiyuan
and Bold, Delgersuren
and Hu, Xiao",
title="Leveraging Artificial Intelligence for Digital Symptom Management in Oncology: The Development of CRCWeb",
journal="JMIR Cancer",
year="2025",
month="Jun",
day="16",
volume="11",
pages="e68516",
keywords="colorectal cancer",
keywords="health disparity",
keywords="health equity",
keywords="generative artificial intelligence",
keywords="large language model",
keywords="software engineering",
keywords="artificial intelligence",
abstract="International Registered Report Identifier (IRRID): RR2-10.2196/48499 ",
doi="10.2196/68516",
url="https://cancer.jmir.org/2025/1/e68516"
}


@Article{info:doi/10.2196/53887,
author="Lin, Ching-Hsiung
and Wang, Bing-Yen
and Lin, Sheng-Hao
and Shih, Hsuan Pei
and Lee, Chin-Jing
and Huang, Ting Yung
and Chen, Chieh Shih
and Pan, Mei-Lien",
title="Process Re-Engineering and Data Integration Using Fast Healthcare Interoperability Resources for the Multidisciplinary Treatment of Lung Cancer",
journal="JMIR Cancer",
year="2025",
month="May",
day="5",
volume="11",
pages="e53887",
keywords="multidisciplinary team meetings",
keywords="process re-engineering",
keywords="multidisciplinary cancer care",
keywords="Fast Healthcare Interoperability Resources",
keywords="tumor board",
keywords="multidisciplinary team",
keywords="cancer",
keywords="lung cancer",
keywords="treatment",
keywords="lung",
keywords="health care professionals",
keywords="health care",
keywords="MDT",
keywords="digitize",
keywords="API",
keywords="hospital",
keywords="information system",
keywords="HIS",
keywords="medical data",
keywords="platform",
keywords="data integration",
keywords="information and communication technology",
keywords="ICT",
keywords="decision support",
keywords="eHealth",
keywords="digital tools",
keywords="clinic",
keywords="patient care",
keywords="application programming interface",
keywords="hospital information system",
doi="10.2196/53887",
url="https://cancer.jmir.org/2025/1/e53887"
}


@Article{info:doi/10.2196/63486,
author="Lyhne, Dam Johanne
and Smith, `Ben' Allan
and Carstensen, Wisbech Tina Birgitte
and Beatty, Lisa
and Bamgboje-Ayodele, Adeola
and Klein, Britt
and Jensen, Henrik Lars
and Frostholm, Lisbeth",
title="Adapting a Self-Guided eHealth Intervention Into a Tailored Therapist-Guided eHealth Intervention for Survivors of Colorectal Cancer",
journal="JMIR Cancer",
year="2025",
month="Mar",
day="5",
volume="11",
pages="e63486",
keywords="fear of cancer recurrence",
keywords="therapist-guided",
keywords="self-guided",
keywords="online intervention",
keywords="colorectal cancer",
keywords="digital health",
keywords="psychosocial intervention",
keywords="survivorship",
keywords="eHealth",
keywords="adaptation",
keywords="survivors",
keywords="oncologists",
keywords="therapists",
keywords="acceptability",
keywords="mobile phone",
abstract="Trial Registration: ClinicalTrials.gov NCT04287218; https://clinicaltrials.gov/study/NCT04287218 International Registered Report Identifier (IRRID): RR2-10.1186/s12885-020-06731-6 ",
doi="10.2196/63486",
url="https://cancer.jmir.org/2025/1/e63486"
}


@Article{info:doi/10.2196/66633,
author="Chow, L. James C.
and Li, Kay",
title="Developing Effective Frameworks for Large Language Model--Based Medical Chatbots: Insights From Radiotherapy Education With ChatGPT",
journal="JMIR Cancer",
year="2025",
month="Feb",
day="18",
volume="11",
pages="e66633",
keywords="artificial intelligence",
keywords="AI",
keywords="AI in medical education",
keywords="radiotherapy chatbot",
keywords="large language models",
keywords="LLMs",
keywords="medical chatbots",
keywords="health care AI",
keywords="ethical AI in health care",
keywords="personalized learning",
keywords="natural language processing",
keywords="NLP",
keywords="radiotherapy education",
keywords="AI-driven learning tools",
doi="10.2196/66633",
url="https://cancer.jmir.org/2025/1/e66633"
}


@Article{info:doi/10.2196/65566,
author="Bak, Marieke
and Hartman, Laura
and Graafland, Charlotte
and Korfage, J. Ida
and Buyx, Alena
and Schermer, Maartje
and ",
title="Ethical Design of Data-Driven Decision Support Tools for Improving Cancer Care: Embedded Ethics Review of the 4D PICTURE Project",
journal="JMIR Cancer",
year="2025",
month="Apr",
day="10",
volume="11",
pages="e65566",
keywords="shared decision-making",
keywords="oncology",
keywords="IT",
keywords="ethics",
keywords="decision support tools",
keywords="big data",
keywords="medical decision-making",
keywords="artificial intelligence",
doi="10.2196/65566",
url="https://cancer.jmir.org/2025/1/e65566"
}


@Article{info:doi/10.2196/66801,
author="Pozzar, A. Rachel
and Tulsky, A. James
and Berry, L. Donna
and Batista, Jeidy
and Barwick, Paige
and Lindvall, J. Charlotta
and Dykes, C. Patricia
and Manni, Michael
and Matulonis, A. Ursula
and McCleary, J. Nadine
and Wright, A. Alexi",
title="Usability, Acceptability, and Barriers to Implementation of a Collaborative Agenda-Setting Intervention (CASI) to Promote Person-Centered Ovarian Cancer Care: Development Study",
journal="JMIR Cancer",
year="2025",
month="Mar",
day="10",
volume="11",
pages="e66801",
keywords="ovarian neoplasm",
keywords="ovarian cancer",
keywords="cancer",
keywords="oncology",
keywords="oncologist",
keywords="metastases",
keywords="communication",
keywords="physician-patient relations",
keywords="electronic health record",
keywords="EHR",
keywords="electronic medical record",
keywords="EMR",
keywords="implementation science",
keywords="digital",
keywords="digital health",
keywords="digital technology",
keywords="digital intervention",
keywords="mobile phone",
abstract="Background: People with advanced ovarian cancer and their caregivers report unmet supportive care needs. We developed a Collaborative Agenda-Setting Intervention (CASI) to elicit patients' and caregivers' needs through the patient portal before a clinic visit and to communicate these needs to clinicians using the electronic health record. Objective: We aimed to assess the usability and acceptability of the CASI and identify barriers to and facilitators of its implementation. Methods: We recruited English- and Spanish-speaking patients, caregivers, and clinicians from the gynecologic oncology program at a comprehensive cancer center. Participants used the CASI prototype and then completed individual cognitive interviews and surveys. We assessed usability with the System Usability Scale (scores range 0?100, scores ?70 indicate acceptable usability) and acceptability with the Acceptability of Intervention Measure and Intervention Appropriateness Measure (scores for both measures range from 1 to 5, higher scores indicate greater acceptability). Interviews were audio recorded, transcribed, and analyzed using directed content analysis. Domains and constructs from the Consolidated Framework for Implementation Research comprised the initial codebook. We analyzed survey data using descriptive statistics and compared usability and acceptability scores across patients, caregivers, and clinicians using analyses of variance. Results: We enrolled 15 participants (5 patients, 5 caregivers, and 5 clinicians). The mean System Usability Scale score was 72 (SD 16). The mean Acceptability of Intervention Measure and Intervention Appropriateness Measure scores were 3.9 (SD 1.0) and 4.1 (SD 0.8), respectively. Participants viewed the CASI content and format positively overall. Several participants appreciated the CASI's integration into the clinical workflow and its potential to increase attention to psychosocial concerns. Suggestions to refine the CASI included removing redundant items, simplifying item language, and adding options to request a conversation or opt out of supportive care referrals. Key barriers to implementing the CASI include its complexity and limited resources available to address patients' and caregivers' needs. Conclusions: The CASI is usable and acceptable to patients with advanced ovarian cancer, caregivers, and clinicians. We identified several barriers to and facilitators of implementing the CASI. In future research, we will apply these insights to a pilot randomized controlled trial to assess the feasibility of comparing the CASI to usual care in a parallel group-randomized efficacy trial. ",
doi="10.2196/66801",
url="https://cancer.jmir.org/2025/1/e66801"
}


@Article{info:doi/10.2196/64145,
author="Bargas-Ochoa, Miguel
and Zulbaran-Rojas, Alejandro
and Finco, G. M.
and Costales, B. Anthony
and Flores-Camargo, Areli
and Bara, O. Rasha
and Pacheco, Manuel
and Phan, Tina
and Khichi, Aleena
and Najafi, Bijan",
title="Development and Implementation of a Personal Virtual Assistant for Patient Engagement and Communication in Postsurgical Cancer Care: Feasibility Cohort Study",
journal="JMIR Cancer",
year="2025",
month="Feb",
day="18",
volume="11",
pages="e64145",
keywords="digital health",
keywords="personal virtual assistant",
keywords="remote patient monitoring",
keywords="surgical oncology",
keywords="posthospital discharge",
keywords="postoperative support",
keywords="medication adherence postsurgery",
keywords="patient engagement",
keywords="mHealth",
keywords="mobile health",
abstract="Background: Cancer-care complexity heightens communication challenges between health care providers and patients, impacting their treatment adherence. This is especially evident upon hospital discharge in patients undergoing surgical procedures. Digital health tools offer potential solutions to address communication challenges seen in current discharge protocols. We aim to explore the usability and acceptability of an interactive health platform among discharged patients who underwent oncology-related procedures. Methods: A 4-week exploratory cohort study was conducted. Following hospital discharge, a tablet equipped with an integrated Personal Virtual Assistant (PVA) system was provided to patients who underwent oncology-related procedures. The PVA encompasses automated features that provide personalized care plans, developed through collaboration among clinicians, researchers, and engineers from various disciplines. These plans include guidance on daily specific assignments that were divided into 4 categories: medication intake, exercise, symptom surveys, and postprocedural specific tasks. The aim was to explore the acceptability of the PVA by quantification of dropout rate and assessing adherence to each care plan category throughout the study duration. The secondary aim assessed acceptability of the PVA through a technology acceptance model (TAM) questionnaire that examined ease of use, usefulness, attitude toward use, and privacy concerns. Results: In total, 17 patients were enrolled. However, 1 (5.8\%) patient dropped out from the study after 3 days due to health deterioration, leaving 16/17 (94.2\%) completing the study (mean age 54.5, SD 12.7, years; n=9, 52\% Caucasian; n=14, 82\% with a gynecological disease; n=3, 18\% with a hepatobiliary disease). At the study end point, adherence to care plan categories were 78\% (SD 25\%) for medications, 81\% (SD 24\%) for exercises, 61\% (SD 30\%) for surveys, and 58\% (SD 44\%) for specific tasks such as following step-by step wound care instructions, managing drains, administering injectable medications independently, and performing pelvic baths as instructed. There was an 80\% patient endorsement (strongly agree or agree) across all TAM categories. Conclusion: This study suggests the potential acceptability of the PVA among patients discharged after oncology-related procedures, with a dropout rate of less than 6\% and fair-to-good adherence to tasks such as medication intake and exercise. However, these findings are preliminary due to the small sample size and highlight the need for further research with larger cohorts to validate and refine the system. ",
doi="10.2196/64145",
url="https://cancer.jmir.org/2025/1/e64145"
}


@Article{info:doi/10.2196/53690,
author="Kalla, Mahima
and Bradford, Ashleigh
and Schadewaldt, Verena
and Burns, Kara
and Bray, E. Sarah C.
and Cain, Sarah
and McAlpine, Heidi
and Dhillon, S. Rana
and Chapman, Wendy
and Whittle, R. James
and J Drummond, Katharine
and Krishnasamy, Meinir",
title="Co-Designing a User-Centered Digital Health Tool for Supportive Care Needs of Patients With Brain Tumors and Their Caregivers: Interview Analysis",
journal="JMIR Cancer",
year="2025",
month="May",
day="23",
volume="11",
pages="e53690",
keywords="brain cancer",
keywords="unmet needs",
keywords="supportive care",
keywords="psychosocial support",
keywords="digital health",
keywords="qualitative research",
keywords="brain tumor",
keywords="user-centered",
keywords="patients",
keywords="caregivers",
keywords="interview analysis",
keywords="quality of life",
keywords="effectiveness",
keywords="co-design paradigm",
keywords="ideas",
keywords="concepts",
keywords="emotional support",
keywords="information sharing",
keywords="social connectedness",
keywords="health care professionals",
abstract="Background: Brain tumors are characterized by the high burden of disease that profoundly impacts the quality of life in patients and their families. Digital health tools hold tremendous potential to enhance supportive care and quality of life for patients with brain tumors and their caregivers. Objective: This study aims to generate ideas and concepts, through a co-design paradigm, to inform the development of a digital health tool to address the unmet needs of people affected by brain tumors. Methods: Patients with brain tumors, caregivers, and health professionals from 2 large public tertiary hospitals in Victoria, Australia, were invited to complete a qualitative interview discussing their unmet needs of care. Overall, 35 qualitative interviews focusing on unmet needs and concepts for a digital health tool were conducted with 13 patients, 11 caregivers, and 11 health professionals. Interviews were audio recorded and transcribed, and a 5-step framework analysis approach was used to analyze data. Results: Four themes of unmet supportive care needs emerged: (1) emotional and psychological, (2) information, (3) physical and practical, and (4) social connectedness. Participants expressed the desire for early and proactive mental health intervention, noted the importance of providing mental health support to caregivers, and emphasized the need for positive stories and affirmative language. From an information perspective, participants noted a sense of information overload, especially at the beginning. They also underscored the variety of information needed on an ongoing basis, including life after treatment, and comprehensive care assistance to maintain quality of life. Participants also described unmet supportive care needs relating to symptom burden, and practical and administrative support to facilitate the logistics of accessing treatment and accomplishing daily life tasks. Finally, they expressed the desire for greater social connectedness and safe spaces to engage with other people in a similar situation. Our findings are consistent with previous research on this subject and were integrated into the development of a web-based platform. Conclusions: Participants' perspectives informed the development of content for a web-based digital health platform called ``Brain Tumours Online.'' The platform comprises three pillars---(1) ``LEARN'': a repository of vetted information about a range of biomedical and psychosocial care topics; (2) ``CONNECT'': a digital peer support community with a health care professional interface; and (3) ``TOOLBOX'': an emerging library of validated digital therapeutics for symptom management. ",
doi="10.2196/53690",
url="https://cancer.jmir.org/2025/1/e53690"
}


@Article{info:doi/10.2196/64083,
author="Huebner, Hanna
and Wurmthaler, A. Lena
and Goossens, Chlo{\"e}
and Ernst, Mathias
and Mocker, Alexander
and Kr{\"u}ckel, Annika
and Kallert, Maximilian
and Geck, J{\"u}rgen
and Limpert, Milena
and Seitz, Katharina
and Ruebner, Matthias
and Kreis, Philipp
and Heindl, Felix
and H{\"o}rner, Manuel
and Volz, Bernhard
and Roth, Eduard
and Hack, C. Carolin
and Beckmann, W. Matthias
and Uhrig, Sabrina
and Fasching, A. Peter",
title="A Digital Home-Based Health Care Center for Remote Monitoring of Side Effects During Breast Cancer Therapy: Prospective, Single-Arm, Monocentric Feasibility Study",
journal="JMIR Cancer",
year="2025",
month="May",
day="2",
volume="11",
pages="e64083",
keywords="breast cancer",
keywords="digital medicine",
keywords="telehealth",
keywords="remote monitoring",
keywords="cyclin-dependent kinase 4/6 inhibitor",
keywords="CDK4/6 inhibitor",
keywords="mobile phone",
abstract="Background: The introduction of oral anticancer therapies has, at least partially, shifted treatment from clinician-supervised hospital care to patient-managed home regimens. However, patients with breast cancer receiving oral cyclin-dependent kinase 4/6 inhibitor therapy still require regular hospital visits to monitor side effects. Telemonitoring has the potential to reduce hospital visits while maintaining quality care. Objective: This study aims to develop a digital home-based health care center (DHHC) for acquiring electrocardiograms (ECGs), white blood cell (WBC) counts, side effect photo documentation, and patient-reported quality of life (QoL) data. Methods: The DHHC was set up using an Apple Watch Series 6 (ECG measurements), a HemoCue WBC DIFF Analyzer (WBC counts), an iPhone SE (QoL assessments and photo documentation), a TP-Link M7350-4G Wi-Fi router, and a Raspberry Pi 4 Model B. A custom-built app stored and synchronized remotely collected data with the clinic. The feasibility and acceptance of the DHHC among patients with breast cancer undergoing cyclin-dependent kinase 4/6 inhibitor therapy were evaluated in a prospective, single-arm, monocentric study. Patients (n=76) monitored side effects---ECGs, WBC counts, photo documentation, and QoL---at 3 predefined time points: study inclusion (on-site), day 14 (remote), and day 28 (remote). After the study completion, patients completed a comprehensive questionnaire on user perception and feasibility. Adherence to scheduled visits, the success rate of the data transfer, user perception and feasibility, and the clinical relevance of remote measurements were evaluated. Results: Mean adherence to the planned remote visits was 63\% on day 14 and 37\% on day 28. ECG measurements were performed most frequently (day 14: 57/76, 75\%; day 28: 31/76, 41\%). The primary patient-reported reason for nonadherence was device malfunction. The expected versus the received data transfer per patient was as follows: ECGs: 3 versus 3.04 (SD 1.9); WBC counts: 3 versus 2.14 (SD 1.14); QoL questionnaires: 3 versus 2.5 (SD 1.14); and photo documentation: 6 versus 4.4 (SD 3.36). Among patients, 81\% (55/68) found ECG measurements easy, 82\% (55/67) found photo documentation easy, and 48\% (33/69) found WBC measurements easy. Additionally, 61\% (40/66) of patients felt comfortable with self-monitoring and 79\% (54/68) were willing to integrate remote monitoring into their future cancer care. Therapy-induced decreased neutrophil count was successfully detected (P<.001; mean baseline: 4.3, SD 2.2, {\texttimes}109/L; on-treatment: 1.8, SD 0.8, {\texttimes}109/L). All-grade neutropenia and corrected QT interval prolongations were detected in 80\% (55/68) and 2\% (1/42) of patients, respectively. Conclusions: Adherence to scheduled remote visits was moderate, with nonadherence primarily attributed to device-related complications, which may have also affected the success rate of data transfer. Overall, patients considered remote monitoring useful and feasible. The prevalence of reported adverse events was comparable to existing literature, suggesting clinical potential. This initial feasibility study highlights the potential of the DHHC. ",
doi="10.2196/64083",
url="https://cancer.jmir.org/2025/1/e64083"
}


@Article{info:doi/10.2196/67914,
author="Liu, Darren
and Hu, Xiao
and Xiao, Canhua
and Bai, Jinbing
and Barandouzi, A. Zahra
and Lee, Stephanie
and Webster, Caitlin
and Brock, La-Urshalar
and Lee, Lindsay
and Bold, Delgersuren
and Lin, Yufen",
title="Evaluation of Large Language Models in Tailoring Educational Content for Cancer Survivors and Their Caregivers: Quality Analysis",
journal="JMIR Cancer",
year="2025",
month="Apr",
day="7",
volume="11",
pages="e67914",
keywords="large language models",
keywords="GPT-4",
keywords="cancer survivors",
keywords="caregivers",
keywords="education",
keywords="health equity",
abstract="Background: Cancer survivors and their caregivers, particularly those from disadvantaged backgrounds with limited health literacy or racial and ethnic minorities facing language barriers, are at a disproportionately higher risk of experiencing symptom burdens from cancer and its treatments. Large language models (LLMs) offer a promising avenue for generating concise, linguistically appropriate, and accessible educational materials tailored to these populations. However, there is limited research evaluating how effectively LLMs perform in creating targeted content for individuals with diverse literacy and language needs. Objective: This study aimed to evaluate the overall performance of LLMs in generating tailored educational content for cancer survivors and their caregivers with limited health literacy or language barriers, compare the performances of 3 Generative Pretrained Transformer (GPT) models (ie, GPT-3.5 Turbo, GPT-4, and GPT-4 Turbo; OpenAI), and examine how different prompting approaches influence the quality of the generated content. Methods: We selected 30 topics from national guidelines on cancer care and education. GPT-3.5 Turbo, GPT-4, and GPT-4 Turbo were used to generate tailored content of up to 250 words at a 6th-grade reading level, with translations into Spanish and Chinese for each topic. Two distinct prompting approaches (textual and bulleted) were applied and evaluated. Nine oncology experts evaluated 360 generated responses based on predetermined criteria: word limit, reading level, and quality assessment (ie, clarity, accuracy, relevance, completeness, and comprehensibility). ANOVA (analysis of variance) or chi-square analyses were used to compare differences among the various GPT models and prompts. Results: Overall, LLMs showed excellent performance in tailoring educational content, with 74.2\% (267/360) adhering to the specified word limit and achieving an average quality assessment score of 8.933 out of 10. However, LLMs showed moderate performance in reading level, with 41.1\% (148/360) of content failing to meet the sixth-grade reading level. LLMs demonstrated strong translation capabilities, achieving an accuracy of 96.7\% (87/90) for Spanish and 81.1\% (73/90) for Chinese translations. Common errors included imprecise scopes, inaccuracies in definitions, and content that lacked actionable recommendations. The more advanced GPT-4 family models showed better overall performance compared to GPT-3.5 Turbo. Prompting GPTs to produce bulleted-format content was likely to result in better educational content compared with textual-format content. Conclusions: All 3 LLMs demonstrated high potential for delivering multilingual, concise, and low health literacy educational content for cancer survivors and caregivers who face limited literacy or language barriers. GPT-4 family models were notably more robust. While further refinement is required to ensure simpler reading levels and fully comprehensive information, these findings highlight LLMs as an emerging tool for bridging gaps in cancer education and advancing health equity. Future research should integrate expert feedback, additional prompt engineering strategies, and specialized training data to optimize content accuracy and accessibility. International Registered Report Identifier (IRRID): RR2-10.2196/48499 ",
doi="10.2196/67914",
url="https://cancer.jmir.org/2025/1/e67914"
}


@Article{info:doi/10.2196/52627,
author="Fei-Zhang, J. David
and Lawrence, Sherron Amelia
and Chelius, C. Daniel
and Sheyn, M. Anthony
and Rastatter, C. Jeffrey",
title="The Impact of Digital Inequities on Nasal and Paranasal-Sinus Cancer Disparities in the United States: A Cohort Study",
journal="JMIR Cancer",
year="2025",
month="Jul",
day="15",
volume="11",
pages="e52627",
keywords="paranasal sinus diseases",
keywords="nasopharyngeal carcinoma",
keywords="statistics",
keywords="digital inequities",
keywords="cancer disparities",
keywords="technology",
keywords="morality",
keywords="treatment",
keywords="care access",
keywords="United States",
keywords="cohort study",
keywords="sinus cancer",
keywords="sociodemographic",
keywords="online access",
keywords="equity",
keywords="digital divide",
keywords="public health",
abstract="Background: In the modern era, the use of technology can substantially impact care access. Despite the extent of its influence on several chronic medical conditions related to the heart, lungs, and others, the relationship between one's access to digital resources and oncologic conditions has been seldom investigated in select pathologies among gastrointestinal and head-neck regions. However, studies on the influence of this ``digital inequity'' on other cancers pertaining to nasal and paranasal sinus cancer (NPSC) have yet to be performed. This remains in stark contrast to the extent of large data approaches assessing the impact of traditional social determinants/drivers of health (SDoH), such as factors related to one's socioeconomic status, minoritized race or ethnicity, and housing-transportation status, on prognostic and treatment outcomes. Objective: This study aims to use the Digital Inequity Index (DII), a novel, comprehensive tool that quantifies digital resource access on an area- or community-based level, to assess the relationship between inequities in digital accessibility with NPSC disparities in prognosis and care in the United States. Methods: Patients with NPSC from 2008 to 2017 in the Surveillance, Epidemiology, and End Results Program were assessed for significant regression trends in the long-term follow-up period and treatment receipt across NPSCs with increasing overall digital inequity, as measured by DII. DII was based on 17 census-tract level variables derived from the summarized values overlapping that same time period from the US Census/American Community Survey and Federal Communications Commission Annual Broadband Report. Variables were categorized as infrastructure-access (ie, electronic device ownership, internet provider availability, and income-broadband subscription ratio) or sociodemographic (education, income, age, and disability), ranked, and then averaged into a composite score to encompass direct and indirect factors related to digital inequity. Results: Across 8012 adult patients with NPSC, males (n=5416, 67.6\%) and White race (n=4293, 53.6\%) were the most represented demographics. With increasing digital inequity, as measured by increasing total DII scores, significant decreases in the length of long-term follow-up were observed with nasopharyngeal (P<.01) and maxillary sinus cancers (P=.02), with decreases as high as 19\% (35.2 to 28.5 months, nasopharynx). Electronic device and service availability inequities showcased higher-magnitude contributions to observed associated regression trends, while the income-broadband ratio contributed less. Significantly decreased odds of receiving indicated surgery (lowest odds ratio 0.87, 95\% CI 0.80-0.95, maxillary) and radiation (lowest odds ratio 0.78, 95\% CI 0.63-0.95, ethmoid) for several NPSCs were also observed. Conclusions: Digital inequities are associated with detrimental NPSC care and surveillance trends in the United States, even when accounting for traditional SDoH factors. These results prompt the need to include digital factors into the discussion of contextualizing SDoH-based analyses of cancer care disparities, as well as the specific factors from which prospective implementations and initiatives can invest limited public health resources to alleviate the most pertinent drivers of disparities. ",
doi="10.2196/52627",
url="https://cancer.jmir.org/2025/1/e52627"
}


@Article{info:doi/10.2196/57715,
author="Fong, Allan
and Boxley, Christian
and Schubel, Laura
and Gallagher, Christopher
and AuBuchon, Katarina
and Arem, Hannah",
title="Identifying Complex Scheduling Patterns Among Patients With Cancer With Transportation and Housing Needs: Feasibility Pilot Study",
journal="JMIR Cancer",
year="2025",
month="Jan",
day="17",
volume="11",
pages="e57715",
keywords="patient scheduling",
keywords="scheduling complexities",
keywords="temporal data mining",
keywords="dataset",
keywords="breast cancer",
keywords="social determinant of health",
keywords="oncology",
keywords="metastasis",
keywords="cancer patient",
keywords="social support",
keywords="community health worker",
keywords="housing need",
keywords="care",
keywords="transportation",
keywords="algorithm",
abstract="Background: Patients with cancer frequently encounter complex treatment pathways, often characterized by challenges with coordinating and scheduling appointments at various specialty services and locations. Identifying patients who might benefit from scheduling and social support from community health workers or patient navigators is largely determined on a case-by-case basis and is resource intensive. Objective: This study aims to propose a novel algorithm to use scheduling data to identify complex scheduling patterns among patients with transportation and housing needs. Methods: We present a novel algorithm to calculate scheduling complexity from patient scheduling data. We define patient scheduling complexity as an aggregation of sequence, resolution, and facility components. Schedule sequence complexity is the degree to which appointments are scheduled and arrived to in a nonchronological order. Resolution complexity is the degree of no shows or canceled appointments. Location complexity reflects the proportion of appointment dates at 2 or more different locations. Schedule complexity captures deviations from chronological order, unresolved appointments, and coordination across multiple locations. We apply the scheduling complexity algorithm to scheduling data from 38 patients with breast cancer enrolled in a 6-month comorbidity management intervention at an urban hospital in the Washington, DC area that serves low-income patients. We compare the scheduling complexity metric with count-based metrics: arrived ratio, rescheduled ratio, canceled ratio, and no-show ratio. We defined an aggregate count-based adjustment metric as the harmonic mean of rescheduled ratio, canceled ratio, and no-show ratio. A low count-based adjustment metric would indicate that a patient has fewer disruptions or changes in their appointment scheduling. Results: The patients had a median of 88 unique appointments (IQR 60.3), 62 arrived appointments (IQR 47.8), 13 rescheduled appointments (IQR 13.5), 9 canceled appointments (IQR 10), and 1.5 missed appointments (IQR 5). There was no statistically significant difference in count-based adjustments and scheduling complexity bins ($\chi$24=6.296, P=.18). In total, 5 patients exhibited high scheduling complexity with low count-based adjustments. A total of 2 patients exhibited high count-based adjustments with low scheduling complexity. Out of the 15 patients that indicated transportation or housing insecurity issues in conversations with community health workers, 86.7\% (13/15) patients were identified as medium or high scheduling complexity while 60\% (9/15) were identified as medium or high count-based adjustments. Conclusions: Scheduling complexity identifies patients with complex but nonchronological scheduling behaviors who would be missed by traditional count-based metrics. This study shows a potential link between transportation and housing needs with schedule complexity. Scheduling complexity can complement count-based metrics when identifying patients who might need additional care coordination support especially as it relates to transportation and housing needs. Trial Registration: ClinicalTrials.gov NCT04836221; https://clinicaltrials.gov/study/NCT04836221 ",
doi="10.2196/57715",
url="https://cancer.jmir.org/2025/1/e57715"
}


@Article{info:doi/10.2196/56625,
author="Brunelli, Cinzia
and Alfieri, Sara
and Zito, Emanuela
and Spelta, Marco
and Arba, Laura
and Lombi, Linda
and Caselli, Luana
and Caraceni, Augusto
and Borreani, Claudia
and Roli, Anna
and Miceli, Rosalba
and Tine', Gabriele
and Zecca, Ernesto
and Platania, Marco
and Procopio, Giuseppe
and Nicolai, Nicola
and Battaglia, Luigi
and Lozza, Laura
and Shkodra, Morena
and Massa, Giacomo
and Loiacono, Daniele
and Apolone, Giovanni",
title="Patient Voices: Multimethod Study on the Feasibility of Implementing Electronic Patient-Reported Outcome Measures in a Comprehensive Cancer Center",
journal="JMIR Cancer",
year="2025",
month="Jan",
day="22",
volume="11",
pages="e56625",
keywords="feasibility",
keywords="oncology",
keywords="patient-reported outcomes",
keywords="PROMs",
keywords="quality of life",
keywords="mixed methods study",
keywords="cancer",
keywords="electronic patient-reported outcomes",
keywords="patient compliance",
keywords="barrier",
keywords="implementation",
keywords="usability scale",
keywords="semistructured interview",
keywords="questionnaire",
keywords="clinical management",
keywords="eHealth",
abstract="Background: ``Patient Voices'' is a software developed to promote the systematic collection of electronic patient-reported outcome measures (ePROMs) in routine oncology clinical practice. Objective: This study aimed to assess compliance with and feasibility of the Patient Voices ePROM system and analyze patient-related barriers in an Italian comprehensive cancer center. Methods: Consecutive patients with cancer attending 3 outpatient clinics and 3 inpatient wards were screened for eligibility (adults, native speakers, and being able to fill in the ePROMs) and enrolled in a quantitative and qualitative multimethod study. Compliance, reasons for not administering the ePROMs, patients' interaction needs, and patient-perceived System Usability Scale (range 0-100) were collected; semistructured interviews were carried out in a subsample of patients. Results: From June 2020 to September 2021, a total of 435 patients were screened, 421 (96.7\%) were eligible, and 309 completed the ePROMs (309/421, 73.4\%; 95\% CI 69.8\%-77.5\%; mean age 63.3, SD 13.7 years). Organization problems and patient refusal were the main reasons for not administering the ePROMs (outpatients: 40/234, 17.1\% and inpatients: 44/201, 21.9\%). Help for tablet use was needed by 27.8\% (47/169) of outpatients and 10.7\% (15/140) of inpatients, while the support received for item interpretation was similar in the 2 groups (outpatients: 36/169, 21.3\% and inpatients: 26/140, 18.6\%). Average System Usability Scale scores indicated high usability in both groups (outpatients: mean 86.8, SD 15.8 and inpatients: mean 83.9, SD 18.8). Overall, repeated measurement compliance was 76.9\% (173/225; outpatients only). Interviewed patients showed positive attitudes toward ePROMs. However, there are barriers to implementation related to the time and cognitive effort required to complete the questionnaires. There is also skepticism about the usefulness of ePROMs in interactions with health care professionals. Conclusions: This study provides useful information for future ePROM implementation strategies, aimed at effectively supporting the routine clinical management and care of patients with cancer. In addition, these findings may be relevant to other organizations willing to systematically collect PROMs or ePROMs in their clinical routines. Trial Registration: ClinicalTrials.gov NCT03968718; https://clinicaltrials.gov/study/NCT03968718 ",
doi="10.2196/56625",
url="https://cancer.jmir.org/2025/1/e56625"
}


@Article{info:doi/10.2196/50124,
author="Jonnalagedda-Cattin, Magali
and Moukam Datchoua, Mano{\"e}la Alida
and Yakam, Flore Virginie
and Kenfack, Bruno
and Petignat, Patrick
and Thiran, Jean-Philippe
and Sch{\"o}nenberger, Klaus
and Schmidt, C. Nicole",
title="Barriers and Facilitators to the Preadoption of a Computer-Aided Diagnosis Tool for Cervical Cancer: Qualitative Study on Health Care Providers' Perspectives in Western Cameroon",
journal="JMIR Cancer",
year="2025",
month="Feb",
day="5",
volume="11",
pages="e50124",
keywords="qualitative research",
keywords="technology acceptance",
keywords="cervical cancer",
keywords="diagnosis",
keywords="computer-assisted",
keywords="decision support systems",
keywords="artificial intelligence",
keywords="health personnel attitudes",
keywords="Cameroon",
keywords="mobile phone",
abstract="Background: Computer-aided detection and diagnosis (CAD) systems can enhance the objectivity of visual inspection with acetic acid (VIA), which is widely used in low- and middle-income countries (LMICs) for cervical cancer detection. VIA's reliance on subjective health care provider (HCP) interpretation introduces variability in diagnostic accuracy. CAD tools can address some limitations; nonetheless, understanding the contextual factors affecting CAD integration is essential for effective adoption and sustained use, particularly in resource-constrained settings. Objective: This study investigated the barriers and facilitators perceived by HCPs in Western Cameroon regarding sustained CAD tool use for cervical cancer detection using VIA. The aim was to guide smooth technology adoption in similar settings by identifying specific barriers and facilitators and optimizing CAD's potential benefits while minimizing obstacles. Methods: The perspectives of HCPs on adopting CAD for VIA were explored using a qualitative methodology. The study participants included 8 HCPs (6 midwives and 2 gynecologists) working in the Dschang district, Cameroon. Focus group discussions were conducted with midwives, while individual interviews were conducted with gynecologists to comprehend unique perspectives. Each interview was audio-recorded, transcribed, and independently coded by 2 researchers using the ATLAS.ti (Lumivero, LLC) software. The technology acceptance lifecycle framework guided the content analysis, focusing on the preadoption phases to examine the perceived acceptability and initial acceptance of the CAD tool in clinical workflows. The study findings were reported adhering to the COREQ (Consolidated Criteria for Reporting Qualitative Research) and SRQR (Standards for Reporting Qualitative Research) checklists. Results: Key elements influencing the sustained use of CAD tools for VIA by HCPs were identified, primarily within the technology acceptance lifecycle's preadoption framework. Barriers included the system's ease of use, particularly challenges associated with image acquisition, concerns over confidentiality and data security, limited infrastructure and resources such as the internet and device quality, and potential workflow changes. Facilitators encompassed the perceived improved patient care, the potential for enhanced diagnostic accuracy, and the integration of CAD tools into routine clinical practices, provided that infrastructure and training were adequate. The HCPs emphasized the importance of clinical validation, usability testing, and iterative feedback mechanisms to build trust in the CAD tool's accuracy and utility. Conclusions: This study provides practical insights from HCPs in Western Cameroon regarding the adoption of CAD tools for VIA in clinical settings. CAD technology can aid diagnostic objectivity; however, data management, workflow adaptation, and infrastructure limitations must be addressed to avoid ``pilotitis''---the failure of digital health tools to progress beyond the pilot phase. Effective implementation requires comprehensive technology management, including regulatory compliance, infrastructure support, and user-focused training. Involving end users can ensure that CAD tools are fully integrated and embraced in LMICs to aid cervical cancer screening. ",
doi="10.2196/50124",
url="https://cancer.jmir.org/2025/1/e50124"
}


@Article{info:doi/10.2196/62833,
author="Huang, Xiayuan
and Ren, Shushun
and Mao, Xinyue
and Chen, Sirui
and Chen, Elle
and He, Yuqi
and Jiang, Yun",
title="Association Between Risk Factors and Major Cancers: Explainable Machine Learning Approach",
journal="JMIR Cancer",
year="2025",
month="May",
day="2",
volume="11",
pages="e62833",
keywords="electronic health record",
keywords="EHR",
keywords="cancer risk modeling",
keywords="risk factor analysis",
keywords="explainable machine learning",
keywords="machine learning",
keywords="ML",
keywords="risk factor",
keywords="major cancers",
keywords="monitoring",
keywords="cancer risk",
keywords="breast cancer",
keywords="colorectal cancer",
keywords="lung cancer",
keywords="prostate cancer",
keywords="cancer patients",
keywords="clinical decision-making",
abstract="Background: Cancer is a life-threatening disease and a leading cause of death worldwide, with an estimated 611,000 deaths and over 2 million new cases in the United States in 2024. The rising incidence of major cancers, including among younger individuals, highlights the need for early screening and monitoring of risk factors to manage and decrease cancer risk. Objective: This study aimed to leverage explainable machine learning models to identify and analyze the key risk factors associated with breast, colorectal, lung, and prostate cancers. By uncovering significant associations between risk factors and these major cancer types, we sought to enhance the understanding of cancer diagnosis risk profiles. Our goal was to facilitate more precise screening, early detection, and personalized prevention strategies, ultimately contributing to better patient outcomes and promoting health equity. Methods: Deidentified electronic health record data from Medical Information Mart for Intensive Care (MIMIC)--III was used to identify patients with 4 types of cancer who had longitudinal hospital visits prior to their diagnosis presence. Their records were matched and combined with those of patients without cancer diagnoses using propensity scores based on demographic factors. Three advanced models, penalized logistic regression, random forest, and multilayer perceptron (MLP), were conducted to identify the rank of risk factors for each cancer type, with feature importance analysis for random forest and MLP models. The rank biased overlap was adopted to compare the similarity of ranked risk factors across cancer types. Results: Our framework evaluated the prediction performance of explainable machine learning models, with the MLP model demonstrating the best performance. It achieved an area under the receiver operating characteristic curve of 0.78 for breast cancer (n=58), 0.76 for colorectal cancer (n=140), 0.84 for lung cancer (n=398), and 0.78 for prostate cancer (n=104), outperforming other baseline models (P<.001). In addition to demographic risk factors, the most prominent nontraditional risk factors overlapped across models and cancer types, including hyperlipidemia (odds ratio [OR] 1.14, 95\% CI 1.11?1.17; P<.01), diabetes (OR 1.34, 95\% CI 1.29?1.39; P<.01), depressive disorders (OR 1.11, 95\% CI 1.06?1.16; P<.01), heart diseases (OR 1.42, 95\% CI 1.32?1.52; P<.01), and anemia (OR 1.22, 95\% CI 1.14?1.30; P<.01). The similarity analysis indicated the unique risk factor pattern for lung cancer from other cancer types. Conclusions: The study's findings demonstrated the effectiveness of explainable ML models in assessing nontraditional risk factors for major cancers and highlighted the importance of considering unique risk profiles for different cancer types. Moreover, this research served as a hypothesis-generating foundation, providing preliminary results for future investigation into cancer diagnosis risk analysis and management. Furthermore, expanding collaboration with clinical experts for external validation would be essential to refine model outputs, integrate findings into practice, and enhance their impact on patient care and cancer prevention efforts. ",
doi="10.2196/62833",
url="https://cancer.jmir.org/2025/1/e62833"
}


@Article{info:doi/10.2196/69672,
author="Wu, Tong
and Wang, Yuting
and Cui, Xiaoli
and Xue, Peng
and Qiao, Youlin",
title="AI-Based Identification Method for Cervical Transformation Zone Within Digital Colposcopy: Development and Multicenter Validation Study",
journal="JMIR Cancer",
year="2025",
month="Mar",
day="31",
volume="11",
pages="e69672",
keywords="artificial intelligence",
keywords="AI",
keywords="cervical cancer screening",
keywords="transformation zone",
keywords="diagnosis and early treatment",
keywords="lightweight neural network",
abstract="Background: In low- and middle-income countries, cervical cancer remains a leading cause of death and morbidity for women. Early detection and treatment of precancerous lesions are critical in cervical cancer prevention, and colposcopy is a primary diagnostic tool for identifying cervical lesions and guiding biopsies. The transformation zone (TZ) is where a stratified squamous epithelium develops from the metaplasia of simple columnar epithelium and is the most common site of precancerous lesions. However, inexperienced colposcopists may find it challenging to accurately identify the type and location of the TZ during a colposcopy examination. Objective: This study aims to present an artificial intelligence (AI) method for identifying the TZ to enhance colposcopy examination and evaluate its potential clinical application. Methods: The study retrospectively collected data from 3616 women who underwent colposcopy at 6 tertiary hospitals in China between 2019 and 2021. A dataset from 4 hospitals was collected for model conduction. An independent dataset was collected from the other 2 geographic hospitals to validate model performance. There is no overlap between the training and validation datasets. Anonymized digital records, including each colposcopy image, baseline clinical characteristics, colposcopic findings, and pathological outcomes, were collected. The classification model was proposed as a lightweight neural network with multiscale feature enhancement capabilities and designed to classify the 3 types of TZ. The pretrained FastSAM model was first implemented to identify the location of the new squamocolumnar junction for segmenting the TZ. Overall accuracy, average precision, and recall were evaluated for the classification and segmentation models. The classification performance on the external validation was assessed by sensitivity and specificity. Results: The optimal TZ classification model performed with 83.97\% classification accuracy on the test set, which achieved average precision of 91.84\%, 89.06\%, and 95.62\% for types 1, 2, and 3, respectively. The recall and mean average precision of the TZ segmentation model were 0.78 and 0.75, respectively. The proposed model demonstrated outstanding performance in predicting 3 types of the TZ, achieving the sensitivity with 95\% CIs for TZ1, TZ2, and TZ3 of 0.78 (0.74-0.81), 0.81 (0.78-0.82), and 0.8 (0.74-0.87), respectively, with specificity with 95\% CIs of 0.94 (0.92-0.96), 0.83 (0.81-0.86), and 0.91 (0.89-0.92), based on a comprehensive external dataset of 1335 cases from 2 of the 6 hospitals. Conclusions: Our proposed AI-based identification system classified the type of cervical TZs and delineated their location on multicenter, colposcopic, high-resolution images. The findings of this study have shown its potential to predict TZ types and specific regions accurately. It was developed as a valuable assistant to encourage precise colposcopic examination in clinical practice. ",
doi="10.2196/69672",
url="https://cancer.jmir.org/2025/1/e69672"
}


@Article{info:doi/10.2196/63347,
author="{\vS}uto Pavi{\v c}i{\'c}, Jelena
and Maru{\vs}i{\'c}, Ana
and Buljan, Ivan",
title="Using ChatGPT to Improve the Presentation of Plain Language Summaries of Cochrane Systematic Reviews About Oncology Interventions: Cross-Sectional Study",
journal="JMIR Cancer",
year="2025",
month="Mar",
day="19",
volume="11",
pages="e63347",
keywords="health literacy",
keywords="patient education",
keywords="health communication",
keywords="ChatGPT",
keywords="neoplasms",
keywords="Cochrane",
keywords="oncology",
keywords="plain language",
keywords="medical information",
keywords="decision-making",
keywords="large language model",
keywords="artificial intelligence",
keywords="AI",
abstract="Background: Plain language summaries (PLSs) of Cochrane systematic reviews are a simple format for presenting medical information to the lay public. This is particularly important in oncology, where patients have a more active role in decision-making. However, current PLS formats often exceed the readability requirements for the general population. There is still a lack of cost-effective and more automated solutions to this problem. Objective: This study assessed whether a large language model (eg, ChatGPT) can improve the readability and linguistic characteristics of Cochrane PLSs about oncology interventions, without changing evidence synthesis conclusions. Methods: The dataset included 275 scientific abstracts and corresponding PLSs of Cochrane systematic reviews about oncology interventions. ChatGPT-4 was tasked to make each scientific abstract into a PLS using 3 prompts as follows: (1) rewrite this scientific abstract into a PLS to achieve a Simple Measure of Gobbledygook (SMOG) index of 6, (2) rewrite the PLS from prompt 1 so it is more emotional, and (3) rewrite this scientific abstract so it is easier to read and more appropriate for the lay audience. ChatGPT-generated PLSs were analyzed for word count, level of readability (SMOG index), and linguistic characteristics using Linguistic Inquiry and Word Count (LIWC) software and compared with the original PLSs. Two independent assessors reviewed the conclusiveness categories of ChatGPT-generated PLSs and compared them with original abstracts to evaluate consistency. The conclusion of each abstract about the efficacy and safety of the intervention was categorized as conclusive (positive/negative/equal), inconclusive, or unclear. Group comparisons were conducted using the Friedman nonparametric test. Results: ChatGPT-generated PLSs using the first prompt (SMOG index 6) were the shortest and easiest to read, with a median SMOG score of 8.2 (95\% CI 8?8.4), compared with the original PLSs (median SMOG score 13.1, 95\% CI 12.9?13.4). These PLSs had a median word count of 240 (95\% CI 232?248) compared with the original PLSs' median word count of 364 (95\% CI 339?388). The second prompt (emotional tone) generated PLSs with a median SMOG score of 11.4 (95\% CI 11.1?12), again lower than the original PLSs. PLSs produced with the third prompt (write simpler and easier) had a median SMOG score of 8.7 (95\% CI 8.4?8.8). ChatGPT-generated PLSs across all prompts demonstrated reduced analytical tone and increased authenticity, clout, and emotional tone compared with the original PLSs. Importantly, the conclusiveness categorization of the original abstracts was unchanged in the ChatGPT-generated PLSs. Conclusions: ChatGPT can be a valuable tool in simplifying PLSs as medically related formats for lay audiences. More research is needed, including oversight mechanisms to ensure that the information is accurate, reliable, and culturally relevant for different audiences. ",
doi="10.2196/63347",
url="https://cancer.jmir.org/2025/1/e63347"
}


@Article{info:doi/10.2196/63677,
author="Grilo, Ana
and Marques, Catarina
and Corte-Real, Maria
and Carolino, Elisabete
and Caetano, Marco",
title="Assessing the Quality and Reliability of ChatGPT's Responses to Radiotherapy-Related Patient Queries: Comparative Study With GPT-3.5 and GPT-4",
journal="JMIR Cancer",
year="2025",
month="Apr",
day="16",
volume="11",
pages="e63677",
keywords="artificial intelligence",
keywords="ChatGPT",
keywords="large language model",
keywords="radiotherapy",
keywords="patient information",
keywords="quality",
keywords="internet access",
keywords="health information",
keywords="cancer awareness",
keywords="accuracy",
keywords="readability",
keywords="chatbot",
keywords="patient query",
keywords="chat generative pretrained transformer",
keywords="OpenAI",
keywords="natural language processing",
keywords="patients with cancer",
abstract="Background: Patients frequently resort to the internet to access information about cancer. However, these websites often lack content accuracy and readability. Recently, ChatGPT, an artificial intelligence--powered chatbot, has signified a potential paradigm shift in how patients with cancer can access vast amounts of medical information, including insights into radiotherapy. However, the quality of the information provided by ChatGPT remains unclear. This is particularly significant given the general public's limited knowledge of this treatment and concerns about its possible side effects. Furthermore, evaluating the quality of responses is crucial, as misinformation can foster a false sense of knowledge and security, lead to noncompliance, and result in delays in receiving appropriate treatment. Objective: This study aims to evaluate the quality and reliability of ChatGPT's responses to common patient queries about radiotherapy, comparing the performance of ChatGPT's two versions: GPT-3.5 and GPT-4. Methods: We selected 40 commonly asked radiotherapy questions and entered the queries in both versions of ChatGPT. Response quality and reliability were evaluated by 16 radiotherapy experts using the General Quality Score (GQS), a 5-point Likert scale, with the median GQS determined based on the experts' ratings. Consistency and similarity of responses were assessed using the cosine similarity score, which ranges from 0 (complete dissimilarity) to 1 (complete similarity). Readability was analyzed using the Flesch Reading Ease Score, ranging from 0 to 100, and the Flesch-Kincaid Grade Level, reflecting the average number of years of education required for comprehension. Statistical analyses were performed using the Mann-Whitney test and effect size, with results deemed significant at a 5\% level (P=.05). To assess agreement between experts, Krippendorff $\alpha$ and Fleiss $\kappa$ were used. Results: GPT-4 demonstrated superior performance, with a higher GQS and a lower number of scores of 1 and 2, compared to GPT-3.5. The Mann-Whitney test revealed statistically significant differences in some questions, with GPT-4 generally receiving higher ratings. The median (IQR) cosine similarity score indicated substantial similarity (0.81, IQR 0.05) and consistency in the responses of both versions (GPT-3.5: 0.85, IQR 0.04; GPT-4: 0.83, IQR 0.04). Readability scores for both versions were considered college level, with GPT-4 scoring slightly better in the Flesch Reading Ease Score (34.61) and Flesch-Kincaid Grade Level (12.32) compared to GPT-3.5 (32.98 and 13.32, respectively). Responses by both versions were deemed challenging for the general public. Conclusions: Both GPT-3.5 and GPT-4 demonstrated having the capability to address radiotherapy concepts, with GPT-4 showing superior performance. However, both models present readability challenges for the general population. Although ChatGPT demonstrates potential as a valuable resource for addressing common patient queries related to radiotherapy, it is imperative to acknowledge its limitations, including the risks of misinformation and readability issues. In addition, its implementation should be supported by strategies to enhance accessibility and readability. ",
doi="10.2196/63677",
url="https://cancer.jmir.org/2025/1/e63677"
}


@Article{info:doi/10.2196/59464,
author="Rivera Rivera, N. Jessica
and Snir, Moran
and Simmons, Emilie
and Schmidlen, Tara
and Sholeh, Misha
and Maconi, Leigh Melinda
and Geiss, Carley
and Fulton, Hayden
and Barton, Laura
and Gonzalez, D. Brian
and Permuth, Jennifer
and Vadaparampil, Susan",
title="Developing and Assessing a Scalable Digital Health Tool for Pretest Genetic Education in Patients With Early-Onset Colorectal Cancer: Mixed Methods Design",
journal="JMIR Cancer",
year="2025",
month="Jan",
day="17",
volume="11",
pages="e59464",
keywords="genetic education",
keywords="genetic testing",
keywords="genetic counseling",
keywords="digital health",
keywords="early-onset colorectal cancer",
abstract="Background: National guidelines recommend germline genetic testing (GT) for all patients with early-onset colorectal cancer. With recent advances in targeted therapies and GT, these guidelines are expected to expand to include broader groups of patients with colorectal cancer. However, there is a shortage of genetic professionals to provide the necessary education and support for informed consent. As such, there is a pressing need to identify alternative approaches to facilitate and expedite access to GT. Objective: This study describes the development of a pretest education intervention, Nest-CRC, to facilitate the uptake of germline GT among patients with early-onset colorectal cancer. Patients with early-onset colorectal cancer and health care providers reviewed Nest-CRC, and their reactions and recommendations were captured using a nested mixed methods approach. Methods: Using the learner verification approach, we conducted 2 sequential phases of surveys and interviews with English- and Spanish-speaking patients with early-onset colorectal cancer and health care providers. The surveys assessed participants' experiences with genetic services and provided immediate feedback on the Nest-CRC genetic education modules. Semistructured interviews evaluated participants' perceptions of self-efficacy, attraction, comprehension, cultural acceptability, and usability of Nest-CRC. Survey data were analyzed using descriptive statistics (mean, median, and proportions), while interview data were analyzed through line-by-line coding of the transcribed interviews. After each phase, Nest-CRC was refined based on participants' recommendations. Results: A total of 52 participants, including 39 patients with early-onset colorectal cancer and 13 providers, participated in the study. Of these, 19 patients and 6 providers participated in phase 1 (N=25), and 20 patients and 7 providers participated in phase 2 (N=27). Most participants (phase 1: 23/25, 92\%, to 25/25, 100\%; phase 2: 24/27, 89\%, to 27/27, 100\%) agreed that each of the 5 education modules was easy to understand and helpful; 13 patients reported no history of GT, with 11 (85\%) expressing interest in GT and 2 (15\%) remaining unsure after completing Nest-CRC. Participants reported that Nest-CRC provided sufficient information to help them decide about GT. The tool was deemed acceptable by individuals from diverse backgrounds, and participants found it visually attractive, easy to comprehend, and user-friendly. Conclusions: The findings revealed that Nest-CRC is a promising strategy for facilitating pretest education and promoting GT. Nest-CRC has been refined based on participant recommendations and will be re-evaluated. ",
doi="10.2196/59464",
url="https://cancer.jmir.org/2025/1/e59464"
}


@Article{info:doi/10.2196/71062,
author="Marrison, Tucker Sarah
and Shungu, Nicholas
and Diaz, Vanessa",
title="Perception and Counseling for Cardiac Health in Breast Cancer Survivors Using the Health Belief Model: Qualitative Analysis",
journal="JMIR Cancer",
year="2025",
month="Jul",
day="3",
volume="11",
pages="e71062",
keywords="cardiovascular health",
keywords="cancer survivorship",
keywords="lifestyle counseling",
keywords="breast cancer",
keywords="cancer survivors",
abstract="Background: Breast cancer survivors have increased cardiovascular risk compared to those without cancer history. Cardiovascular disease is the most common cause of death in breast cancer survivors. Cardiovascular risk in breast cancer survivors is impacted by both cancer treatment--associated effects and in risk factors for breast cancer and cardiovascular disease overlap. Strategies to improve screening for and management of cardiovascular disease in breast cancer survivors are needed to improve the delivery of survivorship care. Objective: This study aims to assess current cardiovascular risk counseling practices and perceived cardiovascular risk in breast cancer survivors. Methods: Semistructured interviews were conducted from May to December 2021 with breast cancer survivors identified as having a primary care clinician within an academic family medicine center in Charleston, South Carolina. The interview guide and content were developed using the Health Belief Model with a focus on cardiovascular risk behaviors, risk perception, and barriers to risk reduction. Analysis of categorical data was conducted by frequency and quantitative variables by mean and SD. Template analysis was performed for qualitative analysis. Outcome measures included self-reported history of cardiovascular disease, risk perception, and risk behaviors. Results: The average age of participants (n=19) was 54 (SD 7) years; 68\% (13/19) were White and 32\% (6/19) were Black or African American. Of the interviewed women, 90\% (17/19) reported a personal history and 90\% (17/19) reported a family history of cardiovascular disease. Only 53\% (10/19) had previously reported receipt of cardiovascular counseling. Primary care most commonly provided counseling, followed by oncology. Among breast cancer survivors, 32\% (6/19) reported being at increased cardiovascular risk, and 47\% (9/19) were unsure of their relative cardiovascular risk. Factors affecting perceived cardiovascular risk included family history, cancer treatments, cardiovascular diagnoses, and lifestyle factors. Video (15/19, 79\%) and SMS text messaging (13/19, 68\%) were the most highly reported mechanisms through which breast cancer survivors requested to receive additional information and counseling on cardiovascular risk and risk reduction. Commonly reported barriers to risk reduction such as physical activity included time for meal planning and exercise, resources to support dietary and exercise changes, physical limitations, and competing responsibilities. Barriers specific to survivorship status included concerns for immune status during the COVID-19 pandemic, physical limitations associated with cancer treatment, and psychosocial aspects of cancer survivorship. Conclusions: Breast cancer survivors identified that factors associated with their cancer diagnosis and treatment both impacted their cardiovascular risk and introduced additional barriers to risk reduction. Potential strategies to improve counseling and awareness around cardiovascular risk include video and messaging platforms. Further risk reduction strategies should consider the unique challenges of cancer survivorship in delivery and implementation. ",
doi="10.2196/71062",
url="https://cancer.jmir.org/2025/1/e71062"
}


@Article{info:doi/10.2196/60034,
author="Dabbagh, Zakery
and Najjar, Reem
and Kamberi, Ariana
and Gerber, S. Ben
and Singh, Aditi
and Soni, Apurv
and Cutrona, L. Sarah
and McManus, D. David
and Faro, M. Jamie",
title="Usability and Implementation Considerations of Fitbit and App Intervention for Diverse Cancer Survivors: Mixed Methods Study",
journal="JMIR Cancer",
year="2025",
month="Feb",
day="24",
volume="11",
pages="e60034",
keywords="physical activity",
keywords="cancer survivor",
keywords="wearable device",
keywords="smartphone app",
keywords="diverse",
keywords="Fitbit",
keywords="wearable",
keywords="feasibility",
keywords="usability",
keywords="digital health",
keywords="digital health method",
keywords="breast cancer",
keywords="Hispanic",
keywords="women",
keywords="mobile health",
keywords="activity tracker",
keywords="mHealth",
abstract="Background: Despite the known benefits of physical activity, cancer survivors remain insufficiently active. Prior trials have adopted digital health methods, although several have been pedometer-based and enrolled mainly female, non-Hispanic White, and more highly educated survivors of breast cancer. Objective: The objective of this study was to test a previously developed mobile health system consisting of a Fitbit activity tracker and the MyDataHelps smartphone app for feasibility in a diverse group of cancer survivors, with the goal of refining the program and setting the stage for a larger future trial. Methods: Participants were identified from one academic medical center's electronic health records, referred by a clinician, or self-referred to participate in the study. Participants were screened for eligibility, enrolled, provided a Fitbit activity tracker, and instructed to download the Fitbit: Health \& Wellness and MyDataHelps apps. They completed usability surveys at 1 and 3 months. Interviews were conducted at the end of the 3-month intervention with participants and cancer care clinicians to assess the acceptability of the intervention and the implementation of the intervention into clinical practice, respectively. Descriptive statistics were calculated for demographics, usability surveys, and Fitbit adherence and step counts. Rapid qualitative analysis was used to identify key findings from interview transcriptions. Results: Of the 100 patients screened for eligibility, 31 were enrolled in the trial (mean age 64.8, SD 11.1 years; female patients=17/31, 55\%; Hispanic or Latino=7/31, 23\%; non-White=11/31, 35\%; less than a bachelor's degree=14/31, 45\%; and household income <US \$75,000=11/31, 35\%). The mean (SD) years since diagnosis was 7.1 (8.2), and the two most frequent cancer diagnoses were prostate (9/31, 29\%) and breast (4/31, 13\%) cancer. Participants provided positive feedback on the MyDataHelps app usability; the overall app quality received a mean score of 3.79 (SD 0.82) on a 5-point Likert scale (1=worst, 5=best). Interviews with 10 patients yielded four themes: (1) Fitbit and app setup was easy but the research team provided assistance, when needed, which was helpful, (2) motivational messages within the app were not memorable, (3) step counts and Fitbit notifications were motivating, and (4) medical professionals viewing their data were acceptable. Interviews with 5 cancer care clinicians yielded four themes: (1) some patients used wearables but rarely discussed data with clinicians; (2) activity trackers can be helpful to motivate patients and keep them accountable; (3) objective activity measures---similar to BMI, weight, and blood pressure--- that they can track over time and refer to afterward were preferred; and (4) training and systematic processes to view these data as part of active workflow were desired. Conclusions: Implementing a remotely delivered, light-intensity physical activity program was feasible and acceptable in a sample of diverse cancer survivors. Future studies should consider registry-based methods and work with clinicians to engage hard-to-reach survivor populations who have low physical activity levels and disproportionately high adverse health outcomes. Trial Registration: ClinicalTrials.gov NCT05417438; https://clinicaltrials.gov/study/NCT05417438 ",
doi="10.2196/60034",
url="https://cancer.jmir.org/2025/1/e60034"
}


@Article{info:doi/10.2196/59391,
author="Newton, Lorelei
and Monkman, Helen
and Fullerton, Claire",
title="Exploring Older Adult Cancer Survivors' Digital Information Needs: Qualitative Pilot Study",
journal="JMIR Cancer",
year="2025",
month="Feb",
day="27",
volume="11",
pages="e59391",
keywords="older adults",
keywords="cancer survivors",
keywords="digital health literacy",
keywords="digital health technologies",
keywords="aging",
keywords="qualitative",
keywords="pilot study",
keywords="semistructured interview",
abstract="Background: Older adults (aged >65 years) are disproportionately affected by cancer at a time when Canadians are surviving cancer in an unprecedented fashion. Contrary to persistent ageist assumptions, not only do the majority of older adult cancer survivors use digital health technologies (DHTs) regularly, such technologies also serve as important sources of their health information. Although older adults' transition to cancer survivorship is connected to the availability and provision of relevant and reliable information, little evidence exists as to how they use DHTs to supplement their understanding of their unique situation to manage, and make decisions about, their ongoing cancer-related concerns. Objective: This pilot study, which examined older adult cancer survivors' use of DHTs, was conducted to support a larger study designed to explore how digital health literacy dimensions might affect the management of cancer survivorship sequelae. Understanding DHT use is also an important consideration for digital health literacy. Thus, we sought to investigate older adult cancer survivors' perceptions of DHTs in the context of accessing information about their health, health care systems, and health care providers. Methods: A qualitative pilot study, which involved semistructured interviews with older adult cancer survivors (N=5), was conducted to explore how participants interacted with, accessed, and searched for information, as well as how DHT use related to their cancer survivorship. Institutional ethics approval (\#21?0421) was obtained. Interpretive description inquiry---a practice-based approach suitable for generating applied knowledge---supported exploration of the research question. Thematic analysis was used to examine the transcripts for patterns of meaning (themes). Results: Assessing the credibility of digital information remains challenging for older adult cancer survivors. Identified benefits of DHTs included improved access to meet health information needs, older adult cancer survivors feeling empowered to make informed decisions regarding their health trajectory, and the ability to connect with interdisciplinary teams for care continuity. Additionally, participants described feeling disconnected when DHTs seemed to be used as substitutes for human interaction. The results of this pilot study were used to create 12 additional questions to supplement a digital health literacy survey, through which we will seek a more fulsome account of the relationship between digital health literacy and DHTs for older adult cancer survivors. Conclusions: Overall, this pilot study confirmed the utility of DHTs in enhancing the connection of older adult cancer survivors to their health care needs. Importantly, this connection exists on a continuum, and providing greater access to technologies, in combination with human support, leads to feelings of empowerment. DHTs are an important aspect of contemporary health care; yet, these technologies must be seen as complementary and not as replacements for human interaction. Otherwise, we risk dehumanizing patients and disconnecting them from the care that they need and deserve. ",
doi="10.2196/59391",
url="https://cancer.jmir.org/2025/1/e59391"
}


@Article{info:doi/10.2196/66636,
author="Werts-Pelter, J. Samantha
and Chen, Zhao
and Bea, W. Jennifer
and Sokan, E. Amanda
and Thomson, A. Cynthia",
title="Analysis of the Relationship Between Rural-Urban Status and Use of Digital Health Technology Among Older Cancer Survivors Based on the Health Information National Trends Survey: Cross-Sectional Analysis",
journal="JMIR Cancer",
year="2025",
month="Mar",
day="4",
volume="11",
pages="e66636",
keywords="cancer",
keywords="non-metropolitan",
keywords="disparities",
keywords="digital divide",
keywords="health research",
keywords="aging",
keywords="rural-urban",
keywords="digital health technology",
keywords="cross-sectional",
keywords="health behaviors",
keywords="mobile phone",
abstract="Background: Though telehealth has been a promising avenue for engaging cancer survivors with health care and lifestyle programming, older and rural-dwelling cancer survivors may have additional challenges in accessing digital devices and tools that have not yet been described. This study aimed to use a robust, nationally representative sample collected in 2022 to provide an updated view of digital technology use and the use of technology for health in this population. Objective: This study aimed to examine the prevalence of digital technology use for health-related activities among older cancer survivors in both rural and urban settings. The primary outcomes of interest included (1) internet access and use for health-related activities, (2) digital device ownership and use as a tool for health behaviors, (3) use of social media for health, and (4) use of telehealth. Methods: A cross-sectional analysis of the National Cancer Institute's Health Information National Trends Survey Cycle 6 (HINTS 6) was completed to examine the prevalence of digital technology use among older cancer survivors. For analysis, the sample was restricted to cancer survivors over the age of 60 years (n=710). Unadjusted and adjusted logistic regression models were used to test the association between rurality and digital health tool use. Results: Overall, 17\% (125/710) of the sample lived in a rural area of the United States and the mean sample age was 73 (SD 8.2) years. Older cancer survivors, regardless of rural-urban status, reported a high prevalence of internet usage (n=553, 79.9\%), digital device ownership (n=676, 94.9\%), and social media use (n=448, 66.6\%). In unadjusted models, rural survivors were less likely than urban survivors to report that they had used a health or wellness application in the previous year (odds ratio [OR] 0.56, 95\% CI 0.32-0.97; P=.04). In adjusted models, rural survivors were more likely to report that they had shared personal health information on social media (OR 2.64, 95\% CI 1.13-6.19; P=.03). There were no differences in the proportion of rural and urban respondents who reported receiving health services through telehealth in the previous year. Conclusions: Regardless of the residential status, older cancer survivors report high internet and technology use for health-related activities. These results show promise for the feasibility of using digital technologies to implement supportive care and wellness programming with older cancer survivors. ",
doi="10.2196/66636",
url="https://cancer.jmir.org/2025/1/e66636"
}


@Article{info:doi/10.2196/58938,
author="Fraterman, Itske
and Sacchi, Lucia
and Mallo, Henk
and Tibollo, Valentina
and Glaser, Catherina Savannah Lucia
and Medlock, Stephanie
and Cornet, Ronald
and Gabetta, Matteo
and Hisko, Vitali
and Khadakou, Vadzim
and Barkan, Ella
and Del Campo, Laura
and Glasspool, David
and Kogan, Alexandra
and Lanzola, Giordano
and Leizer, Roy
and Ottaviano, Manuel
and Peleg, Mor
and ?niata?a, Konrad
and Lisowska, Aneta
and Wilk, Szymon
and Parimbelli, Enea
and Quaglini, Silvana
and Rizzo, Mimma
and Locati, Deborah Laura
and Boekhout, Annelies
and van de Poll-Franse, V. Lonneke
and Wilgenhof, Sofie",
title="Exploring the Impact of the Multimodal CAPABLE eHealth Intervention on Health-Related Quality of Life in Patients With Melanoma Undergoing Immune-Checkpoint Inhibition: Prospective Pilot Study",
journal="JMIR Cancer",
year="2025",
month="Jan",
day="30",
volume="11",
pages="e58938",
keywords="eHealth",
keywords="melanoma",
keywords="cancer",
keywords="fatigue",
keywords="quality of life",
keywords="intervention",
keywords="pilot study",
keywords="exploratory",
keywords="health-related",
keywords="interventions",
keywords="symptom",
keywords="monitoring",
keywords="well-being",
keywords="immunotherapy",
keywords="immune-related",
keywords="immune-checkpoint inhibitor",
keywords="patient",
keywords="feasibility",
keywords="smartphone",
keywords="app",
keywords="smartwatch",
keywords="linear regression model",
keywords="mobile phone",
abstract="Background: Patients with melanoma receiving immunotherapy with immune-checkpoint inhibitors often experience immune-related adverse events, cancer-related fatigue, and emotional distress, affecting health-related quality of life (HRQoL) and clinical outcome to immunotherapy. eHealth tools can aid patients with cancer in addressing issues, such as adverse events and psychosocial well-being, from various perspectives. Objective: This study aimed to explore the effect of the Cancer Patients Better Life Experience (CAPABLE) system, accessed through a mobile app, on HRQoL compared with a matched historical control group receiving standard care. CAPABLE is an extensively tested eHealth app, including educational material, remote symptom monitoring, and well-being interventions. Methods: This prospective pilot study compared an exploratory cohort that received the CAPABLE smartphone app and a multisensory smartwatch for 6 months (intervention) to a 2:1 individually matched historical prospective control group. HRQoL data were measured with the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 at baseline (T0), 3 months (T1), and 6 months (T2) after start of treatment. Mixed effects linear regression models were used to compare HRQoL between the 2 groups over time. Results: From the 59 eligible patients for the CAPABLE intervention, 31 (53\%) signed informed consent to participate. Baseline HRQoL was on average 10 points higher in the intervention group compared with controls, although equally matched on baseline and clinical characteristics. When correcting for sex, age, disease stage, and baseline scores, an adjusted difference in fatigue of ?5.09 (95\% CI ?15.20 to 5.02, P=.32) at month 3 was found. No significant nor clinically relevant adjusted differences on other HRQoL domains over time were found. However, information satisfaction was significantly higher in the CAPABLE group ($\beta$=8.71, 95\% CI 1.54?15.88, P=.02). Conclusions: The intervention showed a limited effect on HRQoL, although there was a small improvement in fatigue at 3 months, as well as information satisfaction. When aiming at personalized patient and survivorship care, further optimization and prospective investigation of eHealth tools is warranted. Trial Registration: ClinicalTrials NCT05827289; https://clinicaltrials.gov/study/NCT05827289 International Registered Report Identifier (IRRID): RR2-10.2196/49252 ",
doi="10.2196/58938",
url="https://cancer.jmir.org/2025/1/e58938"
}


@Article{info:doi/10.2196/58834,
author="Voigt, Kelly
and Sun, Yingtao
and Patandin, Ayush
and Hendriks, Johanna
and Goossens, Hendrik Richard
and Verhoef, Cornelis
and Husson, Olga
and Gr{\"u}nhagen, Dirk
and Jung, Jiwon",
title="A Machine Learning Approach Using Topic Modeling to Identify and Assess Experiences of Patients With Colorectal Cancer: Explorative Study",
journal="JMIR Cancer",
year="2025",
month="Jan",
day="27",
volume="11",
pages="e58834",
keywords="colorectal cancer",
keywords="forum",
keywords="topic modeling",
keywords="patient journey",
keywords="patient experience",
keywords="AI",
keywords="machine learning",
keywords="cancer care",
keywords="cancer survivor",
keywords="United States",
keywords="quality of life",
keywords="post",
keywords="topic",
keywords="artificial intelligence",
abstract="Background: The rising number of cancer survivors and the shortage of health care professionals challenge the accessibility of cancer care. Health technologies are necessary for sustaining optimal patient journeys. To understand individuals' daily lives during their patient journey, qualitative studies are crucial. However, not all patients wish to share their stories with researchers. Objective: This study aims to identify and assess patient experiences on a large scale using a novel machine learning--supported approach, leveraging data from patient forums. Methods: Forum posts of patients with colorectal cancer (CRC) from the Cancer Survivors Network USA were used as the data source. Topic modeling, as a part of machine learning, was used to recognize the topic patterns in the posts. Researchers read the most relevant 50 posts on each topic, dividing them into ``home'' or ``hospital'' contexts. A patient community journey map, derived from patients stories, was developed to visually illustrate our findings. CRC medical doctors and a quality-of-life expert evaluated the identified topics of patient experience and the map. Results: Based on 212,107 posts, 37 topics and 10 upper clusters were produced. Dominant clusters included ``Daily activities while living with CRC'' (38,782, 18.3\%) and ``Understanding treatment including alternatives and adjuvant therapy'' (31,577, 14.9\%). Topics related to the home context had more emotional content compared with the hospital context. The patient community journey map was constructed based on these findings. Conclusions: Our study highlighted the diverse concerns and experiences of patients with CRC. The more emotional content in home context discussions underscores the personal impact of CRC beyond clinical settings. Based on our study, we found that a machine learning-supported approach is a promising solution to analyze patients' experiences. The innovative application of patient community journey mapping provides a unique perspective into the challenges in patients' daily lives, which is essential for delivering appropriate support at the right moment. ",
doi="10.2196/58834",
url="https://cancer.jmir.org/2025/1/e58834"
}


@Article{info:doi/10.2196/55746,
author="Kamminga, Willemina Nadia Christina
and Lugtenberg, Marjolein
and Van den Broek, Annabel Julia
and Nijsten, Tamar
and Wakkee, Marlies
and Tabeau, Kasia",
title="Exploring Motives Behind Ideal Melanoma Survivorship Care Plans With Multiple Stakeholders: A Cocreation Study",
journal="JMIR Cancer",
year="2025",
month="Jan",
day="2",
volume="11",
pages="e55746",
keywords="cocreation",
keywords="survivorship care",
keywords="psycho-oncology",
keywords="supportive care",
keywords="motives",
keywords="melanoma",
keywords="cancer survivor",
keywords="melanoma care",
abstract="Background: Survivorship care plans (SCPs), ie, personalized health care plans for cancer survivors, can be used to support the growing group of melanoma survivors throughout their disease trajectory. However, implementation and effectiveness of SCPs are suboptimal and could benefit from the involvement of stakeholders in developing a user-centered design. Objective: The aim of this study was to identify the ideal SCP for patients with melanoma in terms of functions and features to be included according to different stakeholders and to explore their underlying motives. Methods: In total, 3 cocreation sessions were organized with mixed samples of stakeholders, ie, patients with (a history of) melanoma (n=4), health care providers (HCPs) active in melanoma care (n=3), and IT specialists active in hospital IT departments (n=6). They were invited to compose their ideal melanoma SCP based on potential functions and features identified from prior qualitative research. These functions and features belonged to one of the four main categories of survivorship care (SSC): (1) information and education, (2) identification and treatment, (3) oncological follow-up, and (4) coordination. Participants were invited to explain their motives for including functions and features. Ideas were shared between stakeholders, and interaction was promoted. Descriptive statistics were used to determine the ideal SCP per stakeholder group. To analyze underlying motives, all cocreation sessions were audio-taped, transcribed verbatim, and analyzed in a thematic content analysis. Results: With regard to their ideal SCPs, all stakeholders added functions from all 4 SSC categories. Patients assembled a rather compact SCP with category 2 on identification and treatment being most important. Both HCPs and IT professionals constructed a somewhat larger SCP, with category 3 on oncological follow-up being the most important aspect and HCPs also focusing on category 4 on coordination. As for the motives behind their ideal SCP compositions, patients predominantly added functions based on their personal experiences or experiences from fellow patients, whereas both HCPS and IT professionals based their compositions primarily on their respective areas of expertise: HCPs related their additions to their roles as medical practitioners; for example, in providing a complete treatment plan and obtaining informed consent, while IT professionals' contributions were mainly influenced by feasibility and privacy concerns. Conclusions: This cocreation study provides insights into stakeholders' ideal melanoma SCP and the motivations behind them. Considering the diversity in both the preferences and underlying motives regarding SCP composition between patients, HCPs, and IT specialists, it is crucial to develop a broad SCP that extends beyond traditional SCP content, emphasizing personalization. In addition to continued stakeholder involvement, efforts should be focused on addressing potential feasibility and privacy issues to ensure the SCP meets both patients' and HCPs' needs. ",
doi="10.2196/55746",
url="https://cancer.jmir.org/2025/1/e55746"
}


@Article{info:doi/10.2196/57834,
author="Hou, J. Sharon H.
and Henry, Brianna
and Drummond, Rachelle
and Forbes, Caitlin
and Mendon{\c{c}}a, Kyle
and Wright, Holly
and Rahamatullah, Iqra
and Tutelman, R. Perri
and Zwicker, Hailey
and Stokoe, Mehak
and Duong, Jenny
and Drake, K. Emily
and Erker, Craig
and Taccone, S. Michael
and Sutherland, Liam
and Nathan, Paul
and Spavor, Maria
and Goddard, Karen
and Reynolds, Kathleen
and Schulte, M. Fiona S.",
title="Co-Designing Priority Components of an mHealth Intervention to Enhance Follow-Up Care in Young Adult Survivors of Childhood Cancer and Health Care Providers: Qualitative Descriptive Study",
journal="JMIR Cancer",
year="2025",
month="Apr",
day="25",
volume="11",
pages="e57834",
keywords="mobile health",
keywords="mHealth",
keywords="pediatric oncology",
keywords="cancer survivorship",
keywords="qualitative research",
keywords="patient-oriented research",
keywords="co-design",
keywords="intervention development",
abstract="Background: Survivors of childhood cancer are at risk of medical, psychological, and social late effects. To screen for their risks, receipt of consistent, cancer-specific follow-up care is crucial. However, <50\% of survivors attend their aftercare, and only 35\% of them recognize that they could have a serious health problem. The use of mobile health (mHealth) is a promising form of intervention to educate, connect, and empower survivors of childhood cancer on the importance of follow-up care. Objective: This study aimed to use co-design to identify the priority components to include in an mHealth intervention with young adult (aged between 18 and 39 years) survivors of childhood cancer and health care providers. Methods: This study was conducted between January and November 2022 in Canada and used patient-oriented research methods. Participants were recruited through local or provincial long-term follow-up clinics, using convenience sampling from patient partners who assisted in recruiting survivors across geographical areas in western, central, and eastern Canada, and social media outreach (X, formally known as Twitter; Facebook; and Instagram). Qualitative descriptive data (focus group interviews) from survivors of childhood cancer and health care providers (individual interviews) were gathered. We analyzed the collected data using reflexive thematic analysis and verified it through member checking techniques through an online community engagement event. Results: We conducted with patient partners 5 online (Zoom) focus groups with 22 survivors of childhood cancer (mean age 29.19, SD 4.78 y). We conducted individual telephone interviews with 7 health care providers. Participants identified five priority areas to be included in an mHealth intervention: (1) connections, (2) education and information, (3) engagement, (4) personalization, and (5) resources. Results were shared with and validated by survivors of childhood cancer, their families, health care providers, and academic researchers as part of a community engagement event. Small and large group discussions were facilitated to allow participants to review and discuss the accuracy of the themes derived regarding the core components to be included in mHealth. A graphic recording artist visually captured key ideas from the event. A subset of the participants also completed a web-based satisfaction survey, and responses indicated that the community engagement event was generally well received. Conclusions: Results from this study have provided the necessary foundation to progress in intervention development. The next step of this multiphased project is to build an innovative and accessible mHealth intervention prototype that is based on the identified core components and is grounded in an established conceptual framework for co-design of mHealth. ",
doi="10.2196/57834",
url="https://cancer.jmir.org/2025/1/e57834"
}


@Article{info:doi/10.2196/54625,
author="Tang, Wen-Zhen
and Mo, Shu-Tian
and Xie, Yuan-Xi
and Wei, Tian-Fu
and Chen, Guo-Lian
and Teng, Yan-Juan
and Jia, Kui",
title="Predicting Overall Survival in Patients with Male Breast Cancer: Nomogram Development and External Validation Study",
journal="JMIR Cancer",
year="2025",
month="Mar",
day="4",
volume="11",
pages="e54625",
keywords="male breast cancer",
keywords="specific survival",
keywords="prediction model",
keywords="nomogram",
keywords="Surveillance, Epidemiology, and End Results database",
keywords="SEER database",
abstract="Background: Male breast cancer (MBC) is an uncommon disease. Few studies have discussed the prognosis of MBC due to its rarity. Objective: This study aimed to develop a nomogram to predict the overall survival of patients with MBC and externally validate it using cases from China. Methods: Based on the Surveillance, Epidemiology, and End Results (SEER) database, male patients who were diagnosed with breast cancer between January 2010, and December 2015, were enrolled. These patients were randomly assigned to either a training set (n=1610) or a validation set (n=713) in a 7:3 ratio. Additionally, 22 MBC cases diagnosed at the First Affiliated Hospital of Guangxi Medical University between January 2013 and June 2021 were used for external validation, with the follow-up endpoint being June 10, 2023. Cox regression analysis was performed to identify significant risk variables and construct a nomogram to predict the overall survival of patients with MBC. Information collected from the test set was applied to validate the model. The concordance index (C-index), receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and a Kaplan-Meier survival curve were used to evaluate the accuracy and reliability of the model. Results: A total of 2301 patients with MBC in the SEER database and 22 patients with MBC from the study hospital were included. The predictive model included 7 variables: age (hazard ratio [HR] 1.89, 95\% CI 1.50?2.38), surgery (HR 0.38, 95\% CI 0.29?0.51), marital status (HR 0.75, 95\% CI 0.63?0.89), tumor stage (HR 1.17, 95\% CI 1.05?1.29), clinical stage (HR 1.41, 95\% CI 1.15?1.74), chemotherapy (HR 0.62, 95\% CI 0.50?0.75), and HER2 status (HR 2.68, 95\% CI 1.20?5.98). The C-index was 0.72, 0.747, and 0.981 in the training set, internal validation set, and external validation set, respectively. The nomogram showed accurate calibration, and the ROC curve confirmed the advantage of the model in clinical validity. The DCA analysis indicated that the model had good clinical applicability. Furthermore, the nomogram classification allowed for more accurate differentiation of risk subgroups, and patients with low-risk MBC demonstrated substantially improved survival outcomes compared with medium- and high-risk patients (P<.001). Conclusions: A survival prognosis prediction nomogram with 7 variables for patients with MBC was constructed in this study. The model can predict the survival outcome of these patients and provide a scientific basis for clinical diagnosis and treatment. ",
doi="10.2196/54625",
url="https://cancer.jmir.org/2025/1/e54625"
}


@Article{info:doi/10.2196/60158,
author="Baygi, Fereshteh
and Bitz Trabjerg, Theis
and Jensen, Henrik Lars
and Munch Storsveen, Maria
and Wehberg, Sonja
and Sisler, James Jeffrey
and S{\o}ndergaard, Jens
and Gils{\aa} Hansen, Dorte",
title="Challenges of Cross-Sectoral Video Consultation in Cancer Care on Patients' Perceived Coordination: Randomized Controlled Trial",
journal="JMIR Cancer",
year="2025",
month="Feb",
day="11",
volume="11",
pages="e60158",
keywords="randomized controlled trials",
keywords="video consultations",
keywords="outcome assessment",
keywords="patients' satisfaction",
keywords="patients' care coordination",
keywords="interprofessional relations",
keywords="cancer",
abstract="Background: Patients with cancer need coordinated care for both treatment and concurrent health conditions. This requires collaboration among specialists when using telemedicine services, emphasizing the importance of care continuity. Objective: This study aimed to explore the effects of cross-sectorial video consultation involving oncologists, general practitioners, and patients with cancer on patients' perceived coordination of care, compared with usual care. Methods: This study describes the primary outcomes from a 7-month follow-up of patients in the Partnership Project, a randomized clinical trial. Patients in the intervention group were randomized to receive a ``partnership consultation,'' a shared video consultation with an oncologist, general practitioners, and the patient, in addition to their usual care. Questionnaires were completed for both groups at baseline and 7 months to assess the primary outcome, ``global assessment of inter-sectorial cooperation,'' from the Danish questionnaire ``Patients' attitude to the health care service.'' The questionnaire also included 2 single items and 5 index scales, examining patients' attitude toward cooperation in the health care system. Change in perceived global coordination from baseline to 7 months was compared between intention-to-treat groups using generalized estimating equations in a linear regression model. Results: A total of 278 participants were randomized with 1:1 allocation, with 80 patients receiving the intervention. Further, 210 patients completed the questionnaire at baseline, while 118 responded at 7-month follow-up. The estimated difference in the primary outcome between usual care (?0.13, 95\% CI ?0.38 to 0.12) and intervention (0.11, 95\% CI ?0.11 to 0.34) was 0.24 (95\% CI ?0.09 to 0.58) and not statistically significant (P=.15). Conclusions: Low rates of intervention completion and high levels of missing data compromised the interpretability of our study. While we observed a high level of global assessment of coordination, the estimated intervention effect was smaller than anticipated, with no significant difference in perceived coordination between control and intervention groups. Future studies should explore strategies like patient incentives to increase response rate and improve the evaluation of this innovative health care model. Trial Registration: ClinicalTrials.gov NCT02716168; https://clinicaltrials.gov/study/NCT02716168 International Registered Report Identifier (IRRID): RR2-10.1186/s12875-019-0978-8 ",
doi="10.2196/60158",
url="https://cancer.jmir.org/2025/1/e60158"
}


@Article{info:doi/10.2196/56098,
author="Sommers, Jonathan
and Dizon, S. Don
and Lewis, A. Mark
and Stone, Erik
and Andreoli, Richard
and Henderson, Vida",
title="Assessing Health Information Seeking Behaviors Among Targeted Social Media Users Using an Infotainment Video About a Cancer Clinical Trial: Population-Based Descriptive Study",
journal="JMIR Cancer",
year="2025",
month="Mar",
day="3",
volume="11",
pages="e56098",
keywords="cancer clinical trials",
keywords="digital media",
keywords="social media",
keywords="infotainment",
keywords="recruitment",
keywords="education and awareness",
keywords="edutainment",
keywords="public engagement",
keywords="cancer",
keywords="lack of information",
keywords="health information",
keywords="medical awareness",
keywords="video series",
keywords="public audience",
keywords="low cost",
keywords="research participants",
abstract="Background: The lack of information and awareness about clinical trials, as well as misconceptions about them, are major barriers to cancer clinical trial participation. Digital and social media are dominant sources of health information and offer optimal opportunities to improve public medical awareness and education by providing accurate and trustworthy health information from reliable sources. Infotainment, material intended to both entertain and inform, is an effective strategy for engaging and educating audiences that can be easily disseminated using social media and may be a novel way to improve awareness of and recruitment in clinical trials. Objective: The purpose of this study was to evaluate whether an infotainment video promoting a clinical trial, disseminated using social media, could drive health information seeking behaviors. Methods: As part of a video series, we created an infotainment video focused on the promotion of a specific cancer clinical trial. We instituted a dissemination and marketing process on Facebook to measure video engagement and health information seeking behaviors among targeted audiences who expressed interest in breast cancer research and organizations. To evaluate video engagement, we measured reach, retention, outbound clicks, and outbound click-through rate. Frequencies and descriptive statistics were used to summarize each measure. Results: The video substantially increased health information seeking behavior by increasing viewership from 1 visitor one month prior to launch to 414 outbound clicks from the video to the clinical trial web page during the 21-day social media campaign period. Conclusions: Our study shows that digital and social media tools can be tailored for specific target audiences, are scalable, and can be disseminated at low cost, making it an accessible educational, recruitment, and retention strategy focused on improving the awareness of clinical trials. Trial Registration: ClinicalTrials.gov NCT03418961; https://clinicaltrials.gov/study/NCT03418961 ",
doi="10.2196/56098",
url="https://cancer.jmir.org/2025/1/e56098"
}


@Article{info:doi/10.2196/59483,
author="Yang, Hongwu
and Zhu, Chuangying
and Zhou, Chunyan
and Huang, Ruibin
and Huang, Lipeng
and Chen, Peifen
and Zhu, Shanshan
and Wang, Huanpeng
and Zhu, Chunmin",
title="Evaluation of Douyin Short Videos on Mammography in China: Quality and Reliability Analysis",
journal="JMIR Cancer",
year="2025",
month="Feb",
day="19",
volume="11",
pages="e59483",
keywords="breast cancer",
keywords="mammography",
keywords="Douyin",
keywords="information quality",
keywords="social media",
keywords="video",
keywords="DISCERN",
keywords="Global Quality Score",
keywords="web-based education",
keywords="cancer screening",
keywords="health information",
keywords="medical content",
abstract="Background: Breast cancer is the most common malignant tumor and the fifth leading cause of cancer death worldwide, imposing a significant disease burden in China. Mammography is a key method for breast cancer screening, particularly for early diagnosis. Douyin, a popular social media platform, is increasingly used for sharing health information, but the quality and reliability of mammography-related videos remain unexamined. Objective: This study aimed to evaluate the information quality and reliability of mammography videos on Douyin. Methods: In October 2023, a search using the Chinese keywords for ``mammography'' and ``mammography screening'' was conducted on Douyin. From 200 retrieved videos, 136 mammography-related videos were selected for analysis. Basic video information, content, and sources were extracted. Video content was assessed for comprehensiveness across 7 categories: conception, examination process, applicable objects, precautions, combined examinations, advantages, and report. Completeness was evaluated using a researcher-developed checklist, while reliability and quality were measured using 2 modified DISCERN (mDISCERN) tool and the Global Quality Score (GQS). Correlations between video quality and characteristics were also examined. Results: Among the video sources, 82.4\% (112/136) were attributed to health professionals, and 17.6\% (24/136) were attributed to nonprofessionals. Among health professionals, only 1 was a radiologist. Overall, 77.2\% (105/136) of the videos had useful information about mammography. Among the useful videos, the advantages of mammography were the most frequently covered topic (53/105, 50.5\%). Median values for the mDISCERN and GQS evaluations across all videos stood at 2.5 (IQR 1.63?3) and 2 (IQR 1?2), respectively. Within the subgroup assessment, the median mDISCERN score among the useful and professional groups stood at 2 (IQR 2?3) and 3 (IQR 2?3), respectively, surpassing the corresponding score for the unhelpful and nonprofessional groups at 0 (IQR 0?0) and 0 (IQR 0?0.75; P<.001). Likewise, the median GQS among the useful and professional groups was evaluated at 2 (IQR 1.5?2) and 2 (IQR 1?2), respectively, eclipsing that of the unhelpful and nonprofessional groups at 1 (IQR 1?1) and 1 (IQR 1?1.37; P<.001). The GQS was weak and negatively correlated with the number of likes (r=?0.24; P=.004), comments (r=?0.29; P<.001), and saves (r=?0.20; P=.02). The mDISCERN score was weak and negatively correlated with the number of likes (r=?0.26; P=.002), comments (r=?0.36; P<.001), saves (r=?0.22; P=.009), and shares (r=?0.18; P=.03). Conclusions: The overall quality of mammography videos on Douyin is suboptimal, with most content uploaded by clinicians rather than radiologists. Radiologists should be encouraged to create accurate and informative videos to better educate patients. As Douyin grows as a health information platform, stricter publishing standards are needed to enhance the quality of medical content. ",
doi="10.2196/59483",
url="https://cancer.jmir.org/2025/1/e59483"
}


@Article{info:doi/10.2196/70251,
author="Le Bonniec, Alice
and Sauvaget, Catherine
and Lucas, Eric
and Nassiri, Abdelhak
and Selmouni, Farida",
title="Design and Validation of a Chatbot-Based Cervical Cancer Screening Decision Aid for Women Experiencing Socioeconomic Disadvantage: User-Centered Approach Study",
journal="JMIR Cancer",
year="2025",
month="Jul",
day="24",
volume="11",
pages="e70251",
keywords="cervical cancer screening",
keywords="human papillomavirus self-sampling",
keywords="chatbot",
keywords="decision aid",
keywords="health education",
keywords="underserved populations",
keywords="user-centered design",
keywords="artificial intelligence",
keywords="AI",
abstract="Background: Cervical cancer (CC) screening participation remains suboptimal among vulnerable populations in France. This study aimed to develop and evaluate AppDate-You, a chatbot-based decision aid, to support women from socioeconomically disadvantaged areas in the French Occitanie region to make informed decisions about CC screening, particularly human papillomavirus self-sampling (HPVss). Objective: This study aimed to explore the needs, preferences, and barriers related to CC screening and to design and validate a user-centered, empathetic, and effective chatbot-based decision aid to empower women experiencing socioeconomic challenges in France to make informed choices about HPVss. Methods: The chatbot was developed following a validated framework for developing decision aids. The process included qualitative research involving online and in-person interviews and focus groups with women and health care professionals, followed by alpha testing with both groups and beta testing with women only. Participants included women (both French and non-French speaking) aged between 30 and 65 years from socioeconomically disadvantaged areas of the Occitanie region and health care professionals (general practitioners, gynecologists, and midwives) working with these populations. AppDate-You was made accessible through WhatsApp and Facebook Messenger, offering text-based and voice-based interactions and multimedia content. Results: The exploratory phase identified key barriers to screening and digital tool preferences. Prototype testing revealed great satisfaction with the chatbot's performance, educational value, and content quality. Contrary to the expectations of health care professionals, women from diverse backgrounds, including women who were older and socioeconomically disadvantaged, were willing and able to use the tool. Users---even those with limited digital literacy---found AppDate-You innovative, user-friendly, and informative. In the beta testing phase, 80\% (12/15) of the participants expressed interest in HPVss. Some limitations were identified, such as the chatbot's occasional repetitive responses and the need for clearer medical terminology. Conclusions: This study demonstrates the potential for artificial intelligence chatbots to improve access to health education and increase cervical screening intention among underserved populations. The user-centered approach resulted in a tool that effectively meets the needs of the target population. International Registered Report Identifier (IRRID): RR2-10.2196/39288 ",
doi="10.2196/70251",
url="https://cancer.jmir.org/2025/1/e70251",
url="http://www.ncbi.nlm.nih.gov/pubmed/40705442"
}


@Article{info:doi/10.2196/65960,
author="Le D{\^u}, Katell
and Chauchet, Adrien
and Sadot-Lebouvier, Sophie
and Fitoussi, Olivier
and Fontanet, Bijou
and Saint-Lezer, Arnaud
and Maloisel, Fr{\'e}d{\'e}ric
and Rossi, C{\'e}dric
and Carras, Sylvain
and Parcelier, Anne
and Balavoine, Magali
and Septans, Anne-Lise",
title="Comparison of Electronic Surveillance With Routine Monitoring for Patients With Lymphoma at High Risk of Relapse: Prospective Randomized Controlled Phase 3 Trial (Sentinel Lymphoma)",
journal="JMIR Cancer",
year="2025",
month="May",
day="6",
volume="11",
pages="e65960",
keywords="patient-reported outcome measures",
keywords="lymphoma",
keywords="risk of relapse",
keywords="relapse",
keywords="randomized trial",
keywords="web-based",
keywords="quality of life",
keywords="survival",
keywords="detection",
keywords="progression",
keywords="T-cell lymphoma",
keywords="Hodgkin lymphoma",
abstract="Background: Relapse is a major event in patients with lymphoma. Therefore, early detection may have an impact on quality of life and overall survival. Patient-reported outcome measures have demonstrated clinical benefits for patients with lung cancer; however, evidence is lacking in patients with lymphoma. We evaluated the effect of a web-mediated follow-up application for patients with lymphoma at high risk of relapse. Objective: This study aims to demonstrate that monitoring patients via a web application enables the detection of at least 30\% more significant events occurring between 2 systematic follow-up consultations with the specialist using an electronic questionnaire. Methods: We conducted a prospective, randomized phase 3 trial comparing the impact of web-based follow-up (experimental arm) with a standard follow-up (control arm). The trial was based on a 2-step triangular test and was designed to have a power of 90\% to detect a 30\% improvement in the detection of significant events. A significant event was defined as a relapse, progression, or a serious adverse event. The study covered the follow-up period after completion of first-line treatment or relapse (24 months). Eligible patients were aged 18 years and older and had lymphoma at a high risk of relapse. In the experimental arm, patients received a 16-symptom questionnaire by email every 2 weeks. An email alert was sent to the medical team based on a predefined algorithm. The primary objective was assessed after the inclusion of the 40th patient. The study was continued for the duration of the analysis. Results: A total of 52 patients were included between July 12, 2017, and April 7, 2020, at 11 centers in France, with 27 in the experimental arm and 25 in the control arm. The median follow-up was 21.3 (range 1.3?25.6) months, and 121 events were reported during the study period. Most events occurred in the experimental arm (83/119, 69.7\%) compared with 30.2\% (36/119) in the control arm. A median number of 3.5 (range 1-8) events per patient occurred in the experimental arm, and 1.8 (range 1-6) occurred in the control arm (P=.01). Progression and infection were the most frequently reported events. Further, 19 patients relapsed during follow-up: 6 in the experimental arm and 13 in the control arm (P<.001), with a median follow-up of 7.7 (range 2.8?20.6) months and 6.7 (range 1.9?16.4) months (P=.94), respectively. Statistical analysis was conducted after including the 40th patient, which showed no superiority of the experimental arm over the control arm. The study was therefore stopped after the 52nd patient was enrolled. Conclusions: The primary objective was not reached; however, patient-reported outcome measures remain essential for detecting adverse events in patients with cancer, and the electronic monitoring method needs to demonstrate its effectiveness and comply with international safety guidelines. Trial Registration: ClinicalTrials.gov NCT03154710; https://clinicaltrials.gov/ct2/show/NCT03154710 ",
doi="10.2196/65960",
url="https://cancer.jmir.org/2025/1/e65960"
}


@Article{info:doi/10.2196/56718,
author="Dunston, R. Emily
and Oza, Sonal
and Bai, Yang
and Newton, Maria
and Podlog, Leslie
and Larson, Kish
and Walker, Darren
and Zingg, W. Rebecca
and Hansen, A. Pamela
and Coletta, M. Adriana",
title="Preliminary Effectiveness of a Telehealth-Delivered Exercise Program in Older Adults Living With and Beyond Cancer: Retrospective Study",
journal="JMIR Cancer",
year="2025",
month="Jan",
day="13",
volume="11",
pages="e56718",
keywords="physical activity",
keywords="physical function",
keywords="telerehabilitation",
keywords="remote exercise",
keywords="digital health",
keywords="cancer survivors",
keywords="older adults",
keywords="smartphone",
abstract="Background: Exercise can attenuate the deleterious combined effects of cancer treatment and aging among older adults with cancer, yet exercise participation is low. Telehealth exercise may improve exercise engagement by decreasing time and transportation barriers; however, the utility of telehealth exercise among older adults with cancer is not well established. Objective: We aimed to evaluate the preliminary effectiveness of a one-on-one, supervised telehealth exercise program on physical function, muscular endurance, balance, and flexibility among older adults with cancer. Methods: In this retrospective study, we analyzed electronic health record data collected from the Personal Optimism With Exercise Recovery clinical exercise program delivered via telehealth among older adults with cancer (?65 y) who completed a virtual initial program telehealth assessment between March 2020 and December 2021. The virtual initial assessment included the following measures: 30-second chair stand test, 30-second maximum push-up test, 2-minute standing march, single leg stance, plank, chair sit and reach, shoulder range of motion, and the clock test. All baseline measures were repeated after 12-weeks of telehealth exercise. Change scores were calculated for all assessments and compared to minimal clinically important difference (MCID) values for assessments with published MCIDs. Paired samples t tests (2-tailed) were conducted to determine change in assessment outcomes. Results: Older adults with cancer who chose to participate in the telehealth exercise program (N=68) were 71.8 (SD 5.3) years of age on average (range 65?92 y). The 3 most common cancer types in this sample were breast (n=13), prostate (n=13), and multiple myeloma (n=8). All cancer stages were represented in this sample with stage II (n=16, 23.5\%) and III (n=18, 26.5\%) being the most common. A follow-up telehealth assessment was completed by 29.4\% (n=20) of older adults with cancer. Among those who completed a follow-up telehealth assessment, there were significant increases in the 30-second chair stand (n=19; mean change +2.00 repetitions, 95\% CI 0.12 to 3.88) and 30-second maximum push-up scores (n=20; mean change +2.85 repetitions, 95\% CI 1.60 to 4.11). There were no significant differences for the 2-minute standing march, plank, single leg stance, sit and reach, shoulder mobility, or clock test (P>.05). Nine (47.3\%) older adults with cancer had a change in 30-second chair stand scores greater than the MCID of 2 repetitions. Conclusions: Our findings suggest a one-on-one, supervised telehealth exercise program may positively influence measures of physical function, muscular endurance, balance, and flexibility among older adults with cancer, but more adequately powered trials are needed to confirm these findings. ",
doi="10.2196/56718",
url="https://cancer.jmir.org/2025/1/e56718"
}


@Article{info:doi/10.2196/59478,
author="Marker, J. Ryan
and Kittelson, J. Andrew
and Scorsone, J. Jared
and Moran, A. Ian
and Quindry, C. John
and Leach, J. Heather",
title="A Novel Telehealth Exercise Program Designed for Rural Survivors of Cancer With Cancer-Related Fatigue: Single-Arm Feasibility Trial",
journal="JMIR Cancer",
year="2025",
month="Jan",
day="10",
volume="11",
pages="e59478",
keywords="cancer-related fatigue",
keywords="telehealth",
keywords="physical activity",
keywords="survivorship",
keywords="digital health",
keywords="lifestyle intervention",
keywords="videoconference",
keywords="symptom burden",
keywords="symptom monitoring",
keywords="geographic disparities",
keywords="mHealth",
abstract="Background: Exercise interventions are among the best-known interventions for cancer-related fatigue (CRF). Rural survivors of cancer, however, report specific barriers to engaging in exercise programs and lack overall access to effective programs. Objective: The purpose of this investigation was to assess the feasibility of a novel telehealth exercise program designed specifically for rural survivors of cancer with CRF. Methods: A single-arm clinical trial of the BfitBwell Telehealth Program was performed. Based on an established clinical program, this adapted 12-week program addressed barriers previously reported by rural survivors by providing synchronous videoconference exercise sessions (2 per program), asynchronous exercise sessions using a personal training smartphone or internet app (3-5 per week), and regular symptom (CRF) monitoring using automated emailed surveys (every 2 weeks). Personalized exercise prescriptions containing aerobic and resistance activities were implemented by cancer exercise specialists. Symptom-triggered synchronous sessions were initiated for participants failing to improve in CRF, as identified by a reference chart of CRF improvements observed during a supervised exercise program. Eligible participants were adult survivors of any cancer diagnosis who had completed treatment with curative intent in the past 12 months or had no planned changes in treatment for the duration of the study, lived in a rural area, and were currently experiencing CRF. Feasibility was assessed by objective measures of recruitment, data collection, intervention acceptability and suitability, and preliminary evaluations of participant responses. CRF was the primary clinical outcome (assessed using the Functional Assessment of Chronic Illness Therapy---Fatigue Scale [FACIT-Fatigue]) and was measured before, after, and 6 months after program completion. Results: In total, 19 participants enrolled in the study, 16 initiated the exercise program, and 15 completed the program. A total of 14 participants were recruited through internet advertisements, and the total recruitment rate peaked at 5 participants per month. Participants completed 100\% of initial and final assessments (30 assessments across all participants) and 93\% (70/75 possible surveys across all participants) of emailed surveys and attended 97\% (29/30 possible sessions across all participants) of synchronous exercise sessions. In total, 6 participants initiated symptom-triggered sessions, with 6 of 7 initiated sessions attended. The mean FACIT-Fatigue scores significantly improved (P=.001) by 11.2 (SD 6.8) points following the completion of the program. A total of 13 participants demonstrated at least a minimal clinically important difference in FACIT-Fatigue scores (? +3 points) at this time. FACIT-Fatigue scores did not significantly change from program completion to 6-month follow-up (n=13; mean change --1.1, SD 3.4 points; P=.29). Conclusions: Results from this investigation support the feasibility of the BfitBwell Telehealth Program and a subsequent efficacy trial. Novel program components also provide potential models for improving exercise program efficacy and efficiency through asynchronous exercise prescription and symptom monitoring. Trial Registration: ClinicalTrials.gov NCT04533165; https://clinicaltrials.gov/study/NCT04533165 ",
doi="10.2196/59478",
url="https://cancer.jmir.org/2025/1/e59478"
}


@Article{info:doi/10.2196/58093,
author="Inayama, Yoshihide
and Yamaguchi, Ken
and Mizuno, Kayoko
and Tanaka-Mizuno, Sachiko
and Koike, Ayami
and Higashiyama, Nozomi
and Taki, Mana
and Yamanoi, Koji
and Murakami, Ryusuke
and Hamanishi, Junzo
and Yoshida, Satomi
and Mandai, Masaki
and Kawakami, Koji",
title="Changes in Physical Activity Across Cancer Diagnosis and Treatment Based on Smartphone Step Count Data Linked to a Japanese Claims Database: Retrospective Cohort Study",
journal="JMIR Cancer",
year="2025",
month="Jan",
day="20",
volume="11",
pages="e58093",
keywords="cancer",
keywords="lifelog data",
keywords="physical activity",
keywords="quality of life",
keywords="step count",
keywords="Japanese",
keywords="database",
keywords="smartphone",
keywords="mobile app",
keywords="exercise",
keywords="mobile phone",
abstract="Background: Although physical activity (PA) is recommended for patients with cancer, changes in PA across cancer diagnosis and treatment have not been objectively evaluated. Objective: This study aimed to assess the impact of cancer diagnosis and treatment on PA levels. Methods: This was a retrospective cohort study using a Japanese claims database provided by DeSC Healthcare Inc, in which daily step count data, derived from smartphone pedometers, are linked to the claims data. In this study, we included patients newly diagnosed with cancer, along with those newly diagnosed with diabetes mellitus for reference. We collected data between April 2014 and September 2021 and analyzed them. The observation period spanned from 6 months before diagnosis to 12 months after diagnosis. We applied a generalized additive mixed model with a cubic spline to describe changes in step counts before and after diagnosis. Results: We analyzed the step count data of 326 patients with malignant solid tumors and 1388 patients with diabetes. Patients with cancer exhibited a 9.6\% (95\% CI 7.1\%-12.1\%; P<.001) reduction in step counts from baseline at the start of the diagnosis month, which further deepened to 12.4\% (95\% CI 9.5\%-15.2\%; P<.001) at 3 months and persisted at 7.1\% (95\% CI 4.2\%-10.0\%; P<.001) at 12 months, all relative to baseline. Conversely, in patients with diabetes, step counts remained relatively stable after diagnosis, with a slight upward trend, resulting in a change of +0.6\% (95\% CI --0.6\% to 1.9\%; P=.31) from baseline at 3 months after diagnosis. At 12 months after diagnosis, step counts remained decreased in the nonendoscopic subdiaphragmatic surgery group, with an 18.0\% (95\% CI 9.1\%-26.2\%; P<.001) reduction, whereas step counts returned to baseline in the laparoscopic surgery group (+0.3\%, 95\% CI --6.3\% to 7.5\%; P=.93). Conclusions: The analysis of objective pre- and postdiagnostic step count data provided fundamental information crucial for understanding changes in PA among patients with cancer. While cancer diagnosis and treatment reduced PA, the decline may have already started before diagnosis. The study findings may help tailor exercise recommendations based on lifelog data for patients with cancer in the future. ",
doi="10.2196/58093",
url="https://cancer.jmir.org/2025/1/e58093",
url="http://www.ncbi.nlm.nih.gov/pubmed/39726139"
}


@Article{info:doi/10.2196/64747,
author="Kennedy, Fiona
and Smith, Susan
and Beeken, J. Rebecca
and Buck, Caroline
and Williams, Sarah
and Martin, Charlene
and Lally, Phillippa
and Fisher, Abi",
title="An App-Based Intervention With Behavioral Support to Promote Brisk Walking in People Diagnosed With Breast, Prostate, or Colorectal Cancer (APPROACH): Process Evaluation Study",
journal="JMIR Cancer",
year="2025",
month="Feb",
day="10",
volume="11",
pages="e64747",
keywords="cancer",
keywords="physical activity",
keywords="process evaluation",
keywords="randomized controlled trial",
keywords="intervention",
keywords="app",
keywords="habit",
abstract="Background: The APPROACH pilot study explored the feasibility and acceptability of an app (NHS Active 10) with brief, habit-based, behavioral support calls and print materials intended to increase brisk walking in people diagnosed with cancer. Objective: Following UK Medical Research Council guidelines, this study assessed the implementation of the intervention, examined the mechanisms of impact, and identified contextual factors influencing engagement. Methods: Adults (aged ?18 y) with breast, prostate, or colorectal cancer who reported not meeting the UK guidelines for moderate-to-vigorous physical activity (?150 min/wk) were recruited from a single hospital site in Yorkshire, United Kingdom. They were randomly assigned to the intervention or control (usual care) arm and assessed via quantitative surveys at baseline (time point 0 [T0]) and 3-month follow-up (time point 1 [T1]) and qualitative exit interviews (36/44, 82\%) at T1. The process evaluation included intervention participants only (n=44). Implementation was assessed using data from the T1 questionnaire exploring the use of the intervention components. The perceived usefulness of the app, leaflet, and behavioral support call was rated from 0 to 5. Behavioral support calls were recorded, and the fidelity of delivery of 25 planned behavior change techniques was rated from 0 to 5 using an adapted Dreyfus scale. Mechanisms of impact were identified by examining T0 and T1 scores on the Self-Reported Behavioural Automaticity Index and feedback on the leaflet, app, call, and planner in the T1 questionnaire and qualitative interviews. Contextual factors influencing engagement were identified through qualitative interviews. Results: The implementation of the intervention was successful: 98\% (43/44) of the participants received a behavioral support call, 78\% (32/41) reported reading the leaflet, 95\% (39/41) reported downloading the app, and 83\% (34/41) reported using the planners. The mean perceived usefulness of the app was 4.3 (SD 0.8) in participants still using the app at T1 (n=33). Participants rated the leaflet (mean 3.9, SD 0.6) and the behavioral support call (mean 4.1, SD 1) as useful. The intended behavior change techniques in the behavioral support calls were proficiently delivered (overall mean 4.2, SD 1.2). Mechanisms of impact included habit formation, behavioral monitoring, and support and reassurance from the intervention facilitator. Contextual factors impacting engagement included barriers, such as the impact of cancer and its treatment, and facilitators, such as social support. Conclusions: The APPROACH intervention was successfully implemented and shows promise for increasing brisk walking, potentially through promoting habit formation and enabling self-monitoring. Contextual factors will be important to consider when interpreting outcomes in the larger APPROACH randomized controlled trial. International Registered Report Identifier (IRRID): RR2-10.1186/s40814-022-01028-w ",
doi="10.2196/64747",
url="https://cancer.jmir.org/2025/1/e64747"
}


@Article{info:doi/10.2196/66847,
author="Kochashvili, Mariam
and Guttoo, Parishma
and Sezgin, Emre
and Pai, Ahna
and Bajwa, Rajinder
and Landier, Wendy
and Gerhardt, Cynthia
and Skeens, Micah",
title="Supporting Medication Adherence in Pediatric Patients Undergoing Hematopoietic Stem Cell Transplant Using the BMT4me mHealth App: Mixed Methods Usability Study",
journal="JMIR Cancer",
year="2025",
month="May",
day="29",
volume="11",
pages="e66847",
keywords="mHealth",
keywords="pediatric transplant",
keywords="digital health",
keywords="medication adherence",
keywords="usability",
keywords="hematopoietic stem cell transplant",
keywords="bone marrow transplant",
keywords="pediatric",
keywords="children",
keywords="hematopoietic stem cell",
keywords="HSC",
keywords="smartphone",
keywords="mobile health",
keywords="BMT4me",
keywords="digital health intervention",
keywords="descriptive statistics",
keywords="thematic analysis",
keywords="usability study",
keywords="mixed method",
keywords="social support",
keywords="health outcomes",
keywords="medication management",
keywords="symptom tracking",
keywords="electronic medical records",
keywords="user-friendly",
abstract="Background: Due to multifaceted outpatient regimens, children receiving hematopoietic stem cell transplants (HCTs) are at high risk of medication nonadherence, leading to life-threatening complications. Mobile health (mHealth) interventions have proven effective in improving adherence in various pediatric conditions; however, adherence intervention literature on HCT is limited. Objective: This study aimed to assess the usability of a mHealth intervention (BMT4me) designed to serve as a real-time, personalized tool for medication management or adherence, symptom tracking, and journal keeping. Methods: Following a mixed methods approach, 14 caregivers (n=11, 79\% female; n=10, 71\% White) of children aged 2?18 (mean age 8.51, SD 5.18) years in the acute phase (first 100 d) post-HCT were recruited. Caregivers were asked to use the BMT4me app for 100 days or until weaning of the immunosuppressant medications to measure usability. The System Usability Scale (assessing functionality and acceptability), reaction cards (assessing desirability), caregiver satisfaction (assessing satisfaction) with the app, and semistructured interviews (assessing participant experience using the app and feedback regarding features) were conducted at two time points, at enrollment and study completion. Results: The mean System Usability Scale score was 86.15 (SD 12.81) at enrollment and 73.13 (SD 16.13) at study completion, with most participants reporting the app easy to use and accepable during both time points. At enrollment, 80\% (n=12) of caregivers reported that the app was effective in motivating them to stay on schedule, and 87\% (n=13) indicated they would recommend it to others. At study completion, 75\% (n=6) of caregivers found the app helpful for tracking their child's medication schedule, and 64\% (n=5) would recommend it to others. Caregivers described the app as ``accessible,'' ``useful,'' and ``valuable.'' Qualitative interviews during both time points revealed caregivers' positive reactions to the app, particularly regarding medication reminders, tracking symptoms, and notes features, while also providing suggestions for improvements, such as integrating the BMT4me app with electronic medical records, incorporating educational content, adding fields for recording vital signs, and important phone numbers. Conclusions: The BMT4me app demonstrated promising usability as a mHealth intervention among pediatric patients undergoing HCT. Caregivers considered the app user-friendly and valuable, with positive feedback on its features, such as medication reminders and symptom tracking. Despite minor reported issues with app functionality, the overall acceptance of the app suggests its potential to support families in managing complex treatment. The findings from this study will inform the feasibility of testing in larger randomized controlled trials. Trial Registration: ClinicalTrials.gov NCT04976933; https://clinicaltrials.gov/ct2/show/NCT04976933 International Registered Report Identifier (IRRID): RR2-10.2196/39098 ",
doi="10.2196/66847",
url="https://cancer.jmir.org/2025/1/e66847"
}


@Article{info:doi/10.2196/67108,
author="Lin, Chen Yu
and Hagen, Ryan
and Powers, D. Benjamin
and Dineen, P. Sean
and Milano, Jeanine
and Hume, Emma
and Sprow, Olivia
and Diaz-Carraway, Sophia
and Permuth, B. Jennifer
and Deneve, Jeremiah
and Alishahi Tabriz, Amir
and Turner, Kea",
title="Digital Health Intervention to Reduce Malnutrition Among Individuals With Gastrointestinal Cancer Receiving Cytoreductive Surgery Combined With Hyperthermic Intraperitoneal Chemotherapy: Feasibility, Acceptability, and Usability Trial",
journal="JMIR Cancer",
year="2025",
month="Apr",
day="7",
volume="11",
pages="e67108",
keywords="gastrointestinal cancer",
keywords="peritoneal disease",
keywords="cytoreductive surgery combined with hyperthermic intraperitoneal chemotherapy",
keywords="digital health intervention",
keywords="nutrition",
keywords="feasibility",
abstract="Background: Cytoreductive surgery combined with hyperthermic intraperitoneal chemotherapy (CRS-HIPEC) can improve survival outcomes for individuals with gastrointestinal (GI) cancer and peritoneal disease (PD). Individuals with GI cancer and PD receiving CRS-HIPEC are at increased risk for malnutrition. Despite the increased risk for malnutrition, there has been limited study of nutritional interventions for individuals receiving CRS-HIPEC. Objective: We aimed to test the feasibility, acceptability, and usability of Support Through Remote Observation and Nutrition Guidance (STRONG), a multilevel digital health intervention to improve nutritional management among individuals with GI cancer and PD receiving CRS-HIPEC. We also assessed patient-reported outcomes, including malnutrition risk, health-related quality of life, and weight-related measures. Methods: STRONG is a 12-week digital intervention in which participants received biweekly nutritional counseling with a dietitian, logged food intake using a Fitbit tracker, and reported nutrition-related outcomes. Dietitians received access to a web-based dashboard and remotely monitored patients' reported food intake and nutrition-impact symptoms. Implementation outcomes were assessed against prespecified benchmarks consistent with benchmarks used in prior studies. Changes in patient-reported outcomes at baseline and follow-up were assessed using linear and ordered logistic regressions. Results: Participants (N=10) had a median age of 57.5 (IQR 54-69) years. Feasibility benchmarks were achieved for recruitment (10/17, 59\% vs benchmark: 50\%), study assessment completion (9/10, 90\% vs benchmark: 60\%), dietitian appointment attendance (7/10, 70\% vs benchmark: 60\%), daily food intake logging adherence (6/10, 60\% vs benchmark: 60\%), and participant retention (10/10, 100\% vs benchmark: 60\%). Most participants rated the intervention as acceptable (8/10, 80\% vs benchmark: 70\%) and reported a high level of usability for dietitian services (10/10, 100\%). The benchmark usability for the Fitbit tracker to log food intake was not met. Compared to baseline, participants saw on average a 6.0 point reduction in malnutrition risk score (P=.01), a 20.5 point improvement in general health-related quality of life score (P=.002), and a 5.6 percentage point increase in 1-month weight change (P=.04) at the end of the study. Conclusions: The STRONG intervention demonstrated to be feasible, acceptable, and usable among individuals with GI cancer and PD receiving CRS-HIPEC. A fully powered randomized controlled trial is needed to test the effectiveness of STRONG for reducing malnutrition and improving patient outcomes. Trial Registration: ClinicalTrials.gov NCT05649969; https://clinicaltrials.gov/study/NCT05649969 ",
doi="10.2196/67108",
url="https://cancer.jmir.org/2025/1/e67108"
}


@Article{info:doi/10.2196/66471,
author="Sterba, Katherine
and Graboyes, Evan
and Burris, Jessica
and Scallion, Megan
and Kinder, Hannah
and Olsen, Jama
and Toll, Benjamin
and Armeson, Kent
and Day, Terry
and Chera, Bhishamjit
and Ruggiero, Kenneth",
title="Development of a Mobile App to Support Head and Neck Cancer Caregiving: Mixed Methods Study",
journal="JMIR Cancer",
year="2025",
month="Jun",
day="10",
volume="11",
pages="e66471",
keywords="head and neck cancer",
keywords="cancer survivorship",
keywords="caregiving",
keywords="nutrition",
keywords="mobile health",
keywords="app development",
keywords="mixed methods",
abstract="Background: Survivors with head and neck cancer (HNC) face challenging treatment consequences that can lead to severe disruptions in swallowing and result in weight loss, malnutrition, and feeding tube dependence. Caregivers (family or friends who provide support), therefore, often encounter distressing nutritional caregiving burdens and feel unprepared to provide adequate support at home. Objective: The purpose of this mixed methods study was to develop a mobile support app to support HNC caregiving with an emphasis on nutritional support following treatment. Methods: We assessed perspectives on nutritional recovery challenges and mobile support app preferences in (1) a national panel of oncology dietitians using a web-based cross-sectional survey and (2) survivors with HNC completing treatment within the past 24 months and their nominated caregivers using dyadic semistructured interviews. Descriptive statistics for survey data were synthesized with thematic analysis of interview data to characterize nutrition-related perceptions and intervention preferences; results were integrated, and themes were translated to high-priority main menu domains and subdomains for a mobile app for caregivers. Results: Surveys were completed by dietitians (n=116, 100\%; female n=87, 50\%, with >10 years practice experience). Interviews included survivors with HNC (n=15; 12/15, 80\% male, and 6/15, 40\% with oropharynx cancer) and their caregivers (n=13; 11/13, 85\% female, and 10/13, 77\% spouses). Dietitians, survivors, and caregivers perceived that the majority of nutritional concerns assessed (eg, swallowing, feeding tube management, weight maintenance, and caregiver distress about nutrition) were very or extremely important to caregiving in the 6 months following treatment conclusion. The caregiving tasks rated highest in importance by dietitians included tracking nutritional concerns (n=113, 97\%), working together as a team on nutritional concerns (n=104, 90\%), and making care decisions (n=102, 88\%). Five themes emerged from dyadic interviews, including types of nutritional challenges faced, that competing symptoms were difficult to separate from nutritional challenges, the emotional challenges related to nutrition and recovery, the diverse set of medical and support tasks taken on by caregivers, and information and resource needs in caregivers. Qualitative interview and survey themes guided the content of the Healthy Eating and Recovery Together (HEART) app with an intake tracker and sections for nutrition recovery support, other competing caregiving tips, peer support, and caregiver self-care. Conclusions: Results pinpointed optimal content for a mobile app for caregivers of individuals with HNC and support the acceptability of implementing the HEART app following HNC treatment. ",
doi="10.2196/66471",
url="https://cancer.jmir.org/2025/1/e66471"
}


@Article{info:doi/10.2196/53539,
author="Crafoord, Marie-Ther{\'e}se
and Ekstrand, Joakim
and Sundberg, Kay
and Nilsson, I. Marie
and Fjell, Maria
and Langius-Ekl{\"o}f, Ann",
title="Mobile Electronic Patient-Reported Outcomes and Interactive Support During Breast and Prostate Cancer Treatment: Health Economic Evaluation From Two Randomized Controlled Trials",
journal="JMIR Cancer",
year="2025",
month="Mar",
day="11",
volume="11",
pages="e53539",
keywords="cost-effectiveness",
keywords="ePRO",
keywords="mHealth",
keywords="disease monitoring",
keywords="cancer",
keywords="RCT",
keywords="randomized controlled trial",
keywords="controlled trials",
keywords="digital intervention",
keywords="patient-reported outcomes",
keywords="management",
keywords="payers' perspective",
keywords="health care costs",
keywords="apps",
keywords="prostate cancer",
keywords="breast cancer",
abstract="Background: Digital interventions for supportive care during cancer treatment incorporating electronic patient-reported outcomes (ePROs) can enhance early detection of symptoms and facilitate timely symptom management. However, economic evaluations are needed. Objective: This study aims to conduct a cost-utility analysis of an app for ePRO and interactive support from the perspective of the payer (Region Stockholm Health Care Organization) and to explore its impact on patient health care utilization and costs. Methods: Two open-label randomized controlled trials (RCTs) were conducted, including patients undergoing neoadjuvant chemotherapy for breast cancer (B-RCT; N=149) and radiotherapy for prostate cancer (P-RCT; N=150), recruited from oncology clinics at 2 university hospitals in Stockholm, Sweden. EORTC QLQ-C30 scores were mapped to EQ-5D-3L to calculate quality-adjusted life years (QALYs). Intervention and implementation costs and health care costs, obtained from an administrative database, were used to calculate incremental cost-effectiveness ratios (ICERs) in 3 ways: including all health care costs (ICERa), excluding nonacute health care costs (ICERb), and excluding health care costs altogether (ICERc). Nonparametric bootstrapping was used to explore ICER uncertainty. Health care costs were analyzed by classifying them as disease-related or acute. Results: In both RCT intervention groups, fewer QALYs were lost compared with the control group (P<.001). In the B-RCT, the mean intervention cost was {\texteuro}92 (SD {\texteuro}2; {\texteuro}1=US \$1.03). The mean cost for the intervention and all health care was {\texteuro}36,882 (SD {\texteuro}1032) in the intervention group and {\texteuro}35,427 (SD {\texteuro}959) in the control group (P<.001), with an ICERa of {\texteuro}202,368 (95\% CI {\texteuro}152,008-{\texteuro}252,728). The mean cost for the intervention and acute health care was {\texteuro}3585 (SD {\texteuro}480) in the intervention group and {\texteuro}3235 (SD {\texteuro}494) in the control group (P<.001). ICERb was {\texteuro}49,903 (95\% CI {\texteuro}37,049-{\texteuro}62,758) and ICERc was {\texteuro}13,213 (95\% CI {\texteuro}11,145-{\texteuro}15,281); 22 out of 74 (30\%) intervention group patients and 24 out of 75 (32\%) of the control group patients required acute inpatient care for fever. In the P-RCT, the mean intervention cost was {\texteuro}43 (SD {\texteuro}0.2). The mean cost for the intervention and all health care was {\texteuro}3419 (SD {\texteuro}739) in the intervention group and {\texteuro}3537 (SD {\texteuro}689) in the control group (P<.001), with an ICERa of --{\texteuro}1,092,136 (95\% CI --{\texteuro}3,274,774 to {\texteuro}1,090,502). The mean cost for the intervention and acute health care was {\texteuro}1219 (SD {\texteuro}593) in the intervention group and {\texteuro}802 (SD {\texteuro}281) in the control group (P<.001). ICERb was {\texteuro}745,987 (95\% CI --{\texteuro}247,317 to {\texteuro}1,739,292) and ICERc was {\texteuro}13,118 (95\% CI --68,468 to {\texteuro}94,704). As many as 10 out of the 75 (13\%) intervention group patients had acute inpatient care, with the most common symptom being dyspnea, while 9 out of the 75 (12\%) control group patients had acute inpatient care, with the most common symptom being urinary tract infection. Conclusions: ePRO and interactive support via an app generated a small improvement in QALYs at a low intervention cost and may be cost-effective, depending on the costs considered. Considerable variability in patient health care costs introduced uncertainty around the estimates, preventing a robust determination of cost-effectiveness. Larger studies examining cost-effectiveness from a societal perspective are needed. The study provides valuable insights into acute health care utilization during cancer treatment. Trial Registration: ClinicalTrials.gov NCT02479607; https://clinicaltrials.gov/ct2/show/NCT02479607, ClinicalTrials.gov NCT02477137; https://clinicaltrials.gov/ct2/show/NCT02477137 International Registered Report Identifier (IRRID): RR2-10.1186/s12885-017-3450-y ",
doi="10.2196/53539",
url="https://cancer.jmir.org/2025/1/e53539"
}


@Article{info:doi/10.2196/59298,
author="Spotnitz, Matthew
and Giannini, John
and Ostchega, Yechiam
and Goff, L. Stephanie
and Anandan, Priya Lakshmi
and Clark, Emily
and Litwin, R. Tamara
and Berman, Lew",
title="Assessing the Data Quality Dimensions of Partial and Complete Mastectomy Cohorts in the All of Us Research Program: Cross-Sectional Study",
journal="JMIR Cancer",
year="2025",
month="Mar",
day="11",
volume="11",
pages="e59298",
keywords="data quality",
keywords="electronic health record",
keywords="breast cancer",
keywords="breast-conserving surgery",
keywords="total mastectomy",
keywords="modified radical mastectomy",
keywords="public health informatics",
keywords="cohort",
keywords="assessment",
keywords="women",
keywords="United States",
keywords="American",
keywords="nonmetastatic disease",
keywords="treatment",
keywords="breast cancer surgery",
keywords="real-world evidence",
keywords="data",
keywords="mastectomy",
keywords="female",
keywords="data quality framework",
keywords="therapy",
abstract="Background: Breast cancer is prevalent among females in the United States. Nonmetastatic disease is treated by partial or complete mastectomy procedures. However, the rates of those procedures vary across practices. Generating real-world evidence on breast cancer surgery could lead to improved and consistent practices. We investigated the quality of data from the All of Us Research Program, which is a precision medicine initiative that collected real-world electronic health care data from different sites in the United States both retrospectively and prospectively to participant enrollment. Objective: The paper aims to determine whether All of Us data are fit for use in generating real-world evidence on mastectomy procedures. Methods: Our mastectomy phenotype consisted of adult female participants who had CPT4 (Current Procedural Terminology 4), ICD-9 (International Classification of Diseases, Ninth Revision) procedure, or SNOMED (Systematized Nomenclature of Medicine) codes for a partial or complete mastectomy procedure that mapped to Observational Medical Outcomes Partnership Common Data Model concepts. We evaluated the phenotype with a data quality dimensions (DQD) framework that consisted of 5 elements: conformance, completeness, concordance, plausibility, and temporality. Also, we applied a previously developed DQD checklist to evaluate concept selection, internal verification, and external validation for each dimension. We compared the DQD of our cohort to a control group of adult women who did not have a mastectomy procedure. Our subgroup analysis compared partial to complete mastectomy procedure phenotypes. Results: There were 4175 female participants aged 18 years or older in the partial or complete mastectomy cohort, and 168,226 participants in the control cohort. The geospatial distribution of our cohort varied across states. For example, our cohort consisted of 835 (20\%) participants from Massachusetts, but multiple other states contributed fewer than 20 participants. We compared the sociodemographic characteristics of the partial (n=2607) and complete (n=1568) mastectomy subgroups. Those groups differed in the distribution of age at procedure (P<.001), education (P=.02), and income (P=.03) levels, as per $\chi$2 analysis. A total of 367 (9.9\%) participants in our cohort had overlapping CPT4 and SNOMED codes for a mastectomy, and 63 (1.5\%) had overlapping ICD-9 procedure and SNOMED codes. The prevalence of breast cancer--related concepts was higher in our cohort compared to the control group (P<.001). In both the partial and complete mastectomy subgroups, the correlations among concepts were consistent with the clinical management of breast cancer. The median time between biopsy and mastectomy was 5.5 (IQR 3.5-11.2) weeks. Although we did not have external benchmark comparisons, we were able to evaluate concept selection and internal verification for all domains. Conclusions: Our data quality framework was implemented successfully on a mastectomy phenotype. Our systematic approach identified data missingness. Moreover, the framework allowed us to differentiate breast-conserving therapy and complete mastectomy subgroups in the All of Us data. ",
doi="10.2196/59298",
url="https://cancer.jmir.org/2025/1/e59298"
}


@Article{info:doi/10.2196/52886,
author="Spiegel, Y. Daphna
and Friesner, D. Isabel
and Zhang, William
and Zack, Travis
and Yan, Gianna
and Willcox, Julia
and Prionas, Nicolas
and Singer, Lisa
and Park, Catherine
and Hong, C. Julian",
title="Exploring the Social Media Discussion of Breast Cancer Treatment Choices: Quantitative Natural Language Processing Study",
journal="JMIR Cancer",
year="2025",
month="Jan",
day="28",
volume="11",
pages="e52886",
keywords="breast cancer",
keywords="social media",
keywords="patient decision-making",
keywords="natural language processing",
keywords="breast conservation",
keywords="mastectomy",
abstract="Background: Early-stage breast cancer has the complex challenge of carrying a favorable prognosis with multiple treatment options, including breast-conserving surgery (BCS) or mastectomy. Social media is increasingly used as a source of information and as a decision tool for patients, and awareness of these conversations is important for patient counseling. Objective: The goal of this study was to compare sentiments and associated emotions in social media discussions surrounding BCS and mastectomy using natural language processing (NLP). Methods: Reddit posts and comments from the Reddit subreddit r/breastcancer and associated metadata were collected using pushshift.io. Overall, 105,231 paragraphs across 59,416 posts and comments from 2011 to 2021 were collected and analyzed. Paragraphs were processed through the Apache Clinical Text Analysis Knowledge Extraction System and identified as discussing BCS or mastectomy based on physician-defined Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) concepts. Paragraphs were analyzed with a VADER (Valence Aware Dictionary for Sentiment Reasoning) compound sentiment score (ranging from ?1 to 1, corresponding to negativity or positivity) and GoEmotions scores (0?1) corresponding to the intensity of 27 different emotions and neutrality. Results: Of the 105,231 paragraphs, there were 7306 (6.94\% of those analyzed) paragraphs mentioning BCS and mastectomy (2729 and 5476, respectively). Discussion of both increased over time, with BCS outpacing mastectomy. The median sentiment score for all discussions analyzed in aggregate became more positive over time. In specific analyses by topic, positive sentiments for discussions with mastectomy mentions increased over time; however, discussions with BCS-specific mentions did not show a similar trend and remained overall neutral. Compared to BCS, conversations about mastectomy tended to have more positive sentiments. The most commonly identified emotions included neutrality, gratitude, caring, approval, and optimism. Anger, annoyance, disappointment, disgust, and joy increased for BCS over time. Conclusions: Patients are increasingly participating in breast cancer therapy discussions with a web-based community. While discussions surrounding mastectomy became increasingly positive, BCS discussions did not show the same trend. This mirrors national clinical trends in the United States, with the increasing use of mastectomy over BCS in early-stage breast cancer. Recognizing sentiments and emotions surrounding the decision-making process can facilitate patient-centric and emotionally sensitive treatment recommendations. ",
doi="10.2196/52886",
url="https://cancer.jmir.org/2025/1/e52886"
}


@Article{info:doi/10.2196/59882,
author="Hashtarkhani, Soheil
and Zhou, Yiwang
and Kumsa, Asefa Fekede
and White-Means, Shelley
and Schwartz, L. David
and Shaban-Nejad, Arash",
title="Analyzing Geospatial and Socioeconomic Disparities in Breast Cancer Screening Among Populations in the United States: Machine Learning Approach",
journal="JMIR Cancer",
year="2025",
month="Jan",
day="16",
volume="11",
pages="e59882",
keywords="mammography",
keywords="breast neoplasms",
keywords="social determinants of health",
keywords="geographic information systems",
keywords="machine learning",
abstract="Background: Breast cancer screening plays a pivotal role in early detection and subsequent effective management of the disease, impacting patient outcomes and survival rates. Objective: This study aims to assess breast cancer screening rates nationwide in the United States and investigate the impact of social determinants of health on these screening rates. Methods: Data on mammography screening at the census tract level for 2018 and 2020 were collected from the Behavioral Risk Factor Surveillance System. We developed a large-scale dataset of social determinants of health, comprising 13 variables for 72,337 census tracts. Spatial analysis employing Getis-Ord Gi statistics was used to identify clusters of high and low breast cancer screening rates. To evaluate the influence of these social determinants, we implemented a random forest model, with the aim of comparing its performance to linear regression and support vector machine models. The models were evaluated using R2 and root mean squared error metrics. Shapley Additive Explanations values were subsequently used to assess the significance of variables and direction of their influence. Results: Geospatial analysis revealed elevated screening rates in the eastern and northern United States, while central and midwestern regions exhibited lower rates. The random forest model demonstrated superior performance, with an R2=64.53 and root mean squared error of 2.06, compared to linear regression and support vector machine models. Shapley Additive Explanations values indicated that the percentage of the Black population, the number of mammography facilities within a 10-mile radius, and the percentage of the population with at least a bachelor's degree were the most influential variables, all positively associated with mammography screening rates. Conclusions: These findings underscore the significance of social determinants and the accessibility of mammography services in explaining the variability of breast cancer screening rates in the United States, emphasizing the need for targeted policy interventions in areas with relatively lower screening rates. ",
doi="10.2196/59882",
url="https://cancer.jmir.org/2025/1/e59882"
}


@Article{info:doi/10.2196/64020,
author="Takahashi, Noriaki
and Nakao, Mutsuhiro
and Nakayama, Tomio
and Yamazaki, Tsutomu",
title="Breast Cancer Screening Participation and Internet Search Activity in a Japanese Population: Decade-Long Time-Series Study",
journal="JMIR Cancer",
year="2025",
month="Mar",
day="4",
volume="11",
pages="e64020",
keywords="breast cancer",
keywords="cancer screening",
keywords="internet use",
keywords="mass media",
keywords="public health surveillance",
keywords="health belief model",
keywords="mammography",
keywords="awareness",
keywords="Japanese",
keywords="Google",
abstract="Background: Breast cancer is a major health concern in various countries. Routine mammography screening has been shown to reduce breast cancer mortality, and Japan has set national targets to improve screening participation and increase public attention. However, collecting nationwide data on public attention and activity is not easy. Google Trends can reveal changes in societal interest, yet there are no reports on the relationship between internet search volume and nationwide participation rates in Japan. Objective: This study aims to reveal and discuss the relationship between public awareness and actual behavior in breast cancer screening by examining trends in internet search volume for the keyword ``breast cancer screening'' and participation rates over a decade-long period. Methods: This time-series study evaluated the association between internet search volume and breast cancer screening participation behavior among women aged 60?69 years in Japan from 2009 to 2019. Relative search volume (RSV) data for the search term ``breast cancer screening (nyuugan-kenshin)'' were extracted from Google Trends as internet search volume. Breast cancer screening and further assessment participation rates were based on government municipal screening data. Joinpoint regression analyses were conducted with weighted BIC to evaluate the time trends. An ethics review was not required because all data were open. Results: The RSV for ``breast cancer screening (nyuugan-kenshin)'' peaked in June 2017 (100) and showed clear spikes in June 2016 (94), September (69), and October (77) 2015. No RSVs above 60 were observed except around these three specific periods, and the average RSV for the entire period was 30.7 (SD 16.2). Two statistically significant joinpoints were detected, rising in December 2013 and falling in June 2017. Screening participation rates showed a temporary increase in 2015 in a slowly decreasing trend, and no joinpoints were detected. Further assessment participation rates showed a temporary spike in 2015 in the middle of an increasing trend, with a statistically significant point of slowing increase detected in 2015. Post hoc manual searches revealed that Japanese celebrities' breast cancer diagnoses were announced on the relevant dates, and many Japanese media reports were found. Conclusions: This study found a notable association between internet search activity and celebrity cancer media reports and a temporal association with screening participation in breast cancer screening in Japan. Celebrity cancer media reports triggered internet searches for cancer screening, but this did not lead to long-term changes in screening participation behavior. This finding suggests what information needs to be provided to citizens to encourage participation in screening. ",
doi="10.2196/64020",
url="https://cancer.jmir.org/2025/1/e64020"
}


@Article{info:doi/10.2196/67902,
author="Zhang, Kehe
and Taylor, M. Madison
and Hunyadi, Jocelyn
and Doan, Q. Hung
and Adamson, S. Adewole
and Miller, Paige
and Nelson, C. Kelly
and Bauer, Cici",
title="Examining Demographic, Geographic, and Temporal Patterns of Melanoma Incidence in Texas From 2000 to 2018: Retrospective Study",
journal="JMIR Cancer",
year="2025",
month="May",
day="2",
volume="11",
pages="e67902",
keywords="melanoma incidence",
keywords="melanoma screening",
keywords="geographic disparity",
keywords="geospatial analysis",
keywords="joinpoint regression",
keywords="demographic variation",
keywords="temporal trend analysis",
keywords="stage at diagnosis",
abstract="Background: Melanoma currently ranks as the fifth leading cancer diagnosis and is projected to become the second most common cancer in the United States by 2040. Melanoma detected at earlier stages may be treated with less-risky and less-costly therapeutic options. Objective: This study aims to analyze temporal and spatial trends in melanoma incidence by stage at diagnosis (overall, early, and late) in Texas from 2000 to 2018, focusing on demographic and geographic variations to identify high-risk populations and regions for targeted prevention efforts. Methods: We used melanoma incidence data from all 254 Texas counties from the Texas Cancer Registry (TCR) from 2000 to 2018, aggregated by county and year. Among these, 250 counties reported melanoma cases during the period. Counties with no cases reported in a certain year were treated as having no cases. Melanoma cases were classified by SEER Summary Stage and stratified by the following four key covariates: age, sex, race and ethnicity, and stage at diagnosis. Incidence rates (IRs) were calculated per 100,000 population, and temporal trends were analyzed using joinpoint regression to determine average annual percentage changes (AAPCs) with 95\% CIs for the whole time period (2000?2018), the most recent 10-year period (2009?2018), and the most recent 5-year period (2014?2018). Heat map visualizations were developed to assess temporal trends by patient age, year of diagnosis, stage at diagnosis, sex, and race and ethnicity. Spatial cluster analysis was conducted using Getis-Ord Gi* statistics to identify county-level geographic clusters of high and low melanoma incidence by stage at diagnosis. Results: A total of 82,462 melanoma cases were recorded, of which 74.7\% (n=61,588) were early stage, 11.3\% (n=9,352) were late stage, and 14\% (n=11,522) were of unknown stage. Most cases were identified as males and non-Hispanic White individuals. Melanoma IRs increased from 2000 to 2018, particularly among older adults (60+ years; AAPC range 1.20\%-1.84\%; all P values were <.001), males (AAPC 1.59\%; P<.001), and non-Hispanic White individuals (AAPC of 3.24\% for early stage and 2.38\% for late stage; P<.001 for early stage and P = .03 for late state). Early-stage diagnoses increased while the rates of late-stage diagnoses remained stable for the overall population. The spatial analysis showed that urban areas had higher early-stage incidence rates (P=.06), whereas rural areas showed higher late-stage incidence rates (P=.05), indicating possible geographic-based differences in access to dermatologic care. Conclusions: Melanoma incidence in Texas increased over the study time period, with the most-at-risk populations being non-Hispanic White individuals, males, and individuals aged 50 years and older. The stable rates of late-stage melanoma among racial and ethnic minority populations and rural populations highlight potential differences in access to diagnostic care. Future prevention efforts may benefit from increasing access to dermatologic care in areas with higher rates of late-stage melanoma at diagnosis. ",
doi="10.2196/67902",
url="https://cancer.jmir.org/2025/1/e67902"
}


@Article{info:doi/10.2196/53328,
author="Zippi, D. Zachary
and Cortopassi, O. Isabel
and Grage, A. Rolf
and Johnson, M. Elizabeth
and McCann, R. Matthew
and Mergo, J. Patricia
and Sonavane, K. Sushil
and Stowell, T. Justin
and Little, P. Brent",
title="Assessing Public Interest in Mammography, Computed Tomography Lung Cancer Screening, and Computed Tomography Colonography Screening Examinations Using Internet Search Data: Cross-Sectional Study",
journal="JMIR Cancer",
year="2025",
month="Mar",
day="11",
volume="11",
pages="e53328",
keywords="lung cancer",
keywords="lung cancer screening",
keywords="breast cancer",
keywords="mammography",
keywords="colon cancer",
keywords="CT colonography",
keywords="Google search",
keywords="internet",
keywords="Google Trends",
keywords="imaging-based",
keywords="cancer screening",
keywords="search data",
keywords="noninvasive",
keywords="cancer",
keywords="CT",
keywords="online",
keywords="public awareness",
keywords="big data",
keywords="analytics",
keywords="patient education",
keywords="screening uptake",
abstract="Background: The noninvasive imaging examinations of mammography (MG), low-dose computed tomography (CT) for lung cancer screening (LCS), and CT colonography (CTC) play important roles in screening for the most common cancer types. Internet search data can be used to gauge public interest in screening techniques, assess common screening-related questions and concerns, and formulate public awareness strategies. Objective: This study aims to compare historical Google search volumes for MG, LCS, and CTC and to determine the most common search topics. Methods: Google Trends data were used to quantify relative Google search frequencies for these imaging screening modalities over the last 2 decades. A commercial search engine tracking product (keywordtool.io) was used to assess the content of related Google queries over the year from May 1, 2022, to April 30, 2023, and 2 authors used an iterative process to agree upon a list of thematic categories for these queries. Queries with at least 10 monthly instances were independently assigned to the most appropriate category by the 2 authors, with disagreements resolved by consensus. Results: The mean 20-year relative search volume for MG was approximately 10-fold higher than for LCS and 25-fold higher than for CTC. Search volumes for LCS have trended upward since 2011. The most common topics of MG-related searches included nearby screening locations (60,850/253,810, 24\%) and inquiries about procedural discomfort (28,970/253,810, 11\%). Most common LCS-related searches included CT-specific inquiries (5380/11,150, 48\%) or general inquiries (1790/11,150, 16\%), use of artificial intelligence or deep learning (1210/11,150, 11\%), and eligibility criteria (1020/11,150, 9\%). For CTC, the most common searches were CT-specific inquiries (1800/5590, 32\%) or procedural details (1380/5590, 25\%). Conclusions: Over the past 2 decades, Google search volumes have been significantly higher for MG than for either LCS or CTC, although search volumes for LCS have trended upward since 2011. Knowledge of public interest and queries related to imaging-based screening techniques may help guide public awareness efforts. ",
doi="10.2196/53328",
url="https://cancer.jmir.org/2025/1/e53328"
}


@Article{info:doi/10.2196/66655,
author="Cui, Zixuan
and Suo, Chen
and Zhao, Yidan
and Wang, Shuo
and Zhao, Ming
and Chen, Ruilin
and Lu, Linyao
and Zhang, Tiejun
and Chen, Xingdong",
title="Spatiotemporal Correlation Analysis for the Incidence of Esophageal and Gastric Cancer From 2010 to 2019: Ecological Study",
journal="JMIR Cancer",
year="2025",
month="Jan",
day="29",
volume="11",
pages="e66655",
keywords="spatiotemporal analysis",
keywords="spatiotemporal correlation",
keywords="esophageal cancer",
keywords="gastric cancer",
keywords="cancer",
keywords="global burden of disease",
keywords="GBD",
keywords="average annual percentage change",
keywords="incidence",
keywords="epidemiology",
abstract="Background: Esophageal and gastric cancer were among the top 10 most common cancers worldwide. In addition, sex-specific differences were observed in the incidence. Due to their anatomic proximity, the 2 cancers have both different but also shared risk factors and epidemiological features. Exploring the potential correlated incidence pattern of them, holds significant importance in providing clues in the etiology and preventive strategies. Objective: This study aims to explore the spatiotemporal correlation between the incidence patterns of esophageal and gastric cancer in 204 countries and territories from 2010 to 2019 so that prevention and control strategies can be more effective. Methods: The data of esophageal and gastric cancer were sourced from the Global Burden of Disease (GBD). Spatial autocorrelation analysis using Moran I in ArcGIS 10.8 (Esri) was performed to determine spatial clustering of each cancer incidence. We classified different risk areas based on the risk ratio (RR) of the 2 cancers in various countries to the global, and the correlation between their RR was evaluated using Pearson correlation coefficient. Temporal trends were quantified by calculating the average annual percent change (AAPC), and the correlation between the temporal trends of both cancers was evaluated using Pearson correlation coefficients. Results: In 2019, among 204 countries and territories, the age-standardized incidence rates (ASIR) of esophageal cancer ranged from 0.91 (95\% CI 0.65-1.58) to 24.53 (95\% CI 18.74-32.51), and the ASIR of gastric cancer ranged from 3.28 (95\% CI 2.67-3.91) to 43.70 (95\% CI 34.29-55.10). Malawi was identified as the highest risk for esophageal cancer (male RR=3.27; female RR=5.19) and low risk for gastric cancer (male RR=0.21; female RR=0.23) in both sexes. Spatial autocorrelation analysis revealed significant spatial clustering of the incidence for both cancers (Moran I>0.20 and P<.001). A positive correlation between the risk of esophageal and gastric cancer was observed in males (r=0.25, P<.001). The ASIR of both cancers showed a decreasing trend globally. The ASIR for esophageal and gastric cancer showed an AAPC of ?1.43 (95\% CI ?1.58 to ?1.27) and ?1.76 (95\% CI ?2.08 to ?1.43) in males, and ?1.93 (95\% CI ?2.11 to ?1.75) and ?1.79 (95\% CI ?2.13 to ?1.46) in females. In addition, a positive correlation between the temporal trends in ASIR for both cancers was observed at the global level across sexes (male r=0.98; female r=0.98). Conclusions: Our study shows that there was a significant spatial clustering of the incidence for esophageal and gastric cancer and a positive correlation between the risk of both cancers across countries was observed in males. In addition, a codescending incidence trend between both cancers was observed at the global level. ",
doi="10.2196/66655",
url="https://cancer.jmir.org/2025/1/e66655"
}


@Article{info:doi/10.2196/57414,
author="Lin, Shuangquan
and Duan, Lingxing
and Xu, Xiangda
and Cao, Haichao
and Lu, Xiongbing
and Wen, Xi
and Wei, Shanzun",
title="Analyzing Online Search Trends for Kidney, Prostate, and Bladder Cancers in China: Infodemiology Study Using Baidu Search Data (2011-2023)",
journal="JMIR Cancer",
year="2025",
month="Mar",
day="14",
volume="11",
pages="e57414",
keywords="bladder cancer",
keywords="kidney cancer",
keywords="prostate cancer",
keywords="Baidu Index",
keywords="infodemiology",
keywords="public interest",
keywords="patients' concern",
abstract="Background: Cancers of the bladder, kidney, and prostate are the 3 major genitourinary cancers that significantly contribute to the global burden of disease (GBD) and continue to show increasing rates of morbidity and mortality worldwide. In mainland China, understanding the cancer burden on patients and their families is crucial; however, public awareness and concerns about these cancers, particularly from the patient's perspective, remain predominantly focused on financial costs. A more comprehensive exploration of their needs and concerns has yet to be fully addressed. Objective: This study aims to analyze trends in online searches and user information--seeking behaviors related to bladder, kidney, and prostate cancers---encompassing descriptive terms (eg, ``bladder cancer,'' ``kidney cancer,'' ``prostate cancer'') as well as related synonyms and variations---on both national and regional scales. This study leverages data from mainland China's leading search engine to explore the implications of these search patterns for addressing user needs and improving health management. Methods: The study analyzed Baidu Index search trends for bladder, kidney, and prostate cancers (from January 2011 to August 2023) at national and provincial levels. Search volume data were analyzed using the joinpoint regression model to calculate annual percentage changes (APCs) and average APCs (AAPCs), identifying shifts in public interest. User demand was assessed by categorizing the top 10 related terms weekly into 13 predefined topics, including diagnosis, treatment, and traditional Chinese medicine. Data visualization and statistical analyses were performed using Prism 9. Results revealed keyword trends, demographic distributions, and public information needs, offering insights into health communication and management strategies based on online information-seeking behavior. Results: Three cancer topics were analyzed using 39 search keywords, yielding a total Baidu Search Index (BSI) of 43,643,453. From 2011 to 2015, the overall APC was 15.2\% (P<.05), followed by --2.8\% from 2015 to 2021, and 8.9\% from 2021 to 2023, with an AAPC of 4.9\%. Bladder, kidney, and prostate cancers exhibited AAPCs of 2.8\%, 3.9\%, and 6.8\%, respectively (P<.05). The age distribution of individuals searching for these cancer topics varied across the topics. Geographically, searches for cancer were predominantly conducted by people from East China, who accounted for approximately 30\% of each cancer search query. Regarding user demand, the total BSI for relevant user demand terms from August 2022 to August 2023 was 676,526,998 out of 2,570,697,380 (15.74\%), representing only a limited total cancer-related search volume. Conclusions: Online searches and inquiries related to genitourinary cancers are on the rise. The depth of users' information demands appears to be influenced by regional economic levels. Cancer treatment decision-making may often involve a family-centered approach. Insights from internet search data can help medical professionals better understand public interests and concerns, enabling them to provide more targeted and reliable health care services. ",
doi="10.2196/57414",
url="https://cancer.jmir.org/2025/1/e57414"
}


@Article{info:doi/10.2196/69663,
author="Yanagisawa, Yuki
and Watabe, Satoshi
and Yokoyama, Sakura
and Sayama, Kyoko
and Kizaki, Hayato
and Tsuchiya, Masami
and Imai, Shungo
and Someya, Mitsuhiro
and Taniguchi, Ryoo
and Yada, Shuntaro
and Aramaki, Eiji
and Hori, Satoko",
title="Identifying Adverse Events in Outpatients With Prostate Cancer Using Pharmaceutical Care Records in Community Pharmacies: Application of Named Entity Recognition",
journal="JMIR Cancer",
year="2025",
month="Mar",
day="11",
volume="11",
pages="e69663",
keywords="natural language processing",
keywords="pharmaceutical care records",
keywords="androgen receptor axis-targeting agents",
keywords="adverse events",
keywords="outpatient care",
abstract="Background: Androgen receptor axis-targeting reagents (ARATs) have become key drugs for patients with castration-resistant prostate cancer (CRPC). ARATs are taken long term in outpatient settings, and effective adverse event (AE) monitoring can help prolong treatment duration for patients with CRPC. Despite the importance of monitoring, few studies have identified which AEs can be captured and assessed in community pharmacies, where pharmacists in Japan dispense medications, provide counseling, and monitor potential AEs for outpatients prescribed ARATs. Therefore, we anticipated that a named entity recognition (NER) system might be used to extract AEs recorded in pharmaceutical care records generated by community pharmacists. Objective: This study aimed to evaluate whether an NER system can effectively and systematically identify AEs in outpatients undergoing ARAT therapy by reviewing pharmaceutical care records generated by community pharmacists, focusing on assessment notes, which often contain detailed records of AEs. Additionally, the study sought to determine whether outpatient pharmacotherapy monitoring can be enhanced by using NER to systematically collect AEs from pharmaceutical care records. Methods: We used an NER system based on the widely used Japanese medical term extraction system MedNER-CR-JA, which uses Bidirectional Encoder Representations from Transformers (BERT). To evaluate its performance for pharmaceutical care records by community pharmacists, the NER system was first applied to 1008 assessment notes in records related to anticancer drug prescriptions. Three pharmaceutically proficient researchers compared the results with the annotated notes assigned symptom tags according to annotation guidelines and evaluated the performance of the NER system on the assessment notes in the pharmaceutical care records. The system was then applied to 2193 assessment notes for patients prescribed ARATs. Results: The F1-score for exact matches of all symptom tags between the NER system and annotators was 0.72, confirming the NER system has sufficient performance for application to pharmaceutical care records. The NER system automatically assigned 1900 symptom tags for the 2193 assessment notes from patients prescribed ARATs; 623 tags (32.8\%) were positive symptom tags (symptoms present), while 1067 tags (56.2\%) were negative symptom tags (symptoms absent). Positive symptom tags included ARAT-related AEs such as ``pain,'' ``skin disorders,'' ``fatigue,'' and ``gastrointestinal symptoms.'' Many other symptoms were classified as serious AEs. Furthermore, differences in symptom tag profiles reflecting pharmacists' AE monitoring were observed between androgen synthesis inhibition and androgen receptor signaling inhibition. Conclusions: The NER system successfully extracted AEs from pharmaceutical care records of patients prescribed ARATs, demonstrating its potential to systematically track the presence and absence of AEs in outpatients. Based on the analysis of a large volume of pharmaceutical medical records using the NER system, community pharmacists not only detect potential AEs but also actively monitor the absence of severe AEs, offering valuable insights for the continuous improvement of patient safety management. ",
doi="10.2196/69663",
url="https://cancer.jmir.org/2025/1/e69663"
}


@Article{info:doi/10.2196/65461,
author="Chima, Sophie
and Hunter, Barbara
and Martinez-Gutierrez, Javiera
and Lumsden, Natalie
and Nelson, Craig
and Boyle, Dougie
and Somasundaram, Kaleswari
and Manski-Nankervis, Jo-Anne
and Emery, Jon",
title="Implementation of a Quality Improvement and Clinical Decision Support Tool for Cancer Diagnosis in Primary Care: Process Evaluation",
journal="JMIR Cancer",
year="2025",
month="Jun",
day="12",
volume="11",
pages="e65461",
keywords="cancer diagnosis",
keywords="implementation",
keywords="clinical decision support tool",
keywords="diagnosis",
keywords="primary care",
keywords="process evaluation",
keywords="quality improvement",
keywords="intervention",
keywords="effectiveness",
keywords="interviews",
keywords="surveys",
keywords="cancer care",
keywords="general practice",
abstract="Background: For patients with cancer, the pathway to diagnosis will most often begin in general practice. In the absence of strong diagnostic features or in patients with nonspecific symptoms, delays in diagnosis can occur. Initial presentations and routine blood tests are important in determining whether a patient requires further investigation. Quality improvement interventions, including auditing tools and clinical decision support (CDS), have been developed for use in general practice to support this diagnostic process. We conducted a process evaluation of a pragmatic, cluster-randomized trial that evaluated the effectiveness of a new technology, Future Health Today (FHT), implemented in general practice to assist with the appropriate follow-up of patients at risk of undiagnosed cancer. Objectives: This study aims to understand implementation gaps, explore differences between the general practices involved, provide context to the trial effectiveness outcomes, and understand the mechanisms behind the intervention successes and failures. Methods: The trial intervention consisted of the FHT tool (with CDS, audit, recall, and quality improvement components), training and educational sessions, benchmarking reports, and ongoing practice support. The 21 general practices in the intervention arm of the trial were included in the process evaluation. Process data were collected using semistructured interviews, usability and educational session surveys, engagement with intervention components, and technical logs. The Medical Research Council's Framework for Developing and Evaluating Complex Interventions was used to analyze and interpret the data. Results: The uptake of the supporting components of the intervention (training and education sessions, benchmarking reports) was low. Most practices only used the CDS component of the tool, facilitated by active delivery, with general practitioners reporting acceptability and ease of use. Complexity, time, and resources were reported as barriers to the use of the auditing tool. Access to a study coordinator and ongoing practice support facilitated the sustained involvement of practices in the trial, while contextual factors, such as the COVID-19 pandemic and staff turnover, impacted their level of participation. The relevance of the intervention varied between practices, with some practices reporting very low numbers of patients who were flagged for further investigation. Conclusions: While some components of the intervention, such as the CDS tool, were considered to be acceptable and useful, this process evaluation highlighted barriers such as time and resources, practice differences, and considerations around the optimal amount of support needed when delivering the intervention. Addressing these in future studies may optimize the implementation process. Further work is needed to determine if a scaled-back approach, which meets the time and resource availability of a busy general practice, can effectively facilitate the implementation of CDS tools. Given the variation seen between practices, the use of the FHT cancer module may be better targeted to certain practices based on size, location, and patient demographics. ",
doi="10.2196/65461",
url="https://cancer.jmir.org/2025/1/e65461"
}


@Article{info:doi/10.2196/67650,
author="Agrawal, Lavlin
and DaSouza, Oakley Richelle
and Mulgund, Pavankumar
and Chaudhary, Pankaj",
title="The Role of Online Support, Caregiving, and Gender in Preventative Cancer Genetic Testing Participation: Cross-Sectional Study From a National Study",
journal="JMIR Cancer",
year="2025",
month="Jun",
day="4",
volume="11",
pages="e67650",
keywords="genetic testing",
keywords="cancer",
keywords="health belief model",
keywords="caregiver status",
keywords="online social support",
keywords="gender differences",
abstract="Background: Despite its potential to predict and detect early cancer risks, genetic testing remains underused by the public. This study, guided by the health belief model (HBM), examined key factors influencing an individual's willingness to undergo genetic testing for cancer, with a particular focus on gender, caregiver status, and participation in online social support groups. Objective: This study aimed to explore the factors that can influence the individual's decision to undergo preventative genetic testing for cancer so that more informed action can be taken to encourage the individuals to engage in preventative health behavior. Methods: This study uses data collected from the 2020 Health Information National Trends Survey (HINTS 5 Cycle 4), which included 2947 respondents representing 199,510,996 US adults aged 18 years and older. Multivariable logistic regression and survey-weighted generalized linear models were applied to examine the relationship between cancer genetic testing and caregiver status, participation in online support groups, gender, and constructs associated with the HBM, while controlling for sociodemographic and health-related characteristics. Results: Our findings show that women are more likely to undergo cancer genetic testing, with gender moderating the influence of perceived susceptibility ($\beta$=2.54, P=.03) and severity ($\beta$=0.94, P<.050) on testing decisions. In line with the HBM, perceived benefits ($\beta$=0.19, P=.03) and cues to action ($\beta$=2.86, P<.001) increase the likelihood of testing. Results also show that caregivers of patients with cancer ($\beta$=1.25, P=.04) and those actively participating in online health support groups ($\beta$=0.47, P=.04) are also more likely to engage in cancer genetic testing. Conclusions: Cancer remains a significant health challenge in the United States, with 1.8 million new cases and 606,520 deaths annually. Early detection is vital for treatment success. This study investigates factors influencing the decision to undergo genetic testing for cancer. The examination of caregiver status and online support groups as influencing factors, along with the HBM, provided a significant theoretical contribution to the health care research domain. Results indicated that caregivers and men should be directly targeted with messaging on genetic cancer screening as a proactive health behavior. Additionally, online support groups can promote early detection and encourage participation in genetic testing. Future research should further explore implementing proactive outreach strategies to encourage wider adoption of genetic testing for cancer. ",
doi="10.2196/67650",
url="https://cancer.jmir.org/2025/1/e67650"
}


@Article{info:doi/10.2196/60653,
author="Jones, Tudor Owain
and Calanzani, Natalia
and Scott, E. Suzanne
and Matin, N. Rubeta
and Emery, Jon
and Walter, M. Fiona",
title="User and Developer Views on Using AI Technologies to Facilitate the Early Detection of Skin Cancers in Primary Care Settings: Qualitative Semistructured Interview Study",
journal="JMIR Cancer",
year="2025",
month="Jan",
day="28",
volume="11",
pages="e60653",
keywords="artificial intelligence",
keywords="AI",
keywords="machine learning",
keywords="ML",
keywords="primary care",
keywords="skin cancer",
keywords="melanoma",
keywords="qualitative research",
keywords="mobile phone",
abstract="Background: Skin cancers, including melanoma and keratinocyte cancers, are among the most common cancers worldwide, and their incidence is rising in most populations. Earlier detection of skin cancer leads to better outcomes for patients. Artificial intelligence (AI) technologies have been applied to skin cancer diagnosis, but many technologies lack clinical evidence and/or the appropriate regulatory approvals. There are few qualitative studies examining the views of relevant stakeholders or evidence about the implementation and positioning of AI technologies in the skin cancer diagnostic pathway. Objective: This study aimed to understand the views of several stakeholder groups on the use of AI technologies to facilitate the early diagnosis of skin cancer, including patients, members of the public, general practitioners, primary care nurse practitioners, dermatologists, and AI researchers. Methods: This was a qualitative, semistructured interview study with 29 stakeholders. Participants were purposively sampled based on age, sex, and geographical location. We conducted the interviews via Zoom between September 2022 and May 2023. Transcribed recordings were analyzed using thematic framework analysis. The framework for the Nonadoption, Abandonment, and Challenges to Scale-Up, Spread, and Sustainability was used to guide the analysis to help understand the complexity of implementing diagnostic technologies in clinical settings. Results: Major themes were ``the position of AI in the skin cancer diagnostic pathway'' and ``the aim of the AI technology''; cross-cutting themes included trust, usability and acceptability, generalizability, evaluation and regulation, implementation, and long-term use. There was no clear consensus on where AI should be placed along the skin cancer diagnostic pathway, but most participants saw the technology in the hands of either patients or primary care practitioners. Participants were concerned about the quality of the data used to develop and test AI technologies and the impact this could have on their accuracy in clinical use with patients from a range of demographics and the risk of missing skin cancers. Ease of use and not increasing the workload of already strained health care services were important considerations for participants. Health care professionals and AI researchers reported a lack of established methods of evaluating and regulating AI technologies. Conclusions: This study is one of the first to examine the views of a wide range of stakeholders on the use of AI technologies to facilitate early diagnosis of skin cancer. The optimal approach and position in the diagnostic pathway for these technologies have not yet been determined. AI technologies need to be developed and implemented carefully and thoughtfully, with attention paid to the quality and representativeness of the data used for development, to achieve their potential. ",
doi="10.2196/60653",
url="https://cancer.jmir.org/2025/1/e60653"
}


@Article{info:doi/10.2196/64724,
author="Johnson, Rose Anna
and Longfellow, Anne Grace
and Lee, N. Clara
and Ormseth, Benjamin
and Skolnick, B. Gary
and Politi, C. Mary
and Rivera, M. Yonaira
and Myckatyn, Terence",
title="Social Media as a Platform for Cancer Care Decision-Making Among Women: Internet Survey-Based Study on Trust, Engagement, and Preferences",
journal="JMIR Cancer",
year="2025",
month="Mar",
day="5",
volume="11",
pages="e64724",
keywords="shared decision-making",
keywords="SDM",
keywords="decision aids",
keywords="cancer treatment",
keywords="breast cancer",
keywords="digital health",
keywords="social media",
keywords="health communication",
keywords="online decision aids",
keywords="health information-seeking behavior",
keywords="trust in health information",
keywords="healthcare accessibility",
keywords="mhealth",
abstract="Background: Decision aids improve patient and clinician decision-making but are underused and often restricted to clinical settings. Objective: Given limited studies analyzing the feasibility of disseminating decision aids through social media, this study aimed to evaluate the acceptability, trust, and engagement of women with social media as a tool to deliver online decision aids for cancer treatment. Methods: To prepare for potential dissemination of a breast cancer decision aid via social media, a cross-sectional survey in February 2023 was conducted via Prime Panels, an online market research platform, of women aged 35-75 years in the United States. Demographics, health, cancer information-seeking behaviors, social media use, trust in social media for health information, as well as the likelihood of viewing cancer-related health information and clicking on decision aids through social media, were assessed. Statistical analyses included descriptive statistics, correlations, and multivariable ordinal regression. Results: Of 607 respondents, 397 (65.4\%) had searched for cancer information, with 185 (46.6\%) using the internet as their primary source. Facebook (Meta) was the most popular platform (511/607, 84.2\%). Trust in social media for health information was higher among Black (14/72, 19.4\%) and Asian respondents (7/27, 25.9\%) than among White respondents (49/480, 10.2\%; P=.003). Younger respondents aged 35-39 years (17/82, 20.7\%) showed higher trust than those aged 70-79 years (12/70, 17.1\%; P<.001). Trust in social media for health information was linked to a higher likelihood of viewing cancer information and accessing a decision aid online (P<.001). Participants who rated social media as ``Trustworthy'' (n=73) were more likely to view cancer information (61/73, 83.6\%) and click on decision aids (61/73, 83.6\%) than those who found it ``Untrustworthy'' (n=277; view: 133/277, 48.0\%; click: 125/277, 45.1\%). Engagement with social media positively correlated with viewing online cancer information (Spearman $\rho$=0.20, P<.001) and willingness to use decision aids ($\rho$=0.21, P<.001). Multivariable ordinal regression analyses confirmed that perception of social media's trustworthiness is a significant predictor of engagement with decision aids (untrustworthy vs trustworthy $\beta$=--1.826, P<.001; neutral vs trustworthy $\beta$=--0.926, P=.007) and of viewing cancer information (untrustworthy vs trustworthy $\beta$=--1.680, P<.001, neutral vs trustworthy $\beta$=--0.581, P=.098), while age and employment status were not significant predictors. Conclusions: This exploratory study suggests that social media platforms may increase access to health information and decision aids. No significant differences were observed between demographic variables and the use or trust in social media for health information. However, trust in social media emerged as a mediating factor between demographics and engagement with cancer information online. Before disseminating decision aids on social media, groups should identify existing trust and engagement patterns with different platforms within their target demographic. ",
doi="10.2196/64724",
url="https://cancer.jmir.org/2025/1/e64724",
url="http://www.ncbi.nlm.nih.gov/pubmed/40053770"
}


@Article{info:doi/10.2196/70827,
author="Yung, Alan
and Shaw, Tim
and Kay, Judy
and Janssen, Anna",
title="Examining How Technology Supports Shared Decision-Making in Oncology Consultations: Qualitative Thematic Analysis",
journal="JMIR Cancer",
year="2025",
month="Jun",
day="11",
volume="11",
pages="e70827",
keywords="digital health",
keywords="patient-centered care",
keywords="person-centered care",
keywords="shared decision-making",
keywords="cancer care",
keywords="oncology",
keywords="artificial intelligence",
keywords="AI",
abstract="Background: Commonly used digital health technologies, such as electronic health record systems and patient portals as well as custom-built digital decision aids, have the potential to enhance person-centered shared decision-making (SDM) in cancer care. SDM is a 2-way exchange of information between at least a clinician and the patient and a shared commitment to make informed decisions. However, there is little evidence in the literature on how technologies are used for SDM or how best they can be designed and integrated into workflows and practice. This may be due to the nature of SDM, which is fundamentally human interactions and conversations that produce desired human outcomes. Therefore, technology must be nonintrusive while supporting the human decision-making process. Objective: This study examined how digital technologies can help cancer care professionals improve SDM in oncology consultations. Methods: Health care professionals who treat patients with cancer were invited to participate in online co-design focus group meetings. During these sessions, they shared their experiences using digital technologies for SDM and provided suggestions to improve their use of digital technologies. The session recordings were transcribed and then analyzed using qualitative thematic analysis. The 3-talk SDM model, which consists of 3 steps---team talk, option talk, and decision talk---was used as the guiding framework. This approach was chosen because the 3-talk SDM model has been adopted in Australia. The researchers walked the participants through the SDM model and discussed their routine clinical workflows. Results: In total, 9 health care professionals with experience treating patients with cancer and using technologies participated in the study. Two focus groups and 2 interviews were conducted in 2024. Three themes and 7 subthemes were generated from the thematic analysis. The findings indicated that various digital technologies, such as electronic health record systems, mobile devices, and patient portals, are used by cancer care professionals to help improve patients' understanding of their disease and available care options. Digital technologies can both improve and undermine SDM. Current systems are generally not designed to support SDM. Key issues such as data integration and interoperability between systems negatively impact the ability of digital technologies to support SDM. Emerging technologies such as generative artificial intelligence were discussed as potential facilitators of SDM by automating information gathering and sharing with patients and between health professionals. Conclusions: This research indicates that digital technologies have the potential to impact SDM in oncology consultations. However, this potential has not yet been fully realized, and significant modifications are required to optimize their usefulness in person-centered SDM. Although technology can facilitate information sharing and improve the efficiency of consultation workflows, it is only part of a complex human communication process that needs support from multiple sources, including the broader multidisciplinary cancer team. ",
doi="10.2196/70827",
url="https://cancer.jmir.org/2025/1/e70827"
}


@Article{info:doi/10.2196/65118,
author="Aye, Sin Phyu
and Barnes, Joanne
and Laking, George
and Cameron, Laird
and Anderson, Malcolm
and Luey, Brendan
and Delany, Stephen
and Harris, Dean
and McLaren, Blair
and Brenman, Elliott
and Wong, Jayden
and Lawrenson, Ross
and Arendse, Michael
and Tin Tin, Sandar
and Elwood, Mark
and Hope, Philip
and McKeage, James Mark",
title="Treatment Outcomes From Erlotinib and Gefitinib in Advanced Epidermal Growth Factor Receptor--Mutated Nonsquamous Non--Small Cell Lung Cancer in Aotearoa New Zealand From 2010 to 2020: Nationwide Whole-of-Patient-Population Retrospective Cohort Study",
journal="JMIR Cancer",
year="2025",
month="Mar",
day="3",
volume="11",
pages="e65118",
keywords="non--small cell lung cancer",
keywords="mutations",
keywords="epidemiology",
keywords="target therapy",
keywords="retrospective cohort study",
abstract="Background: Health care system--wide outcomes from routine treatment with erlotinib and gefitinib are incompletely understood. Objective: The aim of the study is to describe the effectiveness of erlotinib and gefitinib during the first decade of their routine use for treating advanced epidermal growth factor receptor (EGFR) mutation-positive nonsquamous non--small cell lung cancer in the entire cohort of patients treated in Aotearoa New Zealand. Methods: Patients were identified, and data collated from national pharmaceutical dispensing, cancer registration, and mortality registration electronic databases by deterministic data linkage using National Health Index numbers. Time-to-treatment discontinuation and overall survival were measured from the date of first dispensing of erlotinib or gefitinib and analyzed by Kaplan-Meier curves. Associations of treatment outcomes with baseline factors were evaluated using univariable and multivariable Cox regressions. Results: Overall, 752 patients were included who started treatment with erlotinib (n=418) or gefitinib (n=334) before October 2020. Median time-to-treatment discontinuation was 11.6 (95\% CI 10.8?12.4) months, and median overall survival was 20.1 (95\% CI 18.1?21.6) months. Shorter time-to-treatment discontinuation was independently associated with high socioeconomic deprivation (hazard ratio [HR] 1.3, 95\% CI 1.1?1.5 compared to the New Zealand Index of Deprivation 1?4 group), EGFR L858R mutations (HR 1.3, 95\% CI 1.1?1.6 compared to exon 19 deletion), and distant disease at cancer diagnosis (HR 1.4, 95\% CI 1.2?1.7 compared to localized or regional disease). The same factors were independently associated with shorter overall survival. Outcome estimates and predictors remained unchanged in sensitivity analyses. Conclusions: Outcomes from routine treatment with erlotinib and gefitinib in New Zealand patients with advanced EGFR-mutant nonsquamous non--small cell lung cancer are comparable with those reported in randomized trials and other health care system--wide retrospective cohort studies. Socioeconomic status, EGFR mutation subtype, and disease extent at cancer diagnosis were independent predictors of treatment outcomes in that setting. Trial Registration: Australia New Zealand Clinical Trials Registry ACTRN12615000998549; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=368928\&isReview=true International Registered Report Identifier (IRRID): RR2-10.2196/51381 ",
doi="10.2196/65118",
url="https://cancer.jmir.org/2025/1/e65118"
}


@Article{info:doi/10.2196/64506,
author="Sun, Chengkun
and Mobley, Erin
and Quillen, Michael
and Parker, Max
and Daly, Meghan
and Wang, Rui
and Visintin, Isabela
and Awad, Ziad
and Fishe, Jennifer
and Parker, Alexander
and George, Thomas
and Bian, Jiang
and Xu, Jie",
title="Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World Data: Case Control Study",
journal="JMIR Cancer",
year="2025",
month="Jun",
day="19",
volume="11",
pages="e64506",
keywords="prediction",
keywords="machine learning",
keywords="ML",
keywords="rectal cancer",
keywords="colorectal cancer",
keywords="CRC",
keywords="youth",
keywords="adolescent",
keywords="middle-aged",
keywords="United States",
keywords="Americans",
keywords="electronic health record",
keywords="EHR",
keywords="Shapley Additive Explanations",
keywords="SHAP",
keywords="diagnosis",
keywords="prevention and treatment",
abstract="Background: Colorectal cancer is now the leading cause of cancer-related deaths among young Americans. Accurate early prediction and a thorough understanding of the risk factors for early-onset colorectal cancer (EOCRC) are vital for effective prevention and treatment, particularly for patients below the recommended screening age. Objective: Our study aims to predict EOCRC using machine learning (ML) and structured electronic health record data for individuals under the screening age of 45 years, with the aim of exploring potential risk and protective factors that could support early diagnosis. Methods: We identified a cohort of patients under the age of 45 years from the OneFlorida+ Clinical Research Consortium. Given the distinct pathology of colon cancer (CC) and rectal cancer (RC), we created separate prediction models for each cancer type with various ML algorithms. We assessed multiple prediction time windows (ie, 0, 1, 3, and 5 y) and ensured robustness through propensity score matching to account for confounding variables including sex, race, ethnicity, and birth year. We conducted a comprehensive performance evaluation using metrics including area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. Both linear (ie, logistic regression, support vector machine) and nonlinear (ie, Extreme Gradient Boosting and random forest) models were assessed to enable rigorous comparison across different classification strategies. In addition, we used the Shapley Additive Explanations to interpret the models and identify key risk and protective factors associated with EOCRC. Results: The final cohort included 1358 CC cases with 6790 matched controls, and 560 RC cases with 2800 matched controls. The RC group had a more balanced sex distribution (2:3 male-to-female) compared to the CC group (2:5 male-to-female), and both groups showed diverse racial and ethnic representation. Our predictive models demonstrated reasonable results, with AUC scores for CC prediction of 0.811, 0.748, 0.689, and 0.686 at 0, 1, 3, and 5 years before diagnosis, respectively. For RC prediction, AUC scores were 0.829, 0.771, 0.727, and 0.721 across the same time windows. Key predictive features across both cancer types included immune and digestive system disorders, secondary malignancies, and underweight status. In addition, blood diseases emerged as prominent indicators specifically for CC. Conclusions: Our findings demonstrate the potential of ML models leveraging electronic health record data to facilitate the early prediction of EOCRC in individuals under 45 years. By uncovering important risk factors and achieving promising predictive performance, this study provides preliminary insights that could inform future efforts toward earlier detection and prevention in younger populations. ",
doi="10.2196/64506",
url="https://cancer.jmir.org/2025/1/e64506"
}


@Article{info:doi/10.2196/73069,
author="Li, Xin
and Yang, Wen-yu
and Zhang, Fan
and Shan, Rui
and Mei, Fang
and Song, Shi-Bing
and Sun, Bang-Kai
and Chen, Jing
and Hu, Run-ze
and Yang, Yang
and Yang, Yi-hang
and Liu, Jing-yao
and Yuan, Chun-Hui
and Liu, Zheng",
title="Size-Specific Predictors for Malignancy Risk in Follicular Thyroid Neoplasms: Machine Learning Analysis",
journal="JMIR Cancer",
year="2025",
month="Jul",
day="11",
volume="11",
pages="e73069",
keywords="follicular thyroid neoplasm",
keywords="tumor size",
keywords="machine learning",
keywords="malignancy",
keywords="follicular thyroid cancer",
keywords="follicular thyroid adenoma",
keywords="random forest",
keywords="XGBoost",
abstract="Background: Surgeons often face challenges in distinguishing between benign and malignant follicular thyroid neoplasms (FTNs), particularly small tumors, until diagnostic surgery is performed. Objective: This study aimed to identify the size-specific predictors for the malignancy risk of FTNs preoperatively. Methods: A retrospective cohort study was conducted at Peking University Third Hospital in Beijing, China, from 2012 to 2023. Patients with a postoperative pathological diagnosis of follicular thyroid adenoma (FTA) or follicular thyroid carcinoma (FTC) were included. FTNs were classified into small- and large-sized categories based on the cutoff value of the tumor diameter derived from spline regression, which indicated the turning point of malignancy risk. We identified the 5 most important predictors from 22 variables including demography, sonography, and hormones, using machine learning methods. We also calculated the odds ratios (OR) with 95\% CI for these predictors in both small- and large-sized FTNs. Results: Altogether, we included 1494 FTNs, comprising 1266 FTAs and 228 FTCs. FTNs with a maximum diameter less than 3.0 cm were grouped as small-sized tumors (n=715), while those with larger diameters were categorized as large-sized tumors (n=779). In the small-sized group, tumors with macrocalcification (OR 2.90, 95\% CI 1.50-5.60), those with peripheral calcification (OR 4.50, 95\% CI 1.50-13.00), and those in younger patients (OR 1.33, 95\% CI 1.05-1.69) showed a higher malignancy risk. In the large-sized group, tumors presenting with a nodule-in-nodule appearance (OR 3.30, 95\% CI 1.30-7.90) exhibited a higher malignancy risk. In both groups, lower thyroid-stimulating hormone levels (OR 1.49, 95\% CI 1.20-1.85 for small-sized FTNs; OR 1.61, 95\% CI 1.37-1.96 for large-sized FTNs) and a larger mean diameter (OR 1.40, 95\% CI 1.10-1.70 for small-sized FTNs; OR 1.50 95\% CI 1.20-1.70 for large-sized FTNs) were associated with the malignancy risk of FTNs. Conclusion: This study identified size-specific predictors for malignancy risk in FTNs, highlighting the importance of stratified prediction based on tumor size. ",
doi="10.2196/73069",
url="https://cancer.jmir.org/2025/1/e73069"
}


@Article{info:doi/10.2196/67379,
author="Wang, Shiqi
and Chai, Na
and Xu, Jingji
and Yu, Pengfei
and Huang, Luguang
and Wang, Quan
and Zhao, Zhifeng
and Yang, Bin
and Wei, Jiangpeng
and Wang, Xiangjie
and Ji, Gang
and Zheng, Minwen",
title="Prediction of a Panel of Programmed Cell Death Protein-1 (PD-1) Inhibitor--Sensitive Biomarkers Using Multiphase Computed Tomography Imaging Textural Features: Retrospective Cohort Analysis",
journal="JMIR Cancer",
year="2025",
month="Jul",
day="11",
volume="11",
pages="e67379",
keywords="biomarkers",
keywords="computed tomography",
keywords="gastric cancer",
keywords="immunotherapy",
keywords="radiomics",
abstract="Background: Immune checkpoint inhibitors represent an effective therapeutic approach for advanced gastric cancer. Their efficacy largely depends on the status of tumor biomarkers including human epidermal growth factor receptor 2 (HER2), programmed death-ligand 1 (PD-L1; combined positive score ?1), and microsatellite instability-high (MSI-H). To noninvasively evaluate these biomarkers, researchers have developed radiomic models for individual biomarker prediction. However, in clinical practice, holistic prediction of these biomarkers as an integrated system is more efficient. Currently, the feasibility of implementing radiomics-based comprehensive biomarker prediction remains unclear, requiring further investigation. Objective: This study aimed to develop a radiomics-based predictive model using multiphase computed tomography (CT) images to holistically evaluate HER2, PD-L1, and MSI-H status in patients with gastric cancer. Methods: A retrospective analysis was conducted on 461 patients with gastric cancer who underwent radical gastrectomy between 2019 and 2022. Clinical data, contrast-enhanced CT images (arterial phase [AP] and portal venous phase [PP]), and pathological results were collected. Patients were categorized into two groups: (1) the programmed cell death protein-1 inhibitor panel-positive group, comprising patients with HER2 overexpression, PD-L1 positive, or MSI-H status; and (2) the negative group, comprising patients without HER2 amplification, PD-L1 negative, or microsatellite instability-low or microsatellite stable condition. Radiomic features (including first-order statistics, shape features, and wavelet-derived textures) were extracted from both AP and PP images, yielding 1834 features per phase. Least absolute shrinkage and selection operator regression was applied to select key features. In total, 3 models were constructed using the Extreme Gradient Boosting algorithm: AP-only (8 features), PP-only (22 features), and a fused model combining AP and PP features (20 features: 6 AP and 14 PP features). Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, and decision curve analysis. Results: Of the 461 patients, 147 patients (31.9\%) were classified into the panel-positive group. The clinical features were similar between the 2 groups. The fused model demonstrated superior performance in the test set (AUC 0.82, 95\% CI 0.68?0.95), significantly outperforming AP-only (AUC 0.61, 95\% CI 0.47?0.74) and PP-only models (AUC 0.70, 95\% CI 0.49?0.91). Sensitivity and specificity for the AP-only, PP-only, and the fused model were 0.33 and 0.85; 0.50 and 0.86; and 0.60 and 0.83, respectively. Decision curve analysis confirmed that the fused model provided higher clinical net benefit across threshold probabilities. Conclusions: The construction of integrated biomarker prediction models through radiomics demonstrates technical feasibility, offering a promising methodology for comprehensive tumor characterization. ",
doi="10.2196/67379",
url="https://cancer.jmir.org/2025/1/e67379"
}


@Article{info:doi/10.2196/71937,
author="Lam, Sing Chun
and Hua, Rong
and Loong, Ho-Fung Herbert
and Ngan, Chun-Kit
and Cheung, Ting Yin",
title="Association Between Comorbidity Clusters and Mortality in Patients With Cancer: Predictive Modeling Using Machine Learning Approaches of Data From the United States and Hong Kong",
journal="JMIR Cancer",
year="2025",
month="Jul",
day="16",
volume="11",
pages="e71937",
keywords="comorbidity",
keywords="multimorbidity",
keywords="machine learning",
keywords="cluster",
keywords="clustering",
keywords="cancer",
keywords="mortality",
keywords="oncology",
keywords="multiple chronic conditions",
keywords="metabolic",
abstract="Background: Patients with cancer and cancer survivors often experience multiple chronic health conditions, which can impact symptom burden and treatment outcomes. Despite the high prevalence of multimorbidity, research on cancer prognosis has predominantly focused on cancers in isolation. There is growing interest in machine learning techniques for cancer studies. However, these methods have not been applied in the context of supportive care for patients with cancer who have multimorbidity. Furthermore, few studies have investigated the associations between comorbidity clusters and mortality outcomes. Objective: This study investigated comorbidity clusters among patients with cancer using machine learning and examined their associations with mortality outcomes in two large representative samples from the United States and Hong Kong. Methods: This study used data from the National Health and Nutrition Examination Survey (NHANES) and the Hospital Authority Data Collaboration Laboratory (HADCL). Participants aged ?20 years with a history of cancer were included. The study used a two-step framework to identify clusters of comorbidities in NHANES. In the first step, we used four machine learning techniques, including the Bernoulli mixture model and partition-based methods, to cluster the comorbidities. In the second step, domain experts reviewed and ranked the identified clusters to ensure clinical relevance. The clusters that had the highest average rank were selected for further analysis. The associations between comorbidity clusters and mortality outcomes were analyzed using Cox proportional hazards models. We conducted an external validation to evaluate the generalizability of the clusters identified in the NHANES cohort and their associations with mortality using HADCL. The same number of clusters was replicated based on the distinctive patterns and distribution of comorbidities observed within each cluster. Results: The study included 4390 participants in NHANES and 12,484 participants in HADCL. Four comorbidity clusters were identified: low comorbidity, metabolic, cardiovascular disease (CVD), and respiratory. In NHANES, participants in the respiratory cluster had the highest risk of all-cause mortality (adjusted hazard ratio [aHR] 1.62, 95\% CI 1.26?2.08; P<.001), followed by the CVD cluster (aHR 1.50, 95\% CI 1.26?1.80; P<.001) compared to the low comorbidity cluster. The 3 clusters were associated with higher risks of CVD-related mortality (aHR 1.48?3.05, 95\% CI 1.14-4.07; P<.003). The effects of comorbidity clusters on mortality were modified by income-to-poverty ratio (P for interaction=.04), diet quality (P for interaction=.02), and cancer prognosis (P for interaction=.005). In the HADCL (validation) cohort, participants in the respiratory and CVD clusters had a higher risk of all-cause mortality. Conclusions: High comorbidity burden clusters showed increased all-cause and CVD-related mortality in patients with cancer. These findings highlight the significance of considering comorbidity burden in cancer care. Machine learning approaches can provide valuable insights into complex multimorbidity profiles. Further research is needed to deepen understanding of the relationships between multimorbidity and cancer-specific outcomes. ",
doi="10.2196/71937",
url="https://cancer.jmir.org/2025/1/e71937"
}


@Article{info:doi/10.2196/64685,
author="Lai, Chun-Chi
and Chen, Cheng-Yu
and Chang, Tzu-Hao",
title="Predicting Pathological Complete Response Following Neoadjuvant Therapy in Patients With Breast Cancer: Development of Machine Learning--Based Prediction Models in a Retrospective Study",
journal="JMIR Cancer",
year="2025",
month="Jul",
day="18",
volume="11",
pages="e64685",
keywords="breast cancer",
keywords="neoadjuvant therapy",
keywords="pathological complete response",
keywords="breast sonography",
keywords="machine learning",
abstract="Background: Breast cancer is the most prevalent form of cancer worldwide, with 2.3 million new diagnoses in 2022. Recent advancements in treatment have led to a shift in the use of chemotherapy-targeted immunotherapy from a postoperative adjuvant to a preoperative neoadjuvant approach in select cases, resulting in enhanced survival outcomes. A pathological complete response (pCR) is a critical prognostic marker, with higher pCR rates linked to improved overall and disease-free survival. Objective: The objective of this study was to develop robust, machine learning--based prediction models for pCR following neoadjuvant therapy, leveraging clinical, laboratory, and imaging data. Methods: A retrospective cohort study was conducted using data from the Taipei Medical University Clinical Research Database from 2015 to 2022. Eligible patients were those with breast cancer who received neoadjuvant therapy followed by curative surgical resection. Machine learning models were developed using 3 distinct sets of variables. Model 1 included 14 clinical features such as age, height, weight, tumor stage, receptor status, tumor markers, and intrinsic subtype. Model 2 expanded on this by incorporating additional laboratory data and comorbidities (29 variables in total). Model 3 added breast sonography response data to the clinical variables in model 1. Algorithms including logistic regression, random forest, support vector machines, and extreme gradient boosting were used. Feature selection was performed using recursive feature elimination with cross-validation, and model performance was assessed using accuracy and area under the receiver operating characteristic curve (AUROC). Results: A total of 334 patients were analyzed, with 199 in the non-pCR group and 135 in the pCR group. The application of logistic regression with recursive feature elimination with cross-validation was found to demonstrate the optimal performance among the various algorithms that were evaluated in this study. Model 1 attained a mean accuracy of 0.66 (SD 0.02) and a mean AUROC of 0.73 (SD 0.01). The incorporation of laboratory data and comorbidities in model 2 did not yield significant enhancement, with a mean accuracy of 0.67 (SD 0.02) and a mean AUROC of 0.73 (SD 0.01). The incorporation of breast sonography response in model 3 led to a modest enhancement in predictive performance for the sonography group (accuracy 0.68; AUROC 0.60) in comparison to the nonsonography group (accuracy 0.66; AUROC 0.55). Despite the modest sample size (41 patients) of model 3, the integration of sonography data appeared to offer additional value in predicting pCR and warrants further investigation. Conclusions: This study suggests that incorporating breast sonography into models with clinical and laboratory data may modestly improve pCR prediction. It is important to note that the findings of this study are preliminary and require cautious interpretation. Further studies are required to validate this approach and support its integration into a machine learning--based clinical workflow. ",
doi="10.2196/64685",
url="https://cancer.jmir.org/2025/1/e64685"
}


@Article{info:doi/10.2196/57275,
author="Yamagishi, Yosuke
and Nakamura, Yuta
and Hanaoka, Shouhei
and Abe, Osamu",
title="Large Language Model Approach for Zero-Shot Information Extraction and Clustering of Japanese Radiology Reports: Algorithm Development and Validation",
journal="JMIR Cancer",
year="2025",
month="Jan",
day="23",
volume="11",
pages="e57275",
keywords="radiology reports",
keywords="clustering",
keywords="large language model",
keywords="natural language processing",
keywords="information extraction",
keywords="lung cancer",
keywords="machine learning",
abstract="Background: The application of natural language processing in medicine has increased significantly, including tasks such as information extraction and classification. Natural language processing plays a crucial role in structuring free-form radiology reports, facilitating the interpretation of textual content, and enhancing data utility through clustering techniques. Clustering allows for the identification of similar lesions and disease patterns across a broad dataset, making it useful for aggregating information and discovering new insights in medical imaging. However, most publicly available medical datasets are in English, with limited resources in other languages. This scarcity poses a challenge for development of models geared toward non-English downstream tasks. Objective: This study aimed to develop and evaluate an algorithm that uses large language models (LLMs) to extract information from Japanese lung cancer radiology reports and perform clustering analysis. The effectiveness of this approach was assessed and compared with previous supervised methods. Methods: This study employed the MedTxt-RR dataset, comprising 135 Japanese radiology reports from 9 radiologists who interpreted the computed tomography images of 15 lung cancer patients obtained from Radiopaedia. Previously used in the NTCIR-16 (NII Testbeds and Community for Information Access Research) shared task for clustering performance competition, this dataset was ideal for comparing the clustering ability of our algorithm with those of previous methods. The dataset was split into 8 cases for development and 7 for testing, respectively. The study's approach involved using the LLM to extract information pertinent to lung cancer findings and transforming it into numeric features for clustering, using the K-means method. Performance was evaluated using 135 reports for information extraction accuracy and 63 test reports for clustering performance. This study focused on the accuracy of automated systems for extracting tumor size, location, and laterality from clinical reports. The clustering performance was evaluated using normalized mutual information, adjusted mutual information , and the Fowlkes-Mallows index for both the development and test data. Results: The tumor size was accurately identified in 99 out of 135 reports (73.3\%), with errors in 36 reports (26.7\%), primarily due to missing or incorrect size information. Tumor location and laterality were identified with greater accuracy in 112 out of 135 reports (83\%); however, 23 reports (17\%) contained errors mainly due to empty values or incorrect data. Clustering performance of the test data yielded an normalized mutual information of 0.6414, adjusted mutual information of 0.5598, and Fowlkes-Mallows index of 0.5354. The proposed method demonstrated superior performance across all evaluation metrics compared to previous methods. Conclusions: The unsupervised LLM approach surpassed the existing supervised methods in clustering Japanese radiology reports. These findings suggest that LLMs hold promise for extracting information from radiology reports and integrating it into disease-specific knowledge structures. ",
doi="10.2196/57275",
url="https://cancer.jmir.org/2025/1/e57275"
}


@Article{info:doi/10.2196/64000,
author="Heudel, Pierre
and Ahmed, Mashal
and Renard, Felix
and Attye, Arnaud",
title="Leveraging Digital Twins for Stratification of Patients with Breast Cancer and Treatment Optimization in Geriatric Oncology: Multivariate Clustering Analysis",
journal="JMIR Cancer",
year="2025",
month="May",
day="23",
volume="11",
pages="e64000",
keywords="digital twins",
keywords="artificial intelligence",
keywords="breast cancer",
keywords="older adult patients with cancer",
keywords="treatment",
keywords="geriatric oncology",
keywords="geriatric",
keywords="oncology",
keywords="cancer",
keywords="clustering analysis",
keywords="therapeutic",
keywords="older adult",
keywords="elder",
keywords="old",
keywords="patients with cancer",
keywords="decision-making tools",
keywords="decision-making",
keywords="manifold learning model",
keywords="chemotherapy",
keywords="comorbidities",
keywords="comorbidity",
keywords="health care",
abstract="Background: Defining optimal adjuvant therapeutic strategies for older adult patients with breast cancer remains a challenge, given that this population is often overlooked and underserved in clinical research and decision-making tools. Objectives: This study aimed to develop a prognostic and treatment guidance tool tailored to older adult patients using artificial intelligence (AI) and a combination of clinical and biological features. Methods: A retrospective analysis was conducted on data from women aged 70+ years with HER2-negative early-stage breast cancer treated at the French L{\'e}on B{\'e}rard Cancer Center between 1997 and 2016. Manifold learning and machine learning algorithms were applied to uncover complex data relationships and develop predictive models. Predictors included age, BMI, comorbidities, hemoglobin levels, lymphocyte counts, hormone receptor status, Scarff-Bloom-Richardson grade, tumor size, and lymph node involvement. The dimension reduction technique PaCMAP was used to map patient profiles into a 3D space, allowing comparison with similar cases to estimate prognoses and potential treatment benefits. Results: Out of 1229 initial patients, 793 were included after data refinement. The selected predictors demonstrated high predictive efficacy for 5-year mortality, with mean area under the curve scores of 0.81 for Random Forest Classification and 0.76 for Support Vector Classifier. The tool categorized patients into prognostic clusters and enabled the estimation of treatment outcomes, such as chemotherapy benefits. Unlike traditional models that focus on isolated factors, this AI-based approach integrates multiple clinical and biological features to generate a comprehensive biomedical profile. Conclusions: This study introduces a novel AI-driven prognostic tool for older adult patients with breast cancer, enhancing treatment guidance by leveraging advanced machine learning techniques. The model provides a more nuanced understanding of disease dynamics and therapeutic strategies, emphasizing the importance of personalized oncology care. ",
doi="10.2196/64000",
url="https://cancer.jmir.org/2025/1/e64000"
}


@Article{info:doi/10.2196/64697,
author="Varma, Gowtham
and Yenukoti, Kumar Rohit
and Kumar M, Praveen
and Ashrit, Sai Bandlamudi
and Purushotham, K.
and Subash, C.
and Ravi, Kumar Sunil
and Kurien, Verghese
and Aman, Avinash
and Manoharan, Mithun
and Jaiswal, Shashank
and Anand, Akash
and Barve, Rakesh
and Thiagarajan, Viswanathan
and Lenehan, Patrick
and Soefje, A. Scott
and Soundararajan, Venky",
title="A Deep Learning--Enabled Workflow to Estimate Real-World Progression-Free Survival in Patients With Metastatic Breast Cancer: Study Using Deidentified Electronic Health Records",
journal="JMIR Cancer",
year="2025",
month="May",
day="15",
volume="11",
pages="e64697",
keywords="real-world evidence",
keywords="data-driven oncology",
keywords="real-world progression-free survival",
keywords="metastatic breast cancer",
keywords="natural language processing",
keywords="NLP",
keywords="survival",
keywords="cancer",
keywords="oncology",
keywords="breast",
keywords="metastatic",
keywords="deep learning",
keywords="machine learning",
keywords="ML",
keywords="workflow",
keywords="report",
keywords="notes",
keywords="electronic health record",
keywords="EHR",
keywords="documentation",
abstract="Background: Progression-free survival (PFS) is a crucial endpoint in cancer drug research. Clinician-confirmed cancer progression, namely real-world PFS (rwPFS) in unstructured text (ie, clinical notes), serves as a reasonable surrogate for real-world indicators in ascertaining progression endpoints. Response evaluation criteria in solid tumors (RECIST) is traditionally used in clinical trials using serial imaging evaluations but is impractical when working with real-world data. Manual abstraction of clinical progression from unstructured notes remains the gold standard. However, this process is a resource-intensive, time-consuming process. Natural language processing (NLP), a subdomain of machine learning, has shown promise in accelerating the extraction of tumor progression from real-world data in recent years. Objectives: We aim to configure a pretrained, general-purpose health care NLP framework to transform free-text clinical notes and radiology reports into structured progression events for studying rwPFS on metastatic breast cancer (mBC) cohorts. Methods: This study developed and validated a novel semiautomated workflow to estimate rwPFS in patients with mBC using deidentified electronic health record data from the Nference nSights platform. The developed workflow was validated in a cohort of 316 patients with hormone receptor--positive, human epidermal growth factor receptor-2 (HER-2) 2-negative mBC, who were started on palbociclib and letrozole combination therapy between January 2015 and December 2021. Ground-truth datasets were curated to evaluate the workflow's performance at both the sentence and patient levels. NLP-captured progression or a change in therapy line were considered outcome events, while death, loss to follow-up, and end of the study period were considered censoring events for rwPFS computation. Peak reduction and cumulative decline in Patient Health Questionnaire-8 (PHQ-8) scores were analyzed in the progressed and nonprogressed patient subgroups. Results: The configured clinical NLP engine achieved a sentence-level progression capture accuracy of 98.2\%. At the patient level, initial progression was captured within {\textpm}30 days with 88\% accuracy. The median rwPFS for the study cohort (N=316) was 20 (95\% CI 18-25) months. In a validation subset (n=100), rwPFS determined by manual curation was 25 (95\% CI 15-35) months, closely aligning with the computational workflow's 22 (95\% CI 15-35) months. A subanalysis revealed rwPFS estimates of 30 (95\% CI 24-39) months from radiology reports and 23 (95\% CI 19-28) months from clinical notes, highlighting the importance of integrating multiple note sources. External validation also demonstrated high accuracy (92.5\% sentence level; 90.2\% patient level). Sensitivity analysis revealed stable rwPFS estimates across varying levels of missing source data and event definitions. Peak reduction in PHQ-8 scores during the study period highlighted significant associations between patient-reported outcomes and disease progression. Conclusions: This workflow enables rapid and reliable determination of rwPFS in patients with mBC receiving combination therapy. Further validation across more diverse external datasets and other cancer types is needed to ensure broader applicability and generalizability. ",
doi="10.2196/64697",
url="https://cancer.jmir.org/2025/1/e64697"
}


@Article{info:doi/10.2196/66269,
author="Shan, Rui
and Li, Xin
and Chen, Jing
and Chen, Zheng
and Cheng, Yuan-Jia
and Han, Bo
and Hu, Run-Ze
and Huang, Jiu-Ping
and Kong, Gui-Lan
and Liu, Hui
and Mei, Fang
and Song, Shi-Bing
and Sun, Bang-Kai
and Tian, Hui
and Wang, Yang
and Xiao, Wu-Cai
and Yao, Xiang-Yun
and Ye, Jing-Ming
and Yu, Bo
and Yuan, Chun-Hui
and Zhang, Fan
and Liu, Zheng",
title="Interpretable Machine Learning to Predict the Malignancy Risk of Follicular Thyroid Neoplasms in Extremely Unbalanced Data: Retrospective Cohort Study and Literature Review",
journal="JMIR Cancer",
year="2025",
month="Feb",
day="10",
volume="11",
pages="e66269",
keywords="follicular thyroid neoplasm",
keywords="machine learning",
keywords="prediction model",
keywords="malignancy",
keywords="unbalanced data",
keywords="literature review",
abstract="Background: Diagnosing and managing follicular thyroid neoplasms (FTNs) remains a significant challenge, as the malignancy risk cannot be determined until after diagnostic surgery. Objective: We aimed to use interpretable machine learning to predict the malignancy risk of FTNs preoperatively in a real-world setting. Methods: We conducted a retrospective cohort study at the Peking University Third Hospital in Beijing, China. Patients with postoperative pathological diagnoses of follicular thyroid adenoma (FTA) or follicular thyroid carcinoma (FTC) were included, excluding those without preoperative thyroid ultrasonography. We used 22 predictors involving demographic characteristics, thyroid sonography, and hormones to train 5 machine learning models: logistic regression, least absolute shrinkage and selection operator regression, random forest, extreme gradient boosting, and support vector machine. The optimal model was selected based on discrimination, calibration, interpretability, and parsimony. To address the highly imbalanced data (FTA:FTC ratio>5:1), model discrimination was assessed using both the area under the receiver operating characteristic curve and the area under the precision-recall curve (AUPRC). To interpret the model, we used Shapley Additive Explanations values and partial dependence and individual conditional expectation plots. Additionally, a systematic review was performed to synthesize existing evidence and validate the discrimination ability of the previously developed Thyroid Imaging Reporting and Data System for Follicular Neoplasm scoring criteria to differentiate between benign and malignant FTNs using our data. Results: The cohort included 1539 patients (mean age 47.98, SD 14.15 years; female: n=1126, 73.16\%) with 1672 FTN tumors (FTA: n=1414; FTC: n=258; FTA:FTC ratio=5.5). The random forest model emerged as optimal, identifying mean thyroid-stimulating hormone (TSH) score, mean tumor diameter, mean TSH, TSH instability, and TSH measurement levels as the top 5 predictors in discriminating FTA from FTC, with the area under the receiver operating characteristic curve of 0.79 (95\% CI 0.77?0.81) and AUPRC of 0.40 (95\% CI 0.37-0.44). Malignancy risk increased nonlinearly with larger tumor diameters and higher TSH instability but decreased nonlinearly with higher mean TSH scores or mean TSH levels. FTCs with small sizes (mean diameter 2.88, SD 1.38 cm) were more likely to be misclassified as FTAs compared to larger ones (mean diameter 3.71, SD 1.36 cm). The systematic review of the 7 included studies revealed that (1) the FTA:FTC ratio varied from 0.6 to 4.0, lower than the natural distribution of 5.0; (2) no studies assessed prediction performance using AUPRC in unbalanced datasets; and (3) external validations of Thyroid Imaging Reporting and Data System for Follicular Neoplasm scoring criteria underperformed relative to the original study. Conclusions: Tumor size and TSH measurements were important in screening FTN malignancy risk preoperatively, but accurately predicting the risk of small-sized FTNs remains challenging. Future research should address the limitations posed by the extreme imbalance in FTA and FTC distributions in real-world data. ",
doi="10.2196/66269",
url="https://cancer.jmir.org/2025/1/e66269"
}


@Article{info:doi/10.2196/64399,
author="Brnabic, Michael Alan James
and Lipkovich, Ilya
and Kadziola, Zbigniew
and He, Dan
and Krein, M. Peter
and Hess, M. Lisa",
title="Next-Generation Sequencing--Based Testing Among Patients With Advanced or Metastatic Nonsquamous Non--Small Cell Lung Cancer in the United States: Predictive Modeling Using Machine Learning Methods",
journal="JMIR Cancer",
year="2025",
month="Jun",
day="11",
volume="11",
pages="e64399",
keywords="lung cancer",
keywords="NGS testing",
keywords="next-generation sequencing",
keywords="real-world data",
keywords="machine learning",
keywords="biomarkers",
keywords="predictive modeling",
keywords="artificial intelligence",
keywords="treatment guidelines",
keywords="tumor biomarker",
keywords="oncology",
abstract="Background: Next-generation sequencing (NGS) has become a cornerstone of treatment for lung cancer and is recommended in current treatment guidelines for patients with advanced or metastatic disease. Objective: This study was designed to use machine learning methods to determine demographic and clinical characteristics of patients with advanced or metastatic non--small cell lung cancer (NSCLC) that may predict likelihood of receiving NGS-based testing (ever vs never NGS-tested) as well as likelihood of timing of testing (early vs late NGS-tested). Methods: Deidentified patient-level data were analyzed in this study from a real-world cohort of patients with advanced or metastatic NSCLC in the United States. Patients with nonsquamous disease, who received systemic therapy for NSCLC, and had at least 3 months of follow-up data for analysis were included in this study. Three strategies, logistic regression models, penalized logistic regression using least absolute shrinkage and selection operator penalty, and extreme gradient boosting with classification trees as base learners, were used to identify predictors of ever versus never and early versus late NGS testing. Data were split into D1 (training+validation; 80\%) and D2 (testing; 20\%) sets; the 3 strategies were evaluated by comparing their performance on multiple m=1000 splits in the training (70\%) and validation data (30\%) within the D1 set. The final model was selected by evaluating performance using the area under the receiver operating curve while taking into account considerations of simplicity and clinical interpretability. Performance was re-estimated using the test data D2. Results: A total of 13,425 met the criteria for the ever NGS-tested, and 17,982 were included in the never NGS-tested group. Performance metrics showed the area under the receiver operating curve evaluated from validation data was similar across all models (77\%-84\%). Among those in the ever NGS-tested group, 84.08\% (n=11,289) were early NGS-tested, and 15.91\% (n=2136) late NGS-tested. Factors associated with both ever having NGS testing as well as early NGS testing included later year of NSCLC diagnosis, no smoking history, and evidence of programmed death ligand 1 testing (all P<.05). Factors associated with a greater chance of never receiving NGS testing included older age, lower performance status, Black race, higher number of single-gene tests, public insurance, and treatment in a geography with Molecular Diagnostics Services Program adoption (all P<.05). Conclusions: Predictors of ever versus never as well as early versus late NGS testing in the setting of advanced or metastatic NSCLC were consistent across machine learning methods in this study, demonstrating the ability of these models to identify factors that may predict NGS-based testing. There is a need to ensure that patients regardless of age, race, insurance status, and geography (factors associated with lower odds of receiving NGS testing in this study) are provided with equitable access to NGS-based testing. ",
doi="10.2196/64399",
url="https://cancer.jmir.org/2025/1/e64399"
}


@Article{info:doi/10.2196/64611,
author="Byrom, Bill
and Everhart, Anthony
and Cordero, Paul
and Garratt, Chris
and Meyer, Tim",
title="Leveraging Patient-Reported Outcome Measures for Optimal Dose Selection in Early Phase Cancer Trials",
journal="JMIR Cancer",
year="2025",
month="Feb",
day="28",
volume="11",
pages="e64611",
keywords="clinical trials",
keywords="early phase",
keywords="dose finding",
keywords="patient-reported outcome",
keywords="PRO",
keywords="electronic patient-reported outcome",
keywords="ePRO",
keywords="PRO-CTCAE",
keywords="adverse events",
keywords="tolerability",
keywords="optimal dose",
keywords="cancer trials",
keywords="dose toxicity",
keywords="oncology",
keywords="drug development",
keywords="electronic collection",
keywords="dose level",
keywords="pharmacodynamic",
keywords="cytotoxic chemotherapy drugs",
keywords="cytotoxic",
keywords="chemotherapy drug",
keywords="life-threatening disease",
keywords="Common Terminology Criteria for Adverse Events",
doi="10.2196/64611",
url="https://cancer.jmir.org/2025/1/e64611"
}


@Article{info:doi/10.2196/62711,
author="Bartel, Christianna
and Chen, Leeann
and Huang, Weiyu
and Li, Qichang
and Li, Qingyang
and Fedor, Jennifer
and Durica, C. Krina
and Low, A. Carissa",
title="Design and Use of Patient-Facing Electronic Patient-Reported Outcomes and Sensor Data Visualizations During Outpatient Chemotherapy",
journal="JMIR Cancer",
year="2025",
month="Jan",
day="10",
volume="11",
pages="e62711",
keywords="oncology",
keywords="cancer",
keywords="data visualization",
keywords="remote monitoring",
keywords="mobile technology",
keywords="patients",
keywords="outpatient",
keywords="chemotherapy",
keywords="symptoms",
keywords="side effects",
keywords="cancer treatment",
keywords="electronic patient-reported outcome",
keywords="online",
keywords="monitoring",
keywords="self-management",
doi="10.2196/62711",
url="https://cancer.jmir.org/2025/1/e62711"
}


@Article{info:doi/10.2196/64809,
author="McElroy, A. Jane
and Smith, B. Jamie
and Everett, D. Kevin",
title="Monthly Variations in Colorectal Cancer Screening Tests Among Federally Qualified Health Center Patients in Missouri: Quality Improvement Project",
journal="JMIR Cancer",
year="2025",
month="Mar",
day="19",
volume="11",
pages="e64809",
keywords="colorectal cancer screening",
keywords="federally qualified health center",
keywords="FQHC",
keywords="fecal immunochemical test",
keywords="FIT",
keywords="FIT-DNA",
keywords="colorectal cancer",
keywords="CRC",
keywords="cancer",
keywords="cancer screening",
keywords="colonoscopy",
keywords="United States",
keywords="health center",
keywords="quality improvement",
abstract="Background: Cancer is the second leading cause of death in the United States. Compelling evidence shows screening detects colorectal cancer (CRC) at earlier stages and prevents the development of CRC through the removal of precancerous polyps. The Healthy People 2030 goal for CRC screening is 68.3\%, but only 36.5\% of Missouri federally qualified health center patients aged 50?75 years are up-to-date on CRC screening. For average risk patients, there are three commonly used screening tests in the United States---two types of stool tests collected at home (fecal immunochemical test [FIT]--immunochemical fecal occult blood test [FOBT] and FIT-DNA, such as Cologuard) and colonoscopies completed at procedural centers. Objective: This study aims to examine variation by month for the three types of CRC testing to evaluate consistent patient care by clinical staff. Methods: Data from 31 federally qualified health center clinics in Missouri from 2011 to 2023 were analyzed. A sample of 34,124 unique eligible ``average risk'' patients defined as persons not having a personal history of CRC or certain types of polyps, family history of CRC, personal history of inflammatory bowel disease, and personal history of receiving radiation to the abdomen or pelvic to treat a previous cancer or confirmed or suspected hereditary CRC syndrome. Another eligibility criterion is that patients need to be seen at least once at the clinic to be included in the denominator for the screening rate calculation. Descriptive statistics characterize the sample, while bivariate analyses assess differences in screening types by month. Results: Completion of CRC screening yielded statistically significant differences for patients completing the different types of CRC screening by month. October-January had the highest proportions of patients (644-680 per month, 8.5\%?10.2\%) receiving a colonoscopy, while February-April had the lowest (509-578 per month, 6.9\%?7.8\%), with 614 being the average monthly number of colonoscopies. For FIT-FOBT, June-August had the higher proportions of patients receiving this test (563-613 per month, 8.9\%?9.6\%), whereas December-February had the lowest (453-495 per month, 7.1\%?8\%), with 541 being the average monthly number of FIT-FOBT kits used. For FIT-DNA, March was the most popular month with 11.3\% (n=261 per month) of patients using the Cologuard test, followed by April, May, and November (207-220 per month, 8.7\%?9.4\%), and January and June (168-171 per month, 7.2\%-7.3\%) had the lowest proportion of patients using Cologuard, with 193 being the average monthly number of FIT-DNA kits used. Combining all tests, February had the fewest CRC tests completed (1153/16,173, 7.1\%). Conclusions: Home-based tests are becoming popular, replacing the gold standard colonoscopy, but need to be repeated more frequently. Monthly variation of screening over the course of a year suggests that CRC screening efforts and patient care may be less than ideal. Months with lower rates of screening for each type of CRC test represent opportunities for improving CRC screening. ",
doi="10.2196/64809",
url="https://cancer.jmir.org/2025/1/e64809"
}


@Article{info:doi/10.2196/72522,
author="Owens, L. Otis
and Leonard, S. Michael",
title="Evaluating an AI Chatbot ``Prostate Cancer Info'' for Providing Quality Prostate Cancer Screening Information: Cross-Sectional Study",
journal="JMIR Cancer",
year="2025",
month="May",
day="21",
volume="11",
pages="e72522",
keywords="generative artificial intelligence",
keywords="chatbot",
keywords="chatGPT",
keywords="prostate cancer",
keywords="cancer screening",
keywords="shared decision making",
keywords="artificial intelligence",
abstract="Background: Generative artificial intelligence (AI) chatbots may be useful tools for supporting shared prostate cancer (PrCA) screening decisions, but the information produced by these tools sometimes lack quality or credibility. ``Prostate Cancer Info'' is a custom GPT chatbot developed to provide plain-language PrCA information only from websites of key authorities on cancer and peer-reviewed literature. Objective: The objective of this paper was to evaluate the accuracy, completeness, and readability of Prostate Cancer Info's responses to frequently asked PrCA screening questions. Methods: A total of 23 frequently asked PrCA questions were individually input into Prostate Cancer Info. Responses were recorded in Microsoft Word and reviewed by 2 raters for their accuracy and completeness. Readability of content was determined by pasting responses into a web-based Flesch Kincaid Reading Ease Scores calculator. Results: Responses to all questions were accurate and culturally appropriate. In total, 17 of the 23 questions (74\%) had complete responses. The average readability of responses was 64.5 (SD 8.7; written at an 8th-grade level). Conclusions: Generative AI chatbots, such as Prostate Cancer Info, are great starting places for learning about PrCA screening and preparing men to engage in shared decision-making but should not be used as independent sources of PrCA information because key information may be omitted. Men are encouraged to use these tools to complement information received from a health care provider. ",
doi="10.2196/72522",
url="https://cancer.jmir.org/2025/1/e72522"
}


@Article{info:doi/10.2196/71958,
author="Komariah, Maria
and Maulana, Sidik
and Amirah, Shakira
and Platini, Hesti
and Rahayuwati, Laili
and Yusuf, Ah
and Firdaus, Hasymi Mohd Khairul Zul",
title="Correction: Benefits of Remote-Based Mindfulness on Physical Symptom Outcomes in Cancer Survivors: Systematic Review and Meta-Analysis",
journal="JMIR Cancer",
year="2025",
month="Feb",
day="13",
volume="11",
pages="e71958",
doi="10.2196/71958",
url="https://cancer.jmir.org/2025/1/e71958"
}