TY - JOUR AU - Huang, Xiayuan AU - Ren, Shushun AU - Mao, Xinyue AU - Chen, Sirui AU - Chen, Elle AU - He, Yuqi AU - Jiang, Yun PY - 2025/5/2 TI - Association Between Risk Factors and Major Cancers: Explainable Machine Learning Approach JO - JMIR Cancer SP - e62833 VL - 11 KW - electronic health record KW - EHR KW - cancer risk modeling KW - risk factor analysis KW - explainable machine learning KW - machine learning KW - ML KW - risk factor KW - major cancers KW - monitoring KW - cancer risk KW - breast cancer KW - colorectal cancer KW - lung cancer KW - prostate cancer KW - cancer patients KW - clinical decision-making N2 - 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. UR - https://cancer.jmir.org/2025/1/e62833 UR - http://dx.doi.org/10.2196/62833 ID - info:doi/10.2196/62833 ER - TY - JOUR AU - Hu, Danqing AU - Zhang, Shanyuan AU - Liu, Qing AU - Zhu, Xiaofeng AU - Liu, Bing PY - 2025/4/3 TI - Large Language Models in Summarizing Radiology Report Impressions for Lung Cancer in Chinese: Evaluation Study JO - J Med Internet Res SP - e65547 VL - 27 KW - large language model KW - impression summarization KW - radiology report KW - radiology KW - evaluation study KW - ChatGPT KW - natural language processing KW - ultrasound KW - radiologist KW - thoracic surgeons N2 - Background: Large language models (LLMs), such as ChatGPT, have demonstrated impressive capabilities in various natural language processing tasks, particularly in text generation. However, their effectiveness in summarizing radiology report impressions remains uncertain. Objective: This study aims to evaluate the capability of nine LLMs, that is, Tongyi Qianwen, ERNIE Bot, ChatGPT, Bard, Claude, Baichuan, ChatGLM, HuatuoGPT, and ChatGLM-Med, in summarizing Chinese radiology report impressions for lung cancer. Methods: We collected 100 Chinese computed tomography (CT), positron emission tomography (PET)?CT, and ultrasound (US) reports each from Peking University Cancer Hospital and Institute. All these reports were from patients with suspected or confirmed lung cancer. Using these reports, we created zero-shot, one-shot, and three-shot prompts with or without complete example reports as inputs to generate impressions. We used both automatic quantitative evaluation metrics and five human evaluation metrics (completeness, correctness, conciseness, verisimilitude, and replaceability) to assess the generated impressions. Two thoracic surgeons (SZ and BL) and one radiologist (QL) compared the generated impressions with reference impressions, scoring them according to the five human evaluation metrics. Results: In the automatic quantitative evaluation, ERNIE Bot, Tongyi Qianwen, and Claude demonstrated the best overall performance in generating impressions for CT, PET-CT, and US reports, respectively. In the human semantic evaluation, ERNIE Bot outperformed the other LLMs in terms of conciseness, verisimilitude, and replaceability on CT impression generation, while its completeness and correctness scores were comparable to those of other LLMs. Tongyi Qianwen excelled in PET-CT impression generation, with the highest scores for correctness, conciseness, verisimilitude, and replaceability. Claude achieved the best conciseness, verisimilitude, and replaceability scores on US impression generation, and its completeness and correctness scores are close to the best results obtained by other LLMs. The generated impressions were generally complete and correct but lacked conciseness and verisimilitude. Although one-shot and few-shot prompts improved conciseness and verisimilitude, clinicians noted a significant gap between the generated impressions and those written by radiologists. Conclusions: Current LLMs can produce radiology impressions with high completeness and correctness but fall short in conciseness and verisimilitude, indicating they cannot yet fully replace impressions written by radiologists. UR - https://www.jmir.org/2025/1/e65547 UR - http://dx.doi.org/10.2196/65547 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/65547 ER - TY - JOUR AU - Pitrou, Isabelle AU - Petrangelo, Adriano AU - Besson, Charlotte AU - Pepe, Carmela AU - Waschke, Helen Annika AU - Agulnik, Jason AU - Gonzalez, V. Anne AU - Ezer, Nicole PY - 2025/3/28 TI - Lung Cancer Screening in Family Members and Peers of Patients With Lung Cancer: Protocol for a Prospective Cohort Study JO - JMIR Res Protoc SP - e58529 VL - 14 KW - lung cancer KW - low-dose CT KW - chest tomography KW - lung cancer screening KW - patient advocacy KW - early detection of cancer KW - referral and consultation KW - cohort study KW - patient empowerment KW - patient experience N2 - Background: Low-dose computed tomography (LDCT) screening is promising for the early detection of lung cancer (LC) and the reduction of LC-related mortality. Despite the implementation of LC screening programs worldwide, recruitment is challenging. While recruitment for LC screening is based on physician referrals and mass advertising, novel recruitment strategies are needed to improve the enrollment of high-risk individuals into LC screening. Objective: We aim to identify whether patients with LC can act as advocates to enroll their family members and close contacts into LC screening and whether this strategy increases screening uptake at the population level. Methods: We designed a prospective cohort study comprising 2 cohorts constituted between June 2023 and January 2024 with a prospective follow-up of 18 months. Patients with LC (cohort 1) are approached at clinics of the McGill University Health Centre, educated on tools for communicating with family members and close contacts about the benefits of LC screening, and invited to refer their close ones. Referred individuals (cohort 2) are directed to this study?s web-based questionnaire to assess their LC risk score with the PLCOm2012 (Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial) prediction model. Individuals meeting the eligibility criteria for LC screening (PLCOm2012 score ?2% and aged 55-74 years) are directed toward the Quebec LC screening program. Data collected include sociodemographic characteristics, health literacy and smoking status (all participants), patient activation (cohort 1), perceived risk of LC, and generalized anxiety at baseline and at 28 days (cohort 2). LDCT completion within 18 months from referral is assessed from health records. Focus groups will identify the barriers and facilitators in the uptake of LC screening and preventative behaviors based on perceived genetic and clinical LC risks. The primary outcomes are the number of referred participants per survivor of LC and the mean risk of LC of the referred population based on PLCOm2012 scores. The secondary outcomes are the proportion of (1) participants eligible for LC screening; (2) participants eligible for screening who complete LDCT screening within 18 months of referral from a survivor of LC; (3) participants showing interest in genetic testing to inform LC risk; and (4) participants showing interest in a smoking cessation program. Multivariable logistic regression will identify the predictive factors of being referred for LC screening. PLCOm2012 scores will be compared for referred participants and controls from the provincial LC screening program. Results: Overall, 25 survivors of LC and 84 close contacts were enrolled from June 2023 to January 2024, with followed up through July 2025. The results are expected by the end of 2025. Conclusions: We describe an approach to LC screening referral, leveraging patients with LC as advocates to increase screening awareness and uptake among their family and peers. Trial Registration: ClinicalTrials.gov NCT05645731; https://clinicaltrials.gov/ct2/show/NCT05645731 International Registered Report Identifier (IRRID): DERR1-10.2196/58529 UR - https://www.researchprotocols.org/2025/1/e58529 UR - http://dx.doi.org/10.2196/58529 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/58529 ER - TY - JOUR AU - Zippi, D. Zachary AU - Cortopassi, O. Isabel AU - Grage, A. Rolf AU - Johnson, M. Elizabeth AU - McCann, R. Matthew AU - Mergo, J. Patricia AU - Sonavane, K. Sushil AU - Stowell, T. Justin AU - Little, P. Brent PY - 2025/3/11 TI - Assessing Public Interest in Mammography, Computed Tomography Lung Cancer Screening, and Computed Tomography Colonography Screening Examinations Using Internet Search Data: Cross-Sectional Study JO - JMIR Cancer SP - e53328 VL - 11 KW - lung cancer KW - lung cancer screening KW - breast cancer KW - mammography KW - colon cancer KW - CT colonography KW - Google search KW - internet KW - Google Trends KW - imaging-based KW - cancer screening KW - search data KW - noninvasive KW - cancer KW - CT KW - online KW - public awareness KW - big data KW - analytics KW - patient education KW - screening uptake N2 - 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. UR - https://cancer.jmir.org/2025/1/e53328 UR - http://dx.doi.org/10.2196/53328 ID - info:doi/10.2196/53328 ER - TY - JOUR AU - Aye, Sin Phyu AU - Barnes, Joanne AU - Laking, George AU - Cameron, Laird AU - Anderson, Malcolm AU - Luey, Brendan AU - Delany, Stephen AU - Harris, Dean AU - McLaren, Blair AU - Brenman, Elliott AU - Wong, Jayden AU - Lawrenson, Ross AU - Arendse, Michael AU - Tin Tin, Sandar AU - Elwood, Mark AU - Hope, Philip AU - McKeage, James Mark PY - 2025/3/3 TI - 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 JO - JMIR Cancer SP - e65118 VL - 11 KW - non?small cell lung cancer KW - mutations KW - epidemiology KW - target therapy KW - retrospective cohort study N2 - 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 UR - https://cancer.jmir.org/2025/1/e65118 UR - http://dx.doi.org/10.2196/65118 ID - info:doi/10.2196/65118 ER - TY - JOUR AU - Li, Guangqi AU - Zhou, Xueyan AU - Deng, Junyue AU - Wang, Jiao AU - Ai, Ping AU - Zeng, Jingyuan AU - Ma, Xuelei AU - Liao, Hu PY - 2025/2/25 TI - Digital Therapeutics?Based Cardio-Oncology Rehabilitation for Lung Cancer Survivors: Randomized Controlled Trial JO - JMIR Mhealth Uhealth SP - e60115 VL - 13 KW - cardio-oncology rehabilitation KW - digital therapeutics KW - telerehabilitation KW - non-small cell lung cancer KW - exercise prescription KW - cardiology KW - oncology KW - rehabilitation KW - cardiorespiratory fitness KW - cardiopulmonary KW - cancer KW - physical activity KW - digital health KW - digital technology KW - randomized controlled trial KW - wearable KW - app KW - quality of life KW - survivor N2 - Background: Lung cancer ranks as the leading cause of cancer-related deaths. For lung cancer survivors, cardiopulmonary fitness is a strong independent predictor of survival, while surgical interventions impact both cardiovascular and pulmonary function. Home-based cardiac telerehabilitation through wearable devices and mobile apps is a substitution for traditional, center-based rehabilitation with equal efficacy and a higher completion rate. However, it has not been widely used in clinical practice. Objective: The objective of this study was to broaden the use of digital health care in the cardiopulmonary rehabilitation of lung cancer survivors and to assess its impact on cardiopulmonary fitness and quality of life (QOL). Methods: Early-stage nonsmall cell lung cancer survivors aged 18-70 years were included. All the participants received surgery 1-2 months before enrollment and did not require further antitumor therapy. Participants were randomly assigned to receive cardiac telerehabilitation or usual care for 5 months. Artificial intelligence?driven exercise prescription with a video guide and real-time heart rate (HR) monitoring was generated based on cardiopulmonary exercise testing. Aerobic exercise combining elastic band?based resistance exercises were recommended with a frequency of 3-5 d/wk and a duration of 90-150 min/wk. The effective exercise duration was recorded when patients? HR reached the target zone (HRresting + [HRmax ? HRresting] × [?40%-60%]), representing the duration under the target intensity. The prescription used a gradual progression in duration and action intensity based on the exercise data and feedback. Outcome measurements included cardiopulmonary fitness; lung function; cardiac function; tumor marker; safety; compliance; and scales assessing symptoms, psychology, sleep, fatigue, and QOL. Results: A total of 40 (85%) out of 47 patients finished the trial. The average prescription compliance rate of patients in the telerehabilitation group reached 101.2%, with an average exercise duration of 151.4 min/wk and an average effective exercise duration of 92.3 min/wk. The cardiac telerehabilitation was associated with higher improvement of maximal oxygen uptake peak (3.66, SD 3.23 mL/Kg/min vs 1.09, SD 3.23 mL/Kg/min; P=.02) and global health status or QOL (16.25, SD 23.02 vs 1.04, SD 13.90; P=.03) compared with usual care. Better alleviation of affective interference (?0.88, SD 1.50 vs 0.21, SD 1.22; P=.048), fatigue (?8.89, SD 15.96 vs 1.39, SD 12.09; P=.02), anxiety (?0.31, SD 0.44 vs ?0.05, SD 0.29; P=.048), and daytime dysfunction (?0.55, SD 0.69 vs 0.00, SD 0.52; P=.02) was also observed in the telerehabilitation group. No exercise-related adverse events were identified during the intervention period. Conclusions: The 5-month, digital therapeutics?based telerehabilitation improved cardiorespiratory fitness in lung cancer survivors with good compliance and safety. Patients receiving telerehabilitation also reported improved QOL with reduced levels of fatigue, anxiety, and daytime dysfunction. Trial Registration: Chinese Clinical Trial Registry ChiCTR2200064000; https://www.chictr.org.cn/showproj.html?proj=180594 UR - https://mhealth.jmir.org/2025/1/e60115 UR - http://dx.doi.org/10.2196/60115 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/60115 ER - TY - JOUR AU - Choudhury, Ananya AU - Volmer, Leroy AU - Martin, Frank AU - Fijten, Rianne AU - Wee, Leonard AU - Dekker, Andre AU - Soest, van Johan PY - 2025/2/6 TI - Advancing Privacy-Preserving Health Care Analytics and Implementation of the Personal Health Train: Federated Deep Learning Study JO - JMIR AI SP - e60847 VL - 4 KW - gross tumor volume segmentation KW - federated learning infrastructure KW - privacy-preserving technology KW - cancer KW - deep learning KW - artificial intelligence KW - lung cancer KW - oncology KW - radiotherapy KW - imaging KW - data protection KW - data privacy N2 - Background: The rapid advancement of deep learning in health care presents significant opportunities for automating complex medical tasks and improving clinical workflows. However, widespread adoption is impeded by data privacy concerns and the necessity for large, diverse datasets across multiple institutions. Federated learning (FL) has emerged as a viable solution, enabling collaborative artificial intelligence model development without sharing individual patient data. To effectively implement FL in health care, robust and secure infrastructures are essential. Developing such federated deep learning frameworks is crucial to harnessing the full potential of artificial intelligence while ensuring patient data privacy and regulatory compliance. Objective: The objective is to introduce an innovative FL infrastructure called the Personal Health Train (PHT) that includes the procedural, technical, and governance components needed to implement FL on real-world health care data, including training deep learning neural networks. The study aims to apply this federated deep learning infrastructure to the use case of gross tumor volume segmentation on chest computed tomography images of patients with lung cancer and present the results from a proof-of-concept experiment. Methods: The PHT framework addresses the challenges of data privacy when sharing data, by keeping data close to the source and instead bringing the analysis to the data. Technologically, PHT requires 3 interdependent components: ?tracks? (protected communication channels), ?trains? (containerized software apps), and ?stations? (institutional data repositories), which are supported by the open source ?Vantage6? software. The study applies this federated deep learning infrastructure to the use case of gross tumor volume segmentation on chest computed tomography images of patients with lung cancer, with the introduction of an additional component called the secure aggregation server, where the model averaging is done in a trusted and inaccessible environment. Results: We demonstrated the feasibility of executing deep learning algorithms in a federated manner using PHT and presented the results from a proof-of-concept study. The infrastructure linked 12 hospitals across 8 nations, covering 4 continents, demonstrating the scalability and global reach of the proposed approach. During the execution and training of the deep learning algorithm, no data were shared outside the hospital. Conclusions: The findings of the proof-of-concept study, as well as the implications and limitations of the infrastructure and the results, are discussed. The application of federated deep learning to unstructured medical imaging data, facilitated by the PHT framework and Vantage6 platform, represents a significant advancement in the field. The proposed infrastructure addresses the challenges of data privacy and enables collaborative model development, paving the way for the widespread adoption of deep learning?based tools in the medical domain and beyond. The introduction of the secure aggregation server implied that data leakage problems in FL can be prevented by careful design decisions of the infrastructure. Trial Registration: ClinicalTrials.gov NCT05775068; https://clinicaltrials.gov/study/NCT05775068 UR - https://ai.jmir.org/2025/1/e60847 UR - http://dx.doi.org/10.2196/60847 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/60847 ER - TY - JOUR AU - Liu, Weiqi AU - Wu, You AU - Zheng, Zhuozhao AU - Bittle, Mark AU - Yu, Wei AU - Kharrazi, Hadi PY - 2025/1/27 TI - Enhancing Diagnostic Accuracy of Lung Nodules in Chest Computed Tomography Using Artificial Intelligence: Retrospective Analysis JO - J Med Internet Res SP - e64649 VL - 27 KW - artificial intelligence KW - diagnostic accuracy KW - lung nodule KW - radiology KW - AI system N2 - Background: Uncertainty in the diagnosis of lung nodules is a challenge for both patients and physicians. Artificial intelligence (AI) systems are increasingly being integrated into medical imaging to assist diagnostic procedures. However, the accuracy of AI systems in identifying and measuring lung nodules on chest computed tomography (CT) scans remains unclear, which requires further evaluation. Objective: This study aimed to evaluate the impact of an AI-assisted diagnostic system on the diagnostic efficiency of radiologists. It specifically examined the report modification rates and missed and misdiagnosed rates of junior radiologists with and without AI assistance. Methods: We obtained effective data from 12,889 patients in 2 tertiary hospitals in Beijing before and after the implementation of the AI system, covering the period from April 2018 to March 2022. Diagnostic reports written by both junior and senior radiologists were included in each case. Using reports by senior radiologists as a reference, we compared the modification rates of reports written by junior radiologists with and without AI assistance. We further evaluated alterations in lung nodule detection capability over 3 years after the integration of the AI system. Evaluation metrics of this study include lung nodule detection rate, accuracy, false negative rate, false positive rate, and positive predictive value. The statistical analyses included descriptive statistics and chi-square, Cochran-Armitage, and Mann-Kendall tests. Results: The AI system was implemented in Beijing Anzhen Hospital (Hospital A) in January 2019 and Tsinghua Changgung Hospital (Hospital C) in June 2021. The modification rate of diagnostic reports in the detection of lung nodules increased from 4.73% to 7.23% (?21=12.15; P<.001) at Hospital A. In terms of lung nodule detection rates postimplementation, Hospital C increased from 46.19% to 53.45% (?21=25.48; P<.001) and Hospital A increased from 39.29% to 55.22% (?21=122.55; P<.001). At Hospital A, the false negative rate decreased from 8.4% to 5.16% (?21=9.85; P=.002), while the false positive rate increased from 2.36% to 9.77% (?21=53.48; P<.001). The detection accuracy demonstrated a decrease from 93.33% to 92.23% for Hospital A and from 95.27% to 92.77% for Hospital C. Regarding the changes in lung nodule detection capability over a 3-year period following the integration of the AI system, the detection rates for lung nodules exhibited a modest increase from 54.6% to 55.84%, while the overall accuracy demonstrated a slight improvement from 92.79% to 93.92%. Conclusions: The AI system enhanced lung nodule detection, offering the possibility of earlier disease identification and timely intervention. Nevertheless, the initial reduction in accuracy underscores the need for standardized diagnostic criteria and comprehensive training for radiologists to maximize the effectiveness of AI-enabled diagnostic systems. UR - https://www.jmir.org/2025/1/e64649 UR - http://dx.doi.org/10.2196/64649 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/64649 ER - TY - JOUR AU - Yamagishi, Yosuke AU - Nakamura, Yuta AU - Hanaoka, Shouhei AU - Abe, Osamu PY - 2025/1/23 TI - Large Language Model Approach for Zero-Shot Information Extraction and Clustering of Japanese Radiology Reports: Algorithm Development and Validation JO - JMIR Cancer SP - e57275 VL - 11 KW - radiology reports KW - clustering KW - large language model KW - natural language processing KW - information extraction KW - lung cancer KW - machine learning N2 - 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. UR - https://cancer.jmir.org/2025/1/e57275 UR - http://dx.doi.org/10.2196/57275 ID - info:doi/10.2196/57275 ER - TY - JOUR AU - Su, Jianwei AU - Ye, Cuiling AU - Zhang, Qian AU - Liang, Yi AU - Wu, Jianwei AU - Liang, Guixi AU - Cheng, Yalan AU - Yang, Xiaojuan PY - 2025/1/1 TI - Impact of Remote Symptom Management on Exercise Adherence After Video-Assisted Thoracic Surgery for Lung Cancer in a Tertiary Hospital in China: Protocol for a Prospective Randomized Controlled Trial JO - JMIR Res Protoc SP - e60420 VL - 14 KW - thoracic surgery KW - rehabilitation medicine KW - patient-reported outcome measures KW - patient participation KW - telemedicine KW - eHealth KW - mobile phone N2 - Background: Regular pulmonary rehabilitation exercises are crucial for patients with lung cancer after surgery. However, poor adherence to outpatient exercises is difficult to address due to inadequate supervision. The integration of remote symptom management through electronic patient-reported outcomes (ePROs) offers a potential solution to improve adherence by enabling more effective monitoring and intervention. Objective: This study aims to evaluate the impact of ePRO-based remote symptom management on enhancing adherence to outpatient pulmonary rehabilitation exercises following video-assisted thoracic surgery for lung cancer. Methods: In this single-center, prospective, randomized controlled trial, 736 patients undergoing minimally invasive lung resection will be recruited. All patients will use a smartphone app for perioperative management, allowing periodic PRO measurement and recording of exercise participation. Upon discharge, patients will be randomly assigned 1:1 into either an intervention or control group. The intervention group will complete the Perioperative Symptom Assessment for Patients Undergoing Lung Surgery (PSA-Lung) scale on the day of discharge and postdischarge days 3, 7, 14, 21, and 28. Alerts will be triggered at the provider side if any of the 5 core symptoms (pain, cough, shortness of breath, sleep disturbance, and fatigue) scored ?4, prompting remote symptom management. The control group will complete the PRO measures without triggering alerts. The primary outcome is the rehabilitation exercise adherence rate. Secondary outcomes include postdischarge pulmonary complication rate, 30-day readmission rate, trajectory of symptom severity changes, exercise participation rate, and patient satisfaction. Results: The enrollment of study participants started in December 2023 and is expected to end in March 2025. The final comprehensive analysis of the results is planned for May 2025, after all data have been collected and thoroughly reviewed. Conclusions: This study is among the first to investigate the feasibility and effectiveness of ePRO-based remote symptom management in enhancing rehabilitation adherence after video-assisted thoracic surgery for lung cancer. If successful, this approach could significantly influence postoperative care practices and potentially be adopted in similar settings. Trial Registration: ClinicalTrials.gov NCT05990946; https://clinicaltrials.gov/study/NCT05990946 International Registered Report Identifier (IRRID): DERR1-10.2196/60420 UR - https://www.researchprotocols.org/2025/1/e60420 UR - http://dx.doi.org/10.2196/60420 UR - http://www.ncbi.nlm.nih.gov/pubmed/39610048 ID - info:doi/10.2196/60420 ER - TY - JOUR AU - Kim, Sanghwan AU - Jang, Sowon AU - Kim, Borham AU - Sunwoo, Leonard AU - Kim, Seok AU - Chung, Jin-Haeng AU - Nam, Sejin AU - Cho, Hyeongmin AU - Lee, Donghyoung AU - Lee, Keehyuck AU - Yoo, Sooyoung PY - 2024/12/20 TI - Automated Pathologic TN Classification Prediction and Rationale Generation From Lung Cancer Surgical Pathology Reports Using a Large Language Model Fine-Tuned With Chain-of-Thought: Algorithm Development and Validation Study JO - JMIR Med Inform SP - e67056 VL - 12 KW - AJCC Cancer Staging Manual 8th edition KW - American Joint Committee on Cancer KW - large language model KW - chain-of-thought KW - rationale KW - lung cancer KW - report analysis KW - AI KW - surgery KW - pathology reports KW - tertiary hospital KW - generative language models KW - efficiency KW - accuracy KW - automated N2 - Background: Traditional rule-based natural language processing approaches in electronic health record systems are effective but are often time-consuming and prone to errors when handling unstructured data. This is primarily due to the substantial manual effort required to parse and extract information from diverse types of documentation. Recent advancements in large language model (LLM) technology have made it possible to automatically interpret medical context and support pathologic staging. However, existing LLMs encounter challenges in rapidly adapting to specialized guideline updates. In this study, we fine-tuned an LLM specifically for lung cancer pathologic staging, enabling it to incorporate the latest guidelines for pathologic TN classification. Objective: This study aims to evaluate the performance of fine-tuned generative language models in automatically inferring pathologic TN classifications and extracting their rationale from lung cancer surgical pathology reports. By addressing the inefficiencies and extensive parsing efforts associated with rule-based methods, this approach seeks to enable rapid and accurate reclassification aligned with the latest cancer staging guidelines. Methods: We conducted a comparative performance evaluation of 6 open-source LLMs for automated TN classification and rationale generation, using 3216 deidentified lung cancer surgical pathology reports based on the American Joint Committee on Cancer (AJCC) Cancer Staging Manual8th edition, collected from a tertiary hospital. The dataset was preprocessed by segmenting each report according to lesion location and morphological diagnosis. Performance was assessed using exact match ratio (EMR) and semantic match ratio (SMR) as evaluation metrics, which measure classification accuracy and the contextual alignment of the generated rationales, respectively. Results: Among the 6 models, the Orca2_13b model achieved the highest performance with an EMR of 0.934 and an SMR of 0.864. The Orca2_7b model also demonstrated strong performance, recording an EMR of 0.914 and an SMR of 0.854. In contrast, the Llama2_7b model achieved an EMR of 0.864 and an SMR of 0.771, while the Llama2_13b model showed an EMR of 0.762 and an SMR of 0.690. The Mistral_7b and Llama3_8b models, on the other hand, showed lower performance, with EMRs of 0.572 and 0.489, and SMRs of 0.377 and 0.456, respectively. Overall, the Orca2 models consistently outperformed the others in both TN stage classification and rationale generation. Conclusions: The generative language model approach presented in this study has the potential to enhance and automate TN classification in complex cancer staging, supporting both clinical practice and oncology data curation. With additional fine-tuning based on cancer-specific guidelines, this approach can be effectively adapted to other cancer types. UR - https://medinform.jmir.org/2024/1/e67056 UR - http://dx.doi.org/10.2196/67056 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/67056 ER - TY - JOUR AU - Hu, Chao AU - Fu, Qiang AU - Gao, Fei Fei AU - Zeng, Jian AU - Xiao, Wei AU - Li, Hui AU - Peng, Li AU - Huang, Xi AU - Yang, Li AU - Chen, Zhi Wen AU - Jiang, Yan Ming PY - 2024/11/29 TI - Ultrasound-Guided High-Intensity Focused Ultrasound Combined With PD-1 Blockade in Patients With Liver Metastases From Lung Cancer: Protocol for a Single-Arm Phase 2 Trial JO - JMIR Res Protoc SP - e59152 VL - 13 KW - high-intensity focused ultrasound KW - programmed cell death protein KW - PD-1 blockade KW - liver metastases KW - lung cancer KW - immunotherapy KW - treatment efficacy KW - quality of life KW - HILL study N2 - Background: While immunotherapy has revolutionized oncological management, its efficacy in lung cancer patients with liver metastases remains limited, potentially due to the unique immunosuppressive microenvironment of the liver. Local liver treatment has been shown to enhance the immunotherapy response, and high-intensity focused ultrasound (HIFU), a minimally invasive local treatment, has demonstrated promising results in combination with immunotherapy. However, clinical data regarding HIFU in lung cancer with liver metastases are limited. Objective: We designed the HILL (Ultrasound-Guided High-Intensity Focused Ultrasound Combined With PD-1 Blockade in Patients With Liver Metastases From Lung Cancer) study to investigate the effectiveness and safety of HIFU in combination with immunotherapy for lung cancer with liver metastases. Methods: The HILL study is a single-armed, single-center, phase 2 clinical trial that will enroll 30 patients with lung cancer and liver metastases. The treatment regimen involves administering HIFU to liver metastases 1 week before the first dose of a programmed cell death protein (PD)?1 blockade, which is then administered every 3 weeks. The primary aim is to determine the overall response rate based on immune-related response criteria. Secondary aims include safety, progression-free survival, overall response, overall survival, and quality of life. Exploratory studies will also be conducted using whole blood, plasma, archival cancer tissue, and tumor biopsies during progression or relapse to identify potential biomarkers. Results: The study was funded on March 14, 2022, and received ethical approval on April 27, 2022. Clinical trial registration was completed by June 10, 2022, with participant recruitment beginning on July 10, 2022. Data collection commenced on July 14, 2022, with the enrollment of the first patient. By April 2024, 6 participants had been recruited. The results are expected to be published in December 2026. Conclusions: This study seeks to improve treatment outcomes for lung cancer patients with liver metastases by combining HIFU and PD-1 inhibition. The study also aims to identify potential biomarkers through exploratory research that can aid in selecting patients for optimized outcomes in the future. Trial Registration: Chinese Clinical Trial Registry ChiCTR2200061076; https://www.chictr.org.cn/showproj.html?proj=170967 International Registered Report Identifier (IRRID): DERR1-10.2196/59152 UR - https://www.researchprotocols.org/2024/1/e59152 UR - http://dx.doi.org/10.2196/59152 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/59152 ER - TY - JOUR AU - Lu, Taiping AU - Deng, Ting AU - Long, Yangyang AU - Li, Jin AU - Hu, Anmei AU - Hu, Yufan AU - Ouyang, Li AU - Wang, Huiping AU - Ma, Junliang AU - Chen, Shaolin AU - Hu, Jiale PY - 2024/11/11 TI - Effectiveness and Feasibility of Digital Pulmonary Rehabilitation in Patients Undergoing Lung Cancer Surgery: Systematic Review and Meta-Analysis JO - J Med Internet Res SP - e56795 VL - 26 KW - app-based KW - digital rehabilitation KW - internet-based intervention KW - lung cancer KW - perioperative pulmonary rehabilitation KW - systematic review KW - telerehabilitation N2 - Background: Pulmonary rehabilitation (PR) has been shown to effectively support postsurgical recovery in patients with lung cancer (LC) at various stages. While digital PR programs offer a potential solution to traditional challenges, such as time and space constraints, their efficacy and feasibility for patients undergoing LC surgery remain unclear. Objective: This systematic review aims to assess the feasibility and effectiveness of digital PR programs for individuals undergoing LC surgery. Methods: A systematic review was conducted, retrieving data from 6 English and 4 Chinese databases from their inception to January 1, 2024. References in related studies were also manually reviewed. The primary outcomes assessed were physical capacity, lung function, and the incidence of postoperative pulmonary complications (PPCs). The secondary outcomes were compliance, hospital stay, chest tube duration, anxiety, depression, and quality of life. Where applicable, recruitment and withdrawal rates were also evaluated. Meta-analysis and descriptive analysis were used to assess the outcomes. Results: A total of 5 randomized controlled trials and 6 quasi-experimental studies (n=1063) were included, with 4 studies being included in the meta-analyses. Our meta-analyses revealed that digital PR reduced the decline in 6-minute walk distance (6-MWD) by an average of 15 m compared with routine PR programs from admission to discharge, demonstrating a clinically significant improvement in physical capacity (mean difference ?15.00, 95% CI ?25.65 to ?4.34, P=.006). Additionally, digital PR was associated with a reduction (26/58, 45%) in the likelihood of PPCs (risk ratio 0.45, 95% CI 0.30-0.66, P<.001) and a reduction of 1.53 days in chest tube duration (mean difference ?1.53, 95% CI ?2.95 to ?0.12, P=.03), without a statistically significant effect on postoperative hospital stay (mean difference ?1.42, 95% CI ?3.45 to 0.62, P=.17). Descriptive analyses suggested that digital PR has the potential to improve knowledge, lung function, quality of life, and self-efficacy, while reducing depression and anxiety. Notably, digital PR was found to be a safe, feasible, and acceptable supplementary intervention. Despite challenges with low recruitment, digital PR enhanced exercise compliance, increased patient satisfaction, and lowered dropout rates. Conclusions: This systematic review is the first comprehensive analysis to suggest that digital PR is a safe, feasible, acceptable, and effective intervention for promoting recovery in patients with LC after surgery. Digital PR has the potential to be a valuable supplement, expanding access to traditional PR programs. Future research should prioritize the development of interactive and inclusive digital solutions tailored to diverse age groups and educational backgrounds. Rigorous studies, including large-scale, high-quality randomized controlled trials with detailed protocols and robust methodologies, are needed to assess the short-, medium-, and long-term efficacy of digital PR, ensuring reproducibility in future research. Trial Registration: PROSPERO CRD42023430271; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=430271 UR - https://www.jmir.org/2024/1/e56795 UR - http://dx.doi.org/10.2196/56795 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/56795 ER - TY - JOUR AU - Gao, Zhiqiang AU - Teng, Jiajun AU - Qiao, Rong AU - Qian, Jialin AU - Pan, Feng AU - Ma, Meili AU - Lu, Jun AU - Zhang, Bo AU - Chu, Tianqing AU - Zhong, Hua PY - 2024/11/8 TI - Efficacy and Safety of a Therapy Combining Sintilimab and Chemotherapy With Cryoablation in the First-Line Treatment of Advanced Nonsquamous Non?Small Cell Lung Cancer: Protocol for a Phase II, Pilot, Single-Arm, Single-Center Study JO - JMIR Res Protoc SP - e64950 VL - 13 KW - cryoablation KW - immunotherapy KW - nonsquamous non?small cell lung cancer N2 - Background: Immunotherapy has significantly advanced lung cancer treatment, particularly in nonsquamous non?small cell lung cancer (NSCLC), with overall response rates between 50% and 60%. However, about 30% of patients only achieve a stable disease state. Cryoablation has shown potential to enhance immunotherapy by modifying the tumor?s immune microenvironment through the release of antigens and immune factors. Addressing how to boost the immune response in these patients is critical. Objective: This study aims to investigate the efficacy and safety of immunochemotherapy in combination with cryoablation as a first-line treatment for advanced NSCLC. Methods: This is a phase II, pilot, open-label, single arm, single center, interventional study. Patients with stage IIIB to IIIC or IV NSCLC with T staging ranging from T1 to T2b will receive sintilimab (200 mg/m2 every 3 weeks) and chemotherapy. After 2 cycles, the feasibility of cryoablation will be considered for those with stable disease by a multidisciplinary team. Cryoablation with 3 freeze-thaw cycles will be performed for the main lesion. The third cycle of systemic therapy will begin 7 (SD 3) days after cryoablation. A total of 20 patients will be enrolled. Treatment will continue until the disease progresses, there is unacceptable toxicity, a participant withdraws consent, other discontinuation criteria are met, or the study reaches completion. The primary objective is to assess progression-free survival (PFS). The secondary objective is to assess efficacy through duration of response, disease control rate, overall survival (OS), and the safety profile. The exploratory objective is to investigate and compare immune factor changes after 2 cycles of immunochemotherapy and at 1, 3, and 7 days after cryoablation. Survival time will be estimated using the Kaplan-Meier method to calculate median PFS and OS. Any adverse events that occur during the trial will be promptly recorded. Results: The project was funded in 2024, and enrollment will be completed in 2025. The first results are expected to be submitted for publication in 2027. Conclusions: This study will provide evidence for the efficacy and safety of the combination of immunochemotherapy and cryoablation as a first-line treatment for advanced NSCLC. Although it has a limited sample size, the findings of this study will be used in the future to inform the design of a fully powered, 2-arm, larger-scale study. Trial Registration: ClinicalTrials.gov NCT06483009; https://clinicaltrials.gov/study/NCT06483009 International Registered Report Identifier (IRRID): PRR1-10.2196/64950 UR - https://www.researchprotocols.org/2024/1/e64950 UR - http://dx.doi.org/10.2196/64950 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/64950 ER - TY - JOUR AU - Bertolaccini, Luca AU - Ciani, Oriana AU - Lucchi, Marco AU - Zaraca, Francesco AU - Bertani, Alessandro AU - Crisci, Roberto AU - Spaggiari, Lorenzo AU - PY - 2024/10/8 TI - Outcomes of Patients With Early and Locally Advanced Lung Cancer: Protocol for the Italian Lung Cancer Observational Study (LUCENT) JO - JMIR Res Protoc SP - e57183 VL - 13 KW - lung cancer KW - quality of life KW - observational study KW - economic aspects KW - multicenter study N2 - Background: Lung cancer, predominantly non-small cell lung cancer (NSCLC), remains a formidable challenge, necessitating an in-depth understanding of evolving treatment paradigms. The Italian Lung Cancer Observational Study (LUCENT) addresses this need by investigating the outcomes of patients with early and locally advanced lung cancer in Italy. Objective: With a focus on real-world data and patient registries, this study aims to provide comprehensive insights into clinical, psychosocial, and economic impacts, contributing to informed decision-making in health care. Methods: LUCENT is a prospective observational multicenter cohort study enrolling patients eligible for minimally invasive manual, robot-assisted, or traditional open surgery. The study will develop a web-based registry to collect longitudinal surgical, oncological, and socioeconomic outcome data. The primary objectives include performance assessment through the establishment of national benchmarks based on risk-adjusted outcomes and processes of care indicators. The secondary objectives encompass economic and psychosocial impact assessments of innovative technologies and treatment pathways. The multicenter design ensures a diverse and representative study population. Results: The evolving landscape of NSCLC treatment necessitates a nuanced approach with consideration of the dynamic shifts in therapeutic strategies. LUCENT strives to fill existing knowledge gaps by providing a platform for collecting and analyzing real-world data, emphasizing the importance of patient-reported outcomes in enhancing the understanding of the disease. By developing a web-based registry, the study not only facilitates efficient data collection but also addresses the limitations of traditional methods, such as suboptimal response rates and costs associated with paper-and-pencil questionnaires. Recruitment will be conducted from January 01, 2024, to December 31, 2026. Follow-up will be performed for a minimum of 2 years. The study will be completed in the year 2028. Conclusions: LUCENT?s potential implications are substantial. Establishing national benchmarks will enable a thorough evaluation of outcomes and care processes, guiding clinicians and policymakers in optimizing patient management. Furthermore, the study?s secondary objectives, focusing on economic and psychosocial impacts, align with the contemporary emphasis on holistic cancer care. Insights gained from this study may influence treatment strategies, resource utilization, and patient well-being, thereby contributing to the ongoing refinement of lung cancer management. Trial Registration: ClinicalTrials.gov NCT05851755; https://clinicaltrials.gov/study/NCT05851755. ISRCTN 67197140; https://www.isrctn.com/ISRCTN67197140 International Registered Report Identifier (IRRID): PRR1-10.2196/57183 UR - https://www.researchprotocols.org/2024/1/e57183 UR - http://dx.doi.org/10.2196/57183 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/57183 ER - TY - JOUR AU - Gwon, Nahyun AU - Jeong, Wonjeong AU - Kim, Hyun Jee AU - Oh, Hee Kyoung AU - Jun, Kwan Jae PY - 2024/8/7 TI - Effects of Intervention Timing on Health-Related Fake News: Simulation Study JO - JMIR Form Res SP - e48284 VL - 8 KW - disinformation KW - fenbendazole KW - cancer information KW - simulation KW - fake news KW - online social networking KW - misinformation KW - lung cancer N2 - Background: Fake health-related news has spread rapidly through the internet, causing harm to individuals and society. Despite interventions, a fenbendazole scandal recently spread among patients with lung cancer in South Korea. It is crucial to intervene appropriately to prevent the spread of fake news. Objective: This study investigated the appropriate timing of interventions to minimize the side effects of fake news. Methods: A simulation was conducted using the susceptible-infected-recovered (SIR) model, which is a representative model of the virus spread mechanism. We applied this model to the fake news spread mechanism. The parameters were set similarly to those in the digital environment, where the fenbendazole scandal occurred. NetLogo, an agent-based model, was used as the analytical tool. Results: Fake news lasted 278 days in the absence of interventions. As a result of adjusting and analyzing the timing of the intervention in response to the fenbendazole scandal, we found that faster intervention leads to a shorter duration of fake news (intervention at 54 days = fake news that lasted for 210 days; intervention at 16 days = fake news that lasted for 187 days; and intervention at 10 days = fake news that lasted for 157 days). However, no significant differences were observed when the intervention was performed within 10 days. Conclusions: Interventions implemented within 10 days were effective in reducing the duration of the spread of fake news. Our findings suggest that timely intervention is critical for preventing the spread of fake news in the digital environment. Additionally, a monitoring system that can detect fake news should be developed for a rapid response UR - https://formative.jmir.org/2024/1/e48284 UR - http://dx.doi.org/10.2196/48284 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/48284 ER - TY - JOUR AU - Kirkpatrick, Suriya AU - Davey, Zoe AU - Wright, Richard Peter AU - Henshall, Catherine PY - 2024/7/26 TI - Supportive eHealth Technologies and Their Effects on Physical Functioning and Quality of Life for People With Lung Cancer: Systematic Review JO - J Med Internet Res SP - e53015 VL - 26 KW - lung cancer KW - physical activity KW - exercise KW - physical functioning KW - mobile technology KW - smartphone apps KW - digital health KW - mobile phone N2 - Background: Despite advancements in treatment and early diagnosis, people with lung cancer are not living as long as those with other cancers. The more common symptoms of lung cancer, such as breathlessness, fatigue, and depression, can be alleviated by improving patients? physical functioning. Therefore, good symptom management and improved health-related quality of life (HRQoL) are priorities in this patient group. However, current health care services have limited capacity to provide this support. One way to address this issue of health care resources is to empower patients to self-manage their condition using eHealth technologies. Objective: The purpose of this review was to identify and assess available research on technologies that support persons with lung cancer to improve or maintain their physical functioning, HRQoL, or both. Methods: Six databases?PubMed, Web of Science, CINAHL, MEDLINE, SPORTDiscus, and PsycINFO?were searched from January 1, 1990, to April 30, 2023. Studies were suitable for inclusion if the participants included people with lung cancer aged >18 years who had been exposed to a physical activity, exercise, or training intervention that was delivered via an electronic or web-based application with or without a comparator. Furthermore, the study had to report on the impact of the intervention on physical functioning and HRQoL. Studies that focused on telemedicine without a digital intervention were excluded. The Grading of Recommendations Assessment, Development, and Evaluation system was used to assess the quality of the included papers. Due to the heterogeneity of the studies, a narrative synthesis was undertaken. Results: This review is reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 794 papers were initially identified through our search, of which, after screening, 8 (1%) were confirmed suitable for inclusion in the review. As 2 (25%) of the 8 papers reported on different stages of the same study, we included only 7 studies in our analysis. The studies were undertaken between 2010 and 2018 across multiple countries and aimed to develop a technology and test its feasibility or acceptance. The 7 technologies identified included web-based applications, mobile apps, and gaming consoles. The studies demonstrated impact on walking distance, muscle strength, balance, dyspnea symptoms, and cancer-related fatigue. HRQoL scores also showed improvement. Conclusions: The findings indicate that eHealth technologies can positively impact physical functioning and well-being for people with lung cancer, but there are limited studies that demonstrate the impact of these digital interventions over longer periods. None of the studies reported on the implementation or adoption of a mobile health or eHealth intervention in routine clinical practice, highlighting the need for further research in this area. Trial Registration: PROSPERO CRD42023414094; https://tinyurl.com/39hhbwyx UR - https://www.jmir.org/2024/1/e53015 UR - http://dx.doi.org/10.2196/53015 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/53015 ER - TY - JOUR AU - Aye, Sin Phyu AU - Barnes, Joanne AU - Laking, George AU - Cameron, Laird AU - Anderson, Malcolm AU - Luey, Brendan AU - Delany, Stephen AU - Harris, Dean AU - McLaren, Blair AU - Brenman, Elliott AU - Wong, Jayden AU - Lawrenson, Ross AU - Arendse, Michael AU - Tin Tin, Sandar AU - Elwood, Mark AU - Hope, Philip AU - McKeage, James Mark PY - 2024/7/2 TI - Erlotinib or Gefitinib for Treating Advanced Epidermal Growth Factor Receptor Mutation?Positive Lung Cancer in Aotearoa New Zealand: Protocol for a National Whole-of-Patient-Population Retrospective Cohort Study and Results of a Validation Substudy JO - JMIR Res Protoc SP - e51381 VL - 13 KW - epidermal growth factor receptor KW - erlotinib KW - gefitinib KW - lung cancer KW - retrospective cohort KW - study protocol KW - validation N2 - Background: Starting in 2010, the epidermal growth factor receptor (EGFR) kinase inhibitors erlotinib and gefitinib were introduced into routine use in Aotearoa New Zealand (NZ) for treating advanced lung cancer, but their impact in this setting is unknown. Objective: The study described in this protocol aims to understand the effectiveness and safety of these new personalized lung cancer treatments and the contributions made by concomitant medicines and other factors to adverse outcomes in the general NZ patient population. A substudy aimed to validate national electronic health databases as the data source and the methods for determining patient eligibility and identifying outcomes and variables. Methods: This study will include all NZ patients with advanced EGFR mutation?positive lung cancer who were first dispensed erlotinib or gefitinib before October 1, 2020, and followed until death or for at least 1 year. Routinely collected health administrative and clinical data will be collated from national electronic cancer registration, hospital discharge, mortality registration, and pharmaceutical dispensing databases by deterministic data linkage using National Health Index numbers. The primary effectiveness and safety outcomes will be time to treatment discontinuation and serious adverse events, respectively. The primary variable will be high-risk concomitant medicines use with erlotinib or gefitinib. For the validation substudy (n=100), data from clinical records were compared to those from national electronic health databases and analyzed by agreement analysis for categorical data and by paired 2-tailed t tests for numerical data. Results: In the validation substudy, national electronic health databases and clinical records agreed in determining patient eligibility and for identifying serious adverse events, high-risk concomitant medicines use, and other categorical data with overall agreement and ? statistic of >90% and >0.8000, respectively; for example, for the determination of patient eligibility, the comparison of proxy and standard eligibility criteria applied to national electronic health databases and clinical records, respectively, showed overall agreement and ? statistic of 96% and 0.8936, respectively. Dates for estimating time to treatment discontinuation and other numerical variables and outcomes showed small differences, mostly with nonsignificant P values and 95% CIs overlapping with zero difference; for example, for the dates of the first dispensing of erlotinib or gefitinib, national electronic health databases and clinical records differed on average by approximately 4 days with a nonsignificant P value of .33 and 95% CIs overlapping with zero difference. As of May 2024, the main study is ongoing. Conclusions: A protocol is presented for a national whole-of-patient-population retrospective cohort study designed to describe the safety and effectiveness of erlotinib and gefitinib during their first decade of routine use in NZ for treating EGFR mutation?positive lung cancer. The validation substudy demonstrated the feasibility and validity of using national electronic health databases and the methods for determining patient eligibility and identifying the study outcomes and variables proposed in the study protocol. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12615000998549; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=368928 International Registered Report Identifier (IRRID): DERR1-10.2196/51381 UR - https://www.researchprotocols.org/2024/1/e51381 UR - http://dx.doi.org/10.2196/51381 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/51381 ER - TY - JOUR AU - Geeraerts, Joran AU - de Nooijer, Kim AU - Pivodic, Lara AU - De Ridder, Mark AU - Van den Block, Lieve PY - 2024/6/12 TI - Intensive Longitudinal Methods Among Adults With Breast or Lung Cancer: Scoping Review JO - J Med Internet Res SP - e50224 VL - 26 KW - diary KW - ecological momentary assessment KW - neoplasms KW - quality of life KW - self-report KW - telemedicine KW - scoping review KW - longitudinal methods KW - breast cancer KW - lung cancer KW - patients with cancer KW - cancer KW - intensive monitoring KW - advanced disease stages KW - mobile phone N2 - Background: Intensive longitudinal methods offer a powerful tool for capturing daily experiences of individuals. However, its feasibility, effectiveness, and optimal methodological approaches for studying or monitoring experiences of oncology patients remain uncertain. Objective: This scoping review aims to describe to what extent intensive longitudinal methods with daily electronic assessments have been used among patients with breast or lung cancer and with which methodologies, associated outcomes, and influencing factors. Methods: We searched the electronic databases (PubMed, Embase, and PsycINFO) up to January 2024 and included studies reporting on the use of these methods among adults with breast or lung cancer. Data were extracted on population characteristics, intensive monitoring methodologies used, study findings, and factors influencing the implementation of these methods in research and clinical practice. Results: We identified 1311 articles and included 52 articles reporting on 41 studies. Study aims and intensive monitoring methodologies varied widely, but most studies focused on measuring physical and psychological symptom constructs, such as pain, anxiety, or depression. Compliance and attrition rates seemed acceptable for most studies, although complete methodological reporting was often lacking. Few studies specifically examined these methods among patients with advanced cancer. Factors influencing implementation were linked to both patient (eg, confidence with intensive monitoring system) and methodology (eg, option to use personal devices). Conclusions: Intensive longitudinal methods with daily electronic assessments hold promise to provide unique insights into the daily lives of patients with cancer. Intensive longitudinal methods may be feasible among people with breast or lung cancer. Our findings encourage further research to determine optimal conditions for intensive monitoring, specifically in more advanced disease stages. UR - https://www.jmir.org/2024/1/e50224 UR - http://dx.doi.org/10.2196/50224 UR - http://www.ncbi.nlm.nih.gov/pubmed/38865186 ID - info:doi/10.2196/50224 ER - TY - JOUR AU - Zheng, Yue AU - Zhao, Ailin AU - Yang, Yuqi AU - Wang, Laduona AU - Hu, Yifei AU - Luo, Ren AU - Wu, Yijun PY - 2024/6/12 TI - Real-World Survival Comparisons Between Radiotherapy and Surgery for Metachronous Second Primary Lung Cancer and Predictions of Lung Cancer?Specific Outcomes Using Machine Learning: Population-Based Study JO - JMIR Cancer SP - e53354 VL - 10 KW - metachronous second primary lung cancer KW - radiotherapy KW - surgical resection KW - propensity score matching analysis KW - machine learning N2 - Background: Metachronous second primary lung cancer (MSPLC) is not that rare but is seldom studied. Objective: We aim to compare real-world survival outcomes between different surgery strategies and radiotherapy for MSPLC. Methods: This retrospective study analyzed data collected from patients with MSPLC between 1988 and 2012 in the Surveillance, Epidemiology, and End Results (SEER) database. Propensity score matching (PSM) analyses and machine learning were performed to compare variables between patients with MSPLC. Survival curves were plotted using the Kaplan-Meier method and were compared using log-rank tests. Results: A total of 2451 MSPLC patients were categorized into the following treatment groups: 864 (35.3%) received radiotherapy, 759 (31%) underwent surgery, 89 (3.6%) had surgery plus radiotherapy, and 739 (30.2%) had neither treatment. After PSM, 470 pairs each for radiotherapy and surgery were generated. The surgery group had significantly better survival than the radiotherapy group (P<.001) and the untreated group (563 pairs; P<.001). Further analysis revealed that both wedge resection (85 pairs; P=.004) and lobectomy (71 pairs; P=.002) outperformed radiotherapy in overall survival for MSPLC patients. Machine learning models (extreme gradient boosting, random forest classifier, adaptive boosting) demonstrated high predictive performance based on area under the curve (AUC) values. Least absolute shrinkage and selection operator (LASSO) regression analysis identified 9 significant variables impacting cancer-specific survival, emphasizing surgery?s consistent influence across 1 year to 10 years. These variables encompassed age at diagnosis, sex, year of diagnosis, radiotherapy of initial primary lung cancer (IPLC), primary site, histology, surgery, chemotherapy, and radiotherapy of MPSLC. Competing risk analysis highlighted lower mortality for female MPSLC patients (hazard ratio [HR]=0.79, 95% CI 0.71-0.87) and recent IPLC diagnoses (HR=0.79, 95% CI 0.73-0.85), while radiotherapy for IPLC increased mortality (HR=1.31, 95% CI 1.16-1.50). Surgery alone had the lowest cancer-specific mortality (HR=0.83, 95% CI 0.81-0.85), with sublevel resection having the lowest mortality rate among the surgical approaches (HR=0.26, 95% CI 0.21-0.31). The findings provide valuable insights into the factors that influence cumulative cancer-specific mortality. Conclusions: Surgical resections such as wedge resection and lobectomy confer better survival than radiation therapy for MSPLC, but radiation can be a valid alternative for the treatment of MSPLC. UR - https://cancer.jmir.org/2024/1/e53354 UR - http://dx.doi.org/10.2196/53354 UR - http://www.ncbi.nlm.nih.gov/pubmed/38865182 ID - info:doi/10.2196/53354 ER - TY - JOUR AU - Meng, Fan-Tsui AU - Jhuang, Jing-Rong AU - Peng, Yan-Teng AU - Chiang, Chun-Ju AU - Yang, Ya-Wen AU - Huang, Chi-Yen AU - Huang, Kuo-Ping AU - Lee, Wen-Chung PY - 2024/5/31 TI - Predicting Lung Cancer Survival to the Future: Population-Based Cancer Survival Modeling Study JO - JMIR Public Health Surveill SP - e46737 VL - 10 KW - lung cancer KW - survival KW - survivorship-period-cohort model KW - prediction KW - prognosis KW - early diagnosis KW - lung cancer screening KW - survival trend KW - population-based KW - population health KW - public health KW - surveillance KW - low-dose computed tomography N2 - Background: Lung cancer remains the leading cause of cancer-related mortality globally, with late diagnoses often resulting in poor prognosis. In response, the Lung Ambition Alliance aims to double the 5-year survival rate by 2025. Objective: Using the Taiwan Cancer Registry, this study uses the survivorship-period-cohort model to assess the feasibility of achieving this goal by predicting future survival rates of patients with lung cancer in Taiwan. Methods: This retrospective study analyzed data from 205,104 patients with lung cancer registered between 1997 and 2018. Survival rates were calculated using the survivorship-period-cohort model, focusing on 1-year interval survival rates and extrapolating to predict 5-year outcomes for diagnoses up to 2020, as viewed from 2025. Model validation involved comparing predicted rates with actual data using symmetric mean absolute percentage error. Results: The study identified notable improvements in survival rates beginning in 2004, with the predicted 5-year survival rate for 2020 reaching 38.7%, marking a considerable increase from the most recent available data of 23.8% for patients diagnosed in 2013. Subgroup analysis revealed varied survival improvements across different demographics and histological types. Predictions based on current trends indicate that achieving the Lung Ambition Alliance?s goal could be within reach. Conclusions: The analysis demonstrates notable improvements in lung cancer survival rates in Taiwan, driven by the adoption of low-dose computed tomography screening, alongside advances in diagnostic technologies and treatment strategies. While the ambitious target set by the Lung Ambition Alliance appears achievable, ongoing advancements in medical technology and health policies will be crucial. The study underscores the potential impact of continued enhancements in lung cancer management and the importance of strategic health interventions to further improve survival outcomes. UR - https://publichealth.jmir.org/2024/1/e46737 UR - http://dx.doi.org/10.2196/46737 UR - http://www.ncbi.nlm.nih.gov/pubmed/38819904 ID - info:doi/10.2196/46737 ER - TY - JOUR AU - Manz, R. Christopher AU - Schriver, Emily AU - Ferrell, J. William AU - Williamson, Joelle AU - Wakim, Jonathan AU - Khan, Neda AU - Kopinsky, Michael AU - Balachandran, Mohan AU - Chen, Jinbo AU - Patel, S. Mitesh AU - Takvorian, U. Samuel AU - Shulman, N. Lawrence AU - Bekelman, E. Justin AU - Barnett, J. Ian AU - Parikh, B. Ravi PY - 2024/5/17 TI - Association of Remote Patient-Reported Outcomes and Step Counts With Hospitalization or Death Among Patients With Advanced Cancer Undergoing Chemotherapy: Secondary Analysis of the PROStep Randomized Trial JO - J Med Internet Res SP - e51059 VL - 26 KW - wearables KW - accelerometers KW - patient-reported outcomes KW - step counts KW - oncology KW - accelerometer KW - patient-generated health data KW - cancer KW - death KW - chemotherapy KW - symptoms KW - gastrointestinal cancer KW - lung cancer KW - monitoring KW - symptom burden KW - risk KW - hospitalization KW - mobile phone N2 - Background: Patients with advanced cancer undergoing chemotherapy experience significant symptoms and declines in functional status, which are associated with poor outcomes. Remote monitoring of patient-reported outcomes (PROs; symptoms) and step counts (functional status) may proactively identify patients at risk of hospitalization or death. Objective: The aim of this study is to evaluate the association of (1) longitudinal PROs with step counts and (2) PROs and step counts with hospitalization or death. Methods: The PROStep randomized trial enrolled 108 patients with advanced gastrointestinal or lung cancers undergoing cytotoxic chemotherapy at a large academic cancer center. Patients were randomized to weekly text-based monitoring of 8 PROs plus continuous step count monitoring via Fitbit (Google) versus usual care.?This preplanned secondary analysis included 57 of 75 patients randomized to the intervention who had PRO and step count data. We analyzed the associations between PROs and mean daily step counts and the associations of PROs and step counts with the composite outcome of hospitalization or death using bootstrapped generalized linear models to account for longitudinal data. Results: Among 57 patients, the mean age was 57 (SD 10.9) years, 24 (42%) were female, 43 (75%) had advanced gastrointestinal cancer, 14 (25%) had advanced lung cancer, and 25 (44%) were hospitalized or died during follow-up. A 1-point weekly increase (on a 32-point scale) in aggregate PRO score was associated with 247 fewer mean daily steps (95% CI ?277 to ?213; P<.001). PROs most strongly associated with step count decline were patient-reported activity (daily step change ?892), nausea score (?677), and constipation score (524). A 1-point weekly increase in aggregate PRO score was associated with 20% greater odds of hospitalization or death (adjusted odds ratio [aOR] 1.2, 95% CI 1.1-1.4; P=.01). PROs most strongly associated with hospitalization or death were pain (aOR 3.2, 95% CI 1.6-6.5; P<.001), decreased activity (aOR 3.2, 95% CI 1.4-7.1; P=.01), dyspnea (aOR 2.6, 95% CI 1.2-5.5; P=.02), and sadness (aOR 2.1, 95% CI 1.1-4.3; P=.03). A decrease in 1000 steps was associated with 16% greater odds of hospitalization or death (aOR 1.2, 95% CI 1.0-1.3; P=.03). Compared with baseline, mean daily step count decreased 7% (n=274 steps), 9% (n=351 steps), and 16% (n=667 steps) in the 3, 2, and 1 weeks before hospitalization or death, respectively. Conclusions: In this secondary analysis of a randomized trial among patients with advanced cancer, higher symptom burden and decreased step count were independently associated with and predictably worsened close to hospitalization or death. Future interventions should leverage longitudinal PRO and step count data to target interventions toward patients at risk for poor outcomes. Trial Registration: ClinicalTrials.gov NCT04616768; https://clinicaltrials.gov/study/NCT04616768 International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2021-054675 UR - https://www.jmir.org/2024/1/e51059 UR - http://dx.doi.org/10.2196/51059 UR - http://www.ncbi.nlm.nih.gov/pubmed/38758583 ID - info:doi/10.2196/51059 ER - TY - JOUR AU - Ni, Chen-Xu AU - Lu, Wen-Jie AU - Ni, Min AU - Huang, Fang AU - Li, Dong-Jie AU - Shen, Fu-Ming PY - 2023/8/31 TI - Advanced Messaging Intervention for Medication Adherence and Clinical Outcomes Among Patients With Cancer: Randomized Controlled Trial JO - JMIR Cancer SP - e44612 VL - 9 KW - 5G messaging KW - fifth-generation KW - medication adherence KW - patients with cancer KW - clinical pharmacists KW - randomized controlled trial N2 - Background: Medication adherence is crucial for improving clinical outcomes in the treatment of patients with cancer. The lack of adherence and adverse drug reactions can reduce the effectiveness of cancer therapy including the quality of life. The commonly used intervention methods for medication adherence continue to evolve, and the age of fifth-generation (5G) messaging has arrived. Objective: In this study, we conducted a prospective, pilot randomized controlled trial to evaluate the effect of 5G messaging on medication adherence and clinical outcomes among patients with cancer in China. Methods: The research population was patients with nonsmall cell lung cancer undergoing pemetrexed chemotherapy who require regular folic acid (FA) and vitamin B12 supplements. The intervention and control groups were assigned to 5G messaging and second-generation (2G) messaging, respectively. The patients? medication adherence and quality of life were assessed at baseline and 1-month and 3-month time points. Moreover, the chemotherapy-related hematologic or nonhematologic toxicities, as well as the serum levels of FA and vitamin B12, were measured. Results: Of the 567 patients assessed for eligibility between January and May 2021, a total of 154 (27.2%) patients were included. Overall, 80 were randomized to the control group and 74 to the intervention group. The odds of adherence in the 5G messaging intervention group were significantly higher than the control group at the 1-month (62/69, 90% vs 56/74, 76%; adjusted odds ratio 2.67, 95% CI 1.02-7.71) and 3-month (50/60, 83% vs 48/64, 75%; adjusted odds ratio 2.36, 95% CI 1.00-5.23) time points. Correspondingly, the FA and vitamin B12 serum levels of patients in the 5G messaging group were higher than those of the control group. Regarding hematologic toxicities, only the incidence of leukopenia in the intervention group was lower than that in the control group (25/80, 31% in the control group vs 12/74, 16% in the intervention group; P=.04). There were no differences in nonhematologic toxicities and quality of life between the 2 groups. Conclusions: In summary, we conclude that compared with conventional 2G text-based messaging, a 5G messaging intervention can better improve medication adherence and clinical outcome among patients with cancer. Trial Registration: Chinese Clinical Trial Registry ChiCTR2200058188; https://www.chictr.org.cn/showproj.html?proj=164489 UR - https://cancer.jmir.org/2023/1/e44612 UR - http://dx.doi.org/10.2196/44612 UR - http://www.ncbi.nlm.nih.gov/pubmed/37651170 ID - info:doi/10.2196/44612 ER - TY - JOUR AU - Booth, Alison AU - Manson, Stephanie AU - Halhol, Sonia AU - Merinopoulou, Evie AU - Raluy-Callado, Mireia AU - Hareendran, Asha AU - Knoll, Stefanie PY - 2023/7/12 TI - Using Health-Related Social Media to Understand the Experiences of Adults With Lung Cancer in the Era of Immuno-Oncology and Targeted Therapies: Observational Study JO - JMIR Cancer SP - e45707 VL - 9 KW - non-small cell lung cancer KW - data science KW - machine learning KW - natural language processing KW - social media data KW - patient experience KW - patient preference KW - immunotherapy KW - targeted therapies KW - lung cancer KW - social media N2 - Background: The treatment of non?small cell lung cancer (NSCLC) has evolved dramatically with the approval of immuno-oncology (IO) and targeted therapies (TTs). Insights on the patient experience with these therapies and their impacts are lacking. Health-related social media has been increasingly used by patients to share their disease and treatment experiences, thus representing a valuable source of real-world data to understand the patient?s voice and uncover potential unmet needs. Objective: This study aimed to describe the experiences of patients with NSCLC as reported in discussions posted on lung cancer?specific social media with respect to their disease symptoms and associated impacts. Methods: Publicly available posts (2010-2019) were extracted from selected lung cancer? or NSCLC-specific websites. Social media users (patients and caregivers posting on these websites) were stratified by metastatic- and adjuvant-eligible subgroups and treatment received using natural language processing (NLP) and machine learning methods. Automated identification of symptoms was conducted using NLP. Qualitative data analysis (QDA) was conducted on random samples of posts mentioning pain-related, fatigue-related, respiratory-related, or infection-related symptoms to capture the patient experience with these and associated impacts. Results: Overall, 1724 users (50,390 posts) and 574 users (4531 posts) were included in the metastatic group and adjuvant group, respectively. Among users in the metastatic group, pain, discomfort, and fatigue were the most commonly mentioned symptoms (49.7% and 39.6%, respectively), and in the QDA (258 posts from 134 users), the most frequent impacts related to physical impairments, sleep, and eating habits. Among users in the adjuvant group, pain, discomfort, and respiratory symptoms were the most commonly mentioned (44.8% and 23.9%, respectively), and impacts identified in the QDA (154 posts from 92 users) were mostly related to physical functioning. Conclusions: Findings from this exploratory observational analysis of social media among patients and caregivers informed the lived experience of NSCLC in the era of novel therapies, shedding light on most reported symptoms and their impacts. These findings can be used to inform future research on NSCLC treatment development and patient management. UR - https://cancer.jmir.org/2023/1/e45707 UR - http://dx.doi.org/10.2196/45707 UR - http://www.ncbi.nlm.nih.gov/pubmed/37436789 ID - info:doi/10.2196/45707 ER - TY - JOUR AU - Hirsch, A. Erin AU - Studts, L. Jamie PY - 2023/4/14 TI - Using User-Centered Design to Facilitate Adherence to Annual Lung Cancer Screening: Protocol for a Mixed Methods Study for Intervention Development JO - JMIR Res Protoc SP - e46657 VL - 12 KW - health information processing KW - intervention design KW - lung cancer KW - lung cancer screening KW - LCS KW - mixed methods KW - photovoice KW - user-centered design N2 - Background: Lung cancer is the leading cause of cancer-related death in the United States, with the majority of lung cancer occurrence diagnosed after the disease has already metastasized. Lung cancer screening (LCS) with low-dose computed tomography can diagnose early-stage disease, especially when eligible individuals participate in screening on a yearly basis. Unfortunately, annual adherence has emerged as a challenge for academic and community screening programs, endangering the individual and population health benefits of LCS. Reminder messages have effectively increased adherence rates in breast, colorectal, and cervical cancer screenings but have not been tested with LCS participants who experience unique barriers to screening associated with the stigma of smoking and social determinants of health. Objective: This research aims to use a theory-informed, multiphase, and mixed methods approach with LCS experts and participants to develop a set of clear and engaging reminder messages to support LCS annual adherence. Methods: In aim 1, survey data informed by the Cognitive-Social Health Information Processing model will be collected to assess how LCS participants process health information aimed at health protective behavior to develop content for reminder messages and pinpoint options for message targeting and tailoring. Aim 2 focuses on identifying themes for message imagery through a modified photovoice activity that asks participants to identify 3 images that represent LCS and then participate in an interview about the selection, likes, and dislikes of each photo. A pool of candidate messages for multiple delivery platforms will be developed in aim 3, using results from aim 1 for message content and aim 2 for imagery selection. The refinement of message content and imagery combinations will be completed through iterative feedback from LCS experts and participants. Results: Data collection began in July 2022 and will be completed by May 2023. The final reminder message candidates are expected to be completed by June 2023. Conclusions: This project proposes a novel approach to facilitate adherence to annual LCS through the development of reminder messages that embrace content and imagery representative of the target population directly in the design process. Developing effective strategies to increase LCS adherence is instrumental in achieving optimal LCS outcomes at individual and population health levels. International Registered Report Identifier (IRRID): DERR1-10.2196/46657 UR - https://www.researchprotocols.org/2023/1/e46657 UR - http://dx.doi.org/10.2196/46657 UR - http://www.ncbi.nlm.nih.gov/pubmed/37058339 ID - info:doi/10.2196/46657 ER - TY - JOUR AU - Luo, Ganfeng AU - Zhang, Yanting AU - Etxeberria, Jaione AU - Arnold, Melina AU - Cai, Xiuyu AU - Hao, Yuantao AU - Zou, Huachun PY - 2023/2/17 TI - Projections of Lung Cancer Incidence by 2035 in 40 Countries Worldwide: Population-Based Study JO - JMIR Public Health Surveill SP - e43651 VL - 9 KW - lung cancer KW - incidence KW - projections KW - temporal trends KW - worldwide N2 - Background: The global burden of lung cancer (LC) is increasing. Quantitative projections of the future LC burden in different world regions could help optimize the allocation of resources and provide a benchmark for evaluating LC prevention and control interventions. Objective: We aimed to predict the future incidence of LC in 40 countries by 2035, with an emphasis on country- and sex-specific disparities. Methods: Data on LC incidence from 1978 to 2012 were extracted from 126 cancer registries of 40 countries in Cancer Incidence in Five Continents Volumes V-XI and used for the projection. Age-standardized incidence rates (ASRs) per 100,000 person-years and the number of incident cases were predicted through 2035, using the NORDPRED age-period-cohort model. Results: Global ASRs of the 40 studied countries were predicted to decrease by 23% (8.2/35.8) among males, from 35.8 per 100,000 person-years in 2010 to 27.6 in 2035, and increase by 2% (0.3/16.8) among females, from 16.8 in 2010 to 17.1 in 2035. The ASRs of LC among females are projected to continue increasing dramatically in most countries by 2035, with peaks after the 2020s in most European, Eastern Asian, and Oceanian countries, whereas the ASRs among males will continue to decline in almost all countries. The ASRs among females are predicted to almost reach those among males in Ireland, Norway, the United Kingdom, the Netherlands, Canada, the United States, and New Zealand in 2025 and in Slovenia in 2035 and even surpass those among males in Denmark in 2020 and in Brazil and Colombia in 2025. In 2035, the highest ASRs are projected to occur among males in Belarus (49.3) and among females in Denmark (36.8). The number of new cases in 40 countries is predicted to increase by 65.32% (858,000/1,314,000), from 1.31 million in 2010 to 2.17 million in 2035. China will have the largest number of new cases. Conclusions: LC incidence is expected to continue to increase through 2035 in most countries, making LC a major public health challenge worldwide. The ongoing transition in the epidemiology of LC highlights the need for resource redistribution and improved LC control measures to reduce future LC burden worldwide. UR - https://publichealth.jmir.org/2023/1/e43651 UR - http://dx.doi.org/10.2196/43651 UR - http://www.ncbi.nlm.nih.gov/pubmed/36800235 ID - info:doi/10.2196/43651 ER - TY - JOUR AU - Varriale, Pasquale AU - Müller, Borna AU - Katz, Grégory AU - Dallas, Lorraine AU - Aguaron, Alfonso AU - Azoulai, Marion AU - Girard, Nicolas PY - 2023/1/26 TI - Patient Perspectives on Value Dimensions of Lung Cancer Care: Cross-sectional Web-Based Survey JO - JMIR Form Res SP - e37190 VL - 7 KW - lung KW - cancer KW - health quality of life KW - patient reported outcome KW - PROM KW - economic burden KW - cost KW - economic KW - burden KW - perspective KW - survey KW - QoL KW - quality of life KW - questionnaire KW - caregiver KW - caregiving KW - physical well-being KW - end of life KW - palliative KW - physical function KW - independence KW - distress N2 - Background: While the lung cancer (LC) treatment landscape has rapidly evolved in recent years, easing symptom burden and treatment side effects remain central considerations in disease control. Objective: The aim of this study was to assess the relative importance of dimensions of LC care to patients, and to explore the disease burden, including socioeconomic aspects not commonly covered in patient-reported outcomes instruments. Methods: A questionnaire was sent to patients with LC and their caregivers to rate the value of a diverse set of quality of life dimensions in care, to evaluate communication between health care professionals (HCPs) and patients, and to explore the economic impact on respondents. The survey included questions on the dimensions of care covered by patient-reported outcomes instruments for quality-of-life evaluation (Functional Assessment of Cancer Therapy?Lung scale, EQ-5D, the European Organization for Research and Treatment of Cancer?s Core Quality of Life questionnaire, and the European Organization for Research and Treatment of Cancer?s Core Quality of Life in lung cancer 13-item questionnaire), as well as the International Consortium for Health Outcomes Measurement (ICHOM) standard set of patient-centered outcomes for LC. The survey respondents were participants on Carenity?s patient community platform, living either in France, the United Kingdom, Germany, Italy, or Spain. Results: The survey included 150 respondents (115 patients and 35 caregivers). ?Physical well-being? and ?end-of-life care? (median scores of 9.6, IQR 7.7-10, and 9.7, IQR 8.0-10, on a 10-point scale) were rated highest among the different value dimensions assessed. ?Physical well-being and functioning? was the dimension most frequently discussed with health care professionals (82/150, 55%), while only (17/100, 17%) reported discussing ?end-of-life care.? After diagnosis, 43% (49/112) of patients younger than 65 years stopped working. Among respondents who indicated their monthly household income before and after diagnosis, 55% (38/69) reported a loss of income. Conclusions: Our results showed the relevance of a broad range of aspects of care for the quality of life of patients with LC. End-of-life care was the dimension of care rated highest by patients with LC, irrespective of stage at diagnosis; however, this aspect is least frequently discussed with HCPs. The results also highlight the considerable socioeconomic impact of the disease, despite insurance coverage of direct costs. UR - https://formative.jmir.org/2023/1/e37190 UR - http://dx.doi.org/10.2196/37190 UR - http://www.ncbi.nlm.nih.gov/pubmed/36416499 ID - info:doi/10.2196/37190 ER - TY - JOUR AU - Guo, Lanwei AU - Meng, Qingcheng AU - Zheng, Liyang AU - Chen, Qiong AU - Liu, Yin AU - Xu, Huifang AU - Kang, Ruihua AU - Zhang, Luyao AU - Liu, Shuzheng AU - Sun, Xibin AU - Zhang, Shaokai PY - 2023/1/6 TI - Lung Cancer Risk Prediction Nomogram in Nonsmoking Chinese Women: Retrospective Cross-sectional Cohort Study JO - JMIR Public Health Surveill SP - e41640 VL - 9 KW - lung cancer KW - risk model KW - forecasting KW - validation KW - female KW - nonsmokers N2 - Background: It is believed that smoking is not the cause of approximately 53% of lung cancers diagnosed in women globally. Objective: The study aimed to develop and validate a simple and noninvasive model that could assess and stratify lung cancer risk in nonsmoking Chinese women. Methods: Based on the population-based Cancer Screening Program in Urban China, this retrospective, cross-sectional cohort study was carried out with a vast population base and an immense number of participants. The training set and the validation set were both constructed using a random distribution of the data. Following the identification of associated risk factors by multivariable Cox regression analysis, a predictive nomogram was developed. Discrimination (area under the curve) and calibration were further performed to assess the validation of risk prediction nomogram in the training set, which was then validated in the validation set. Results: In sum, 151,834 individuals signed up to take part in the survey. Both the training set (n=75,917) and the validation set (n=75,917) were comprised of randomly selected participants. Potential predictors for lung cancer included age, history of chronic respiratory disease, first-degree family history of lung cancer, menopause, and history of benign breast disease. We displayed 1-year, 3-year, and 5-year lung cancer risk?predicting nomograms using these 5 factors. In the training set, the 1-year, 3-year, and 5-year lung cancer risk areas under the curve were 0.762, 0.718, and 0.703, respectively. In the validation set, the model showed a moderate predictive discrimination. Conclusions: We designed and validated a simple and noninvasive lung cancer risk model for nonsmoking women. This model can be applied to identify and triage people at high risk for developing lung cancers among nonsmoking women. UR - https://publichealth.jmir.org/2023/1/e41640 UR - http://dx.doi.org/10.2196/41640 UR - http://www.ncbi.nlm.nih.gov/pubmed/36607729 ID - info:doi/10.2196/41640 ER - TY - JOUR AU - Zhao, Zixuan AU - Du, Lingbin AU - Li, Yuanyuan AU - Wang, Le AU - Wang, Youqing AU - Yang, Yi AU - Dong, Hengjin PY - 2022/7/6 TI - Cost-Effectiveness of Lung Cancer Screening Using Low-Dose Computed Tomography Based on Start Age and Interval in China: Modeling Study JO - JMIR Public Health Surveill SP - e36425 VL - 8 IS - 7 KW - cost-effectiveness analysis KW - low-dose computed tomography KW - screening KW - lung cancer KW - China N2 - Background: Lung cancer is the most commonly diagnosed cancer and the leading cause of cancer-related death in China. The effectiveness of screening for lung cancer has been reported to reduce lung cancer?specific and overall mortality, although the cost-effectiveness, optimal start age, and screening interval remain unclear. Objective: This study aimed to assess the cost-effectiveness of lung cancer screening among heavy smokers in China by incorporating start age and screening interval. Methods: A Markov state-transition model was used to assess the cost-effectiveness of a lung cancer screening program in China. The evaluated screening strategies were based on a screening start age of 50-74 years and a screening interval of once or annually. Transition probabilities were obtained from the literature and validated, while cost parameters were derived from databases of local medical insurance bureaus. A societal perspective was adopted. The outputs of the model included costs, quality-adjusted life years (QALYs), and lung cancer?specific mortality, with future costs and outcomes discounted by 5%. A currency exchange rate of 1 CNY=0.1557 USD is applicable. The incremental cost-effectiveness ratio (ICER) was calculated for different screening strategies relative to nonscreening. Results: The proposed model suggested that screening led to a gain of 0.001-0.042 QALYs per person as compared with the findings in the nonscreening cohort. Meanwhile, one-time and annual screenings were associated with reductions in lung cancer?related mortality of 0.004%-1.171% and 6.189%-15.819%, respectively. The ICER ranged from 119,974.08 to 614,167.75 CNY per QALY gained relative to nonscreening. Using the World Health Organization threshold of 212,676 CNY per QALY gained, annual screening from a start age of 55 years and one-time screening from the age of 65 years can be considered as cost-effective in China. Deterministic and probabilistic sensitivity analyses were conducted. Conclusions: This economic evaluation revealed that a population-based lung cancer screening program in China for heavy smokers using low-dose computed tomography was cost-effective for annual screening of smokers aged 55-74 years and one-time screening of those aged 65-74 years. Moreover, annual lung cancer screening should be promoted in China to realize the benefits of a guideline-recommended screening program. UR - https://publichealth.jmir.org/2022/7/e36425 UR - http://dx.doi.org/10.2196/36425 UR - http://www.ncbi.nlm.nih.gov/pubmed/35793127 ID - info:doi/10.2196/36425 ER - TY - JOUR AU - Ye, Wenjing AU - Lu, Weiwei AU - Li, Xiaopan AU - Chen, Yichen AU - Wang, Lin AU - Zeng, Guangwang AU - Xu, Cheng AU - Ji, Chen AU - Cai, Yuyang AU - Yang, Ling AU - Luo, Zheng PY - 2022/4/20 TI - Long-term Changes in the Premature Death Rate in Lung Cancer in a Developed Region of China: Population-based Study JO - JMIR Public Health Surveill SP - e33633 VL - 8 IS - 4 KW - lung cancer KW - mortality KW - years of life lost KW - trend analysis KW - decomposition method N2 - Background: Lung cancer is a leading cause of death worldwide, and its incidence shows an upward trend. A study of the long-term changes in the premature death rate in lung cancer in a developed region of China has great exploratory significance to further clarify the effectiveness of intervention measures. Objective: This study examined long-term changes in premature lung cancer death rates in order to understand the changes in mortality and to design future prevention plans in Pudong New Area (PNA), Shanghai, China. Methods: Cancer death data were collected from the Mortality Registration System of PNA. We analyzed the crude mortality rate (CMR), age-standardized mortality rate by Segi?s world standard population (ASMRW), and years of life lost (YLL) of patients with lung cancer from 1973 to 2019. Temporal trends in the CMR, ASMRW, and YLL rate were calculated by joinpoint regression expressed as an average annual percentage change (AAPC) with the corresponding 95% CI. Results: All registered permanent residents in PNA (80,543,137 person-years) from 1973 to 2019 were enrolled in this study. There were 42,229 deaths from lung cancer. The CMR and ASMRW were 52.43/105 and 27.79/105 person-years, respectively. The YLL due to premature death from lung cancer was 481779.14 years, and the YLL rate was 598.16/105 person-years. The CMR and YLL rate showed significantly increasing trends in men, women, and the total population (P<.001). The CMR of the total population increased by 2.86% (95% CI 2.66-3.07, P<.001) per year during the study period. The YLL rate increased with an AAPC of 2.21% (95% CI 1.92-2.51, P<.001) per year. The contribution rates of increased CMR values caused by demographic factors were more evident than those caused by nondemographic factors. Conclusions: Lung cancer deaths showed an increasing trend in PNA from 1973 to 2019. Demographic factors, such as the aging population, contributed more to an increased CMR. Our research can help us understand the changes in lung cancer mortality and can be used for similar cities in designing future prevention plans. UR - https://publichealth.jmir.org/2022/4/e33633 UR - http://dx.doi.org/10.2196/33633 UR - http://www.ncbi.nlm.nih.gov/pubmed/35442209 ID - info:doi/10.2196/33633 ER - TY - JOUR AU - Lowery, Julie AU - Fagerlin, Angela AU - Larkin, R. Angela AU - Wiener, S. Renda AU - Skurla, E. Sarah AU - Caverly, J. Tanner PY - 2022/4/1 TI - Implementation of a Web-Based Tool for Shared Decision-making in Lung Cancer Screening: Mixed Methods Quality Improvement Evaluation JO - JMIR Hum Factors SP - e32399 VL - 9 IS - 2 KW - shared decision-making KW - lung cancer KW - screening KW - clinical decision support KW - academic detailing KW - quality improvement KW - implementation N2 - Background: Lung cancer risk and life expectancy vary substantially across patients eligible for low-dose computed tomography lung cancer screening (LCS), which has important consequences for optimizing LCS decisions for different patients. To account for this heterogeneity during decision-making, web-based decision support tools are needed to enable quick calculations and streamline the process of obtaining individualized information that more accurately informs patient-clinician LCS discussions. We created DecisionPrecision, a clinician-facing web-based decision support tool, to help tailor the LCS discussion to a patient?s individualized lung cancer risk and estimated net benefit. Objective: The objective of our study is to test two strategies for implementing DecisionPrecision in primary care at eight Veterans Affairs medical centers: a quality improvement (QI) training approach and academic detailing (AD). Methods: Phase 1 comprised a multisite, cluster randomized trial comparing the effectiveness of standard implementation (adding a link to DecisionPrecision in the electronic health record vs standard implementation plus the Learn, Engage, Act, and Process [LEAP] QI training program). The primary outcome measure was the use of DecisionPrecision at each site before versus after LEAP QI training. The second phase of the study examined the potential effectiveness of AD as an implementation strategy for DecisionPrecision at all 8 medical centers. Outcomes were assessed by comparing absolute tool use before and after AD visits and conducting semistructured interviews with a subset of primary care physicians (PCPs) following the AD visits. Results: Phase 1 findings showed that sites that participated in the LEAP QI training program used DecisionPrecision significantly more often than the standard implementation sites (tool used 190.3, SD 174.8 times on average over 6 months at LEAP sites vs 3.5 SD 3.7 at standard sites; P<.001). However, this finding was confounded by the lack of screening coordinators at standard implementation sites. In phase 2, there was no difference in the 6-month tool use between before and after AD (95% CI ?5.06 to 6.40; P=.82). Follow-up interviews with PCPs indicated that the AD strategy increased provider awareness and appreciation for the benefits of the tool. However, other priorities and limited time prevented PCPs from using them during routine clinical visits. Conclusions: The phase 1 findings did not provide conclusive evidence of the benefit of a QI training approach for implementing a decision support tool for LCS among PCPs. In addition, phase 2 findings showed that our light-touch, single-visit AD strategy did not increase tool use. To enable tool use by PCPs, prediction-based tools must be fully automated and integrated into electronic health records, thereby helping providers personalize LCS discussions among their many competing demands. PCPs also need more time to engage in shared decision-making discussions with their patients. Trial Registration: ClinicalTrials.gov NCT02765412; https://clinicaltrials.gov/ct2/show/NCT02765412 UR - https://humanfactors.jmir.org/2022/2/e32399 UR - http://dx.doi.org/10.2196/32399 UR - http://www.ncbi.nlm.nih.gov/pubmed/35363144 ID - info:doi/10.2196/32399 ER - TY - JOUR AU - Alnefaie, Majed AU - Alamri, Abdullah AU - Saeedi, Asalh AU - Althobaiti, Awwadh AU - Alosaimi, Shahad AU - Alqurashi, Yousuf AU - Marzouki, Hani AU - Merdad, Mazin PY - 2022/3/18 TI - Pulmonary Screening Practices of Otolaryngology?Head and Neck Surgeons Across Saudi Arabia in the Posttreatment Surveillance of Squamous Cell Carcinoma: Cross-sectional Survey Study JO - Interact J Med Res SP - e24592 VL - 11 IS - 1 KW - squamous cell carcinoma of head and neck KW - lung neoplasms KW - radiography KW - otolaryngology KW - surgeons KW - survey N2 - Background: With respect to patients with head and neck squamous cell carcinoma (HNSCC), posttreatment surveillance for distant disease has mostly focused on the lungs, as HNSCC distant metastasis occurs in this organ in 90% of HNSCC cases. Additionally, the incidence rate of primary tumors in the lungs is high due to the field cancerization of the entire upper aerodigestive tract. Objective: Our cross-sectional survey study aims to evaluate the current beliefs and pulmonary screening practices of otolaryngology?head and neck surgeons across Saudi Arabia with respect to the posttreatment surveillance of HNSCC. Methods: This nationwide cross-sectional survey was conducted among head and neck surgeon members of the Saudi Society of Otolaryngology from June 1 to June 30, 2020. A predesigned questionnaire was used for data collection, and a descriptive analysis was carried out. Results: This study included 22 participants and had a 78% (22/28) response rate. This study found that the majority of participants (9/22, 41%) used lung radiography for routine lung screening during posttreatment follow-ups, whereas 32% (7/22) used low-dose computed tomography (CT; 7/22, 32%). With regard to the number of years for which participants perform lung screening during follow-ups, the majority of participants (17/22, 77%) reported 5 years, and only 9% (2/22) have performed lifelong lung screening. With regard to the frequency of lung screening, 77% (17/22) of participants conduct screening annually, 18% (4/22) conduct screening half-yearly, and 5% (1/22) conduct screening biennially. With regard to beliefs about the effectiveness of screening procedures in reducing lung cancer mortality rates during follow-ups, 36% (8/22) of participants believed them to be very effective or somewhat effective, 18% (4/22) did not know, and only 9% (2/22) believed that they were not effective. Conclusions: The participants mainly used lung radiography (9/22, 41%), low-dose CT (7/22, 32%), or positron emission tomography/CT (6/22, 27%) as a routine lung screening method during the posttreatment follow-up of patients with head and neck cancer for 5 years (17/22, 77%) or 10 years (3/22, 14%), and only a small percentage of participants have performed lifelong lung screening (2/22, 9%). Lung screening was mostly conducted annually or half-yearly. Such screening was believed to be very effective or somewhat effective. UR - https://www.i-jmr.org/2022/1/e24592 UR - http://dx.doi.org/10.2196/24592 UR - http://www.ncbi.nlm.nih.gov/pubmed/35302511 ID - info:doi/10.2196/24592 ER - TY - JOUR AU - Penz, Dianne Erika AU - Fenton, John Benjamin AU - Hu, Nianping AU - Marciniuk, Darcy PY - 2022/3/4 TI - Economic Burden of Chronic Obstructive Pulmonary Disease and Lung Cancer Between 2000 and 2015 in Saskatchewan: Study Protocol JO - JMIR Res Protoc SP - e31350 VL - 11 IS - 3 KW - lung cancer KW - COPD KW - chronic obstructive pulmonary disease KW - productivity loss KW - years of life lost KW - premature years of life lost KW - working years lost KW - economic burden of disease KW - lung disease KW - health economics KW - Stats Canada KW - epidemiology KW - pulmonary disease KW - pulmonary health KW - disease burden N2 - Background: Chronic obstructive pulmonary disease (COPD) and lung cancer are both detrimental diseases that present great burdens on society. Years of life lost (YLL), premature years of life lost (PYLL), working years lost (WYL), and productivity loss are all effective measures in identifying economic burden of disease. Objective: We propose a population-based study to analyze comprehensive provincial cohorts of Saskatchewan residents with COPD, lung cancer, and combined COPD and lung cancer in order to identify the burden these diseases present. Methods: Saskatchewan residents over the age of 35 years who had COPD, lung cancer, or both, between January 1, 2000, and December 31, 2015, will be identified and used in this study. Data for analysis including age, gender, and date of death, alongside Statistics Canada income estimates, will be used to estimate productivity loss and WYL. Statistics Canada life tables will be used to calculate YLL and PYLL by subtracting the patients? ages at death by their life expectancies, adjusted using sex and age at death.We will link the Saskatchewan cancer registry with Saskatchewan health administrative databases to create three cohorts: (1) COPD; (2) lung cancer; and (3) COPD and lung cancer. Individuals with lung cancer will be identified using ICDO-T (International Classification of Diseases for Oncology-Topography) codes, and those with COPD will be defined and identified as individuals who had at least 1 visit to a physician with a diagnosis of COPD or 1 hospital separation with a diagnosis of COPD. Those without a valid health care coverage for a consecutive 12 months prior to the first diagnostic code will be excluded from the study. Those with a combined diagnosis of COPD and lung cancer will be identified as individuals who were diagnosed with COPD in the 12 months following their lung cancer diagnosis or anytime preceding their lung cancer diagnosis. Results: As of April 2021, we have had access to all relevant data for this study, have received funding (January 2020), and have begun the preliminary analysis of our data set. Conclusions: It is well documented that COPD and lung cancer are both destructive diseases in terms of YLL, PYLL, WYL, and productivity loss; however, no studies have been conducted to analyze a cohort with combined COPD and lung cancer. Understanding the economic burden associated with each of our 3 cohorts is necessary in understanding and thus reducing the societal impact of COPD and lung cancer. International Registered Report Identifier (IRRID): RR1-10.2196/31350 UR - https://www.researchprotocols.org/2022/3/e31350 UR - http://dx.doi.org/10.2196/31350 UR - http://www.ncbi.nlm.nih.gov/pubmed/35254280 ID - info:doi/10.2196/31350 ER - TY - JOUR AU - Lu, Jiahui AU - Lee, J. Edmund W. PY - 2021/12/29 TI - Examining Twitter Discourse on Electronic Cigarette and Tobacco Consumption During National Cancer Prevention Month in 2018: Topic Modeling and Geospatial Analysis JO - J Med Internet Res SP - e28042 VL - 23 IS - 12 KW - electronic cigarette KW - smoking KW - lung cancer KW - Twitter KW - national cancer prevention month KW - policy KW - topic modeling KW - cessation KW - e-cigarette KW - cancer KW - social media KW - eHealth KW - cancer prevention KW - tweets KW - public health N2 - Background: Examining public perception of tobacco products is critical for effective tobacco policy making and public education outreach. While the link between traditional tobacco products and lung cancer is well established, it is not known how the public perceives the association between electronic cigarettes (e-cigarettes) and lung cancer. In addition, it is unclear how members of the public interact with official messages during cancer campaigns on tobacco consumption and lung cancer. Objective: In this study, we aimed to analyze e-cigarette and smoking tweets in the context of lung cancer during National Cancer Prevention Month in 2018 and examine how e-cigarette and traditional tobacco product discussions relate to implementation of tobacco control policies across different states in the United States. Methods: We mined tweets that contained the term ?lung cancer? on Twitter from February to March 2018. The data set contained 13,946 publicly available tweets that occurred during National Cancer Prevention Month (February 2018), and 10,153 tweets that occurred during March 2018. E-cigarette?related and smoking-related tweets were retrieved, using topic modeling and geospatial analysis. Results: Debates on harmfulness (454/915, 49.7%), personal experiences (316/915, 34.5%), and e-cigarette risks (145/915, 15.8%) were the major themes of e-cigarette tweets related to lung cancer. Policy discussions (2251/3870, 58.1%), smoking risks (843/3870, 21.8%), and personal experiences (776/3870, 20.1%) were the major themes of smoking tweets related to lung cancer. Geospatial analysis showed that discussion on e-cigarette risks was positively correlated with the number of state-level smoke-free policies enacted for e-cigarettes. In particular, the number of indoor and on campus smoke-free policies was related to the number of tweets on e-cigarette risks (smoke-free indoor, r49=0.33, P=.02; smoke-free campus, r49=0.32, P=.02). The total number of e-cigarette policies was also positively related to the number of tweets on e-cigarette risks (r49=0.32, P=.02). In contrast, the number of smoking policies was not significantly associated with any of the smoking themes in the lung cancer discourse (P>.13). Conclusions: Though people recognized the importance of traditional tobacco control policies in reducing lung cancer incidences, their views on e-cigarette risks were divided, and discussions on the importance of e-cigarette policy control were missing from public discourse. Findings suggest the need for health organizations to continuously engage the public in discussions on the potential health risks of e-cigarettes and raise awareness of the insidious lobbying efforts from the tobacco industry. UR - https://www.jmir.org/2021/12/e28042 UR - http://dx.doi.org/10.2196/28042 UR - http://www.ncbi.nlm.nih.gov/pubmed/34964716 ID - info:doi/10.2196/28042 ER - TY - JOUR AU - Yu, Hongfan AU - Yu, Qingsong AU - Nie, Yuxian AU - Xu, Wei AU - Pu, Yang AU - Dai, Wei AU - Wei, Xing AU - Shi, Qiuling PY - 2021/11/9 TI - Data Quality of Longitudinally Collected Patient-Reported Outcomes After Thoracic Surgery: Comparison of Paper- and Web-Based Assessments JO - J Med Internet Res SP - e28915 VL - 23 IS - 11 KW - patient-reported outcome (PRO) KW - data quality KW - MDASI-LC KW - postoperative care KW - symptoms N2 - Background: High-frequency patient-reported outcome (PRO) assessments are used to measure patients' symptoms after surgery for surgical research; however, the quality of those longitudinal PRO data has seldom been discussed. Objective: The aim of this study was to determine data quality-influencing factors and to profile error trajectories of data longitudinally collected via paper-and-pencil (P&P) or web-based assessment (electronic PRO [ePRO]) after thoracic surgery. Methods: We extracted longitudinal PRO data with 678 patients scheduled for lung surgery from an observational study (n=512) and a randomized clinical trial (n=166) on the evaluation of different perioperative care strategies. PROs were assessed by the MD Anderson Symptom Inventory Lung Cancer Module and single-item Quality of Life Scale before surgery and then daily after surgery until discharge or up to 14 days of hospitalization. Patient compliance and data error were identified and compared between P&P and ePRO. Generalized estimating equations model and 2-piecewise model were used to describe trajectories of error incidence over time and to identify the risk factors. Results: Among 678 patients, 629 with at least 2 PRO assessments, 440 completed 3347 P&P assessments and 189 completed 1291 ePRO assessments. In total, 49.4% of patients had at least one error, including (1) missing items (64.69%, 1070/1654), (2) modifications without signatures (27.99%, 463/1654), (3) selection of multiple options (3.02%, 50/1654), (4) missing patient signatures (2.54%, 42/1654), (5) missing researcher signatures (1.45%, 24/1654), and (6) missing completion dates (0.30%, 5/1654). Patients who completed ePRO had fewer errors than those who completed P&P assessments (ePRO: 30.2% [57/189] vs. P&P: 57.7% [254/440]; P<.001). Compared with ePRO patients, those using P&P were older, less educated, and sicker. Common risk factors of having errors were a lower education level (P&P: odds ratio [OR] 1.39, 95% CI 1.20-1.62; P<.001; ePRO: OR 1.82, 95% CI 1.22-2.72; P=.003), treated in a provincial hospital (P&P: OR 3.34, 95% CI 2.10-5.33; P<.001; ePRO: OR 4.73, 95% CI 2.18-10.25; P<.001), and with severe disease (P&P: OR 1.63, 95% CI 1.33-1.99; P<.001; ePRO: OR 2.70, 95% CI 1.53-4.75; P<.001). Errors peaked on postoperative day (POD) 1 for P&P, and on POD 2 for ePRO. Conclusions: It is possible to improve data quality of longitudinally collected PRO through ePRO, compared with P&P. However, ePRO-related sampling bias needs to be considered when designing clinical research using longitudinal PROs as major outcomes. UR - https://www.jmir.org/2021/11/e28915 UR - http://dx.doi.org/10.2196/28915 UR - http://www.ncbi.nlm.nih.gov/pubmed/34751657 ID - info:doi/10.2196/28915 ER - TY - JOUR AU - Meng, Weilin AU - Mosesso, M. Kelly AU - Lane, A. Kathleen AU - Roberts, R. Anna AU - Griffith, Ashley AU - Ou, Wanmei AU - Dexter, R. Paul PY - 2021/10/12 TI - An Automated Line-of-Therapy Algorithm for Adults With Metastatic Non?Small Cell Lung Cancer: Validation Study Using Blinded Manual Chart Review JO - JMIR Med Inform SP - e29017 VL - 9 IS - 10 KW - automated algorithm KW - line of therapy KW - longitudinal changes KW - manual chart review KW - non?small cell lung cancer KW - systemic anticancer therapy N2 - Background: Extraction of line-of-therapy (LOT) information from electronic health record and claims data is essential for determining longitudinal changes in systemic anticancer therapy in real-world clinical settings. Objective: The aim of this retrospective cohort analysis is to validate and refine our previously described open-source LOT algorithm by comparing the output of the algorithm with results obtained through blinded manual chart review. Methods: We used structured electronic health record data and clinical documents to identify 500 adult patients treated for metastatic non?small cell lung cancer with systemic anticancer therapy from 2011 to mid-2018; we assigned patients to training (n=350) and test (n=150) cohorts, randomly divided proportional to the overall ratio of simple:complex cases (n=254:246). Simple cases were patients who received one LOT and no maintenance therapy; complex cases were patients who received more than one LOT and/or maintenance therapy. Algorithmic changes were performed using the training cohort data, after which the refined algorithm was evaluated against the test cohort. Results: For simple cases, 16 instances of discordance between the LOT algorithm and chart review prerefinement were reduced to 8 instances postrefinement; in the test cohort, there was no discordance between algorithm and chart review. For complex cases, algorithm refinement reduced the discordance from 68 to 62 instances, with 37 instances in the test cohort. The percentage agreement between LOT algorithm output and chart review for patients who received one LOT was 89% prerefinement, 93% postrefinement, and 93% for the test cohort, whereas the likelihood of precise matching between algorithm output and chart review decreased with an increasing number of unique regimens. Several areas of discordance that arose from differing definitions of LOTs and maintenance therapy could not be objectively resolved because of a lack of precise definitions in the medical literature. Conclusions: Our findings identify common sources of discordance between the LOT algorithm and clinician documentation, providing the possibility of targeted algorithm refinement. UR - https://medinform.jmir.org/2021/10/e29017 UR - http://dx.doi.org/10.2196/29017 UR - http://www.ncbi.nlm.nih.gov/pubmed/34636730 ID - info:doi/10.2196/29017 ER - TY - JOUR AU - Veldhuijzen, Evalien AU - Walraven, Iris AU - Belderbos, José PY - 2021/9/14 TI - Selecting a Subset Based on the Patient-Reported Outcomes Version of the Common Terminology Criteria for Adverse Events for Patient-Reported Symptom Monitoring in Lung Cancer Treatment: Mixed Methods Study JO - JMIR Cancer SP - e26574 VL - 7 IS - 3 KW - PRO-CTCAE KW - lung cancer KW - side effects KW - patient-reported outcomes KW - PROM KW - symptomatic adverse events N2 - Background: The Patient-Reported Outcomes Version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) item library covers a wide range of symptoms relevant to oncology care. There is a need to select a subset of items relevant to specific patient populations to enable the implementation of PRO-CTCAE?based symptom monitoring in clinical practice. Objective: The aim of this study is to develop a PRO-CTCAE?based subset relevant to patients with lung cancer that can be used for monitoring during multidisciplinary clinical practice. Methods: The PRO-CTCAE?based subset for patients with lung cancer was generated using a mixed methods approach based on the European Organization for Research and Treatment of Cancer guidelines for developing questionnaires, comprising a literature review and semistructured interviews with both patients with lung cancer and health care practitioners (HCPs). Both patients and HCPs were queried on the relevance and impact of all PRO-CTCAE items. The results were summarized, and after a final round of expert review, a selection of clinically relevant items for patients with lung cancer was made. Results: A heterogeneous group of patients with lung cancer (n=25) from different treatment modalities and HCPs (n=22) participated in the study. A final list of eight relevant PRO-CTCAE items was created: decreased appetite, cough, shortness of breath, fatigue, constipation, nausea, sadness, and pain (general). Conclusions: On the basis of the literature and both professional and patient input, a subset of PRO-CTCAE items has been identified for use in patients with lung cancer in clinical practice. Future work is needed to confirm the validity and effectiveness of this PRO-CTCAE?based lung cancer subset internationally and in real-world clinical practice settings. UR - https://cancer.jmir.org/2021/3/e26574 UR - http://dx.doi.org/10.2196/26574 UR - http://www.ncbi.nlm.nih.gov/pubmed/34519658 ID - info:doi/10.2196/26574 ER - TY - JOUR AU - Yeh, Chia-Han Marvin AU - Wang, Yu-Hsiang AU - Yang, Hsuan-Chia AU - Bai, Kuan-Jen AU - Wang, Hsiao-Han AU - Li, Jack Yu-Chuan PY - 2021/8/3 TI - Artificial Intelligence?Based Prediction of Lung Cancer Risk Using Nonimaging Electronic Medical Records: Deep Learning Approach JO - J Med Internet Res SP - e26256 VL - 23 IS - 8 KW - artificial intelligence KW - lung cancer screening KW - electronic medical record N2 - Background: Artificial intelligence approaches can integrate complex features and can be used to predict a patient?s risk of developing lung cancer, thereby decreasing the need for unnecessary and expensive diagnostic interventions. Objective: The aim of this study was to use electronic medical records to prescreen patients who are at risk of developing lung cancer. Methods: We randomly selected 2 million participants from the Taiwan National Health Insurance Research Database who received care between 1999 and 2013. We built a predictive lung cancer screening model with neural networks that were trained and validated using pre-2012 data, and we tested the model prospectively on post-2012 data. An age- and gender-matched subgroup that was 10 times larger than the original lung cancer group was used to assess the predictive power of the electronic medical record. Discrimination (area under the receiver operating characteristic curve [AUC]) and calibration analyses were performed. Results: The analysis included 11,617 patients with lung cancer and 1,423,154 control patients. The model achieved AUCs of 0.90 for the overall population and 0.87 in patients ?55 years of age. The AUC in the matched subgroup was 0.82. The positive predictive value was highest (14.3%) among people aged ?55 years with a pre-existing history of lung disease. Conclusions: Our model achieved excellent performance in predicting lung cancer within 1 year and has potential to be deployed for digital patient screening. Convolution neural networks facilitate the effective use of EMRs to identify individuals at high risk for developing lung cancer. UR - https://www.jmir.org/2021/8/e26256 UR - http://dx.doi.org/10.2196/26256 UR - http://www.ncbi.nlm.nih.gov/pubmed/34342588 ID - info:doi/10.2196/26256 ER - TY - JOUR AU - Hu, Danqing AU - Zhang, Huanyao AU - Li, Shaolei AU - Wang, Yuhong AU - Wu, Nan AU - Lu, Xudong PY - 2021/7/21 TI - Automatic Extraction of Lung Cancer Staging Information From Computed Tomography Reports: Deep Learning Approach JO - JMIR Med Inform SP - e27955 VL - 9 IS - 7 KW - lung cancer KW - clinical staging KW - information extraction KW - named entity recognition KW - relation classification N2 - Background: Lung cancer is the leading cause of cancer deaths worldwide. Clinical staging of lung cancer plays a crucial role in making treatment decisions and evaluating prognosis. However, in clinical practice, approximately one-half of the clinical stages of lung cancer patients are inconsistent with their pathological stages. As one of the most important diagnostic modalities for staging, chest computed tomography (CT) provides a wealth of information about cancer staging, but the free-text nature of the CT reports obstructs their computerization. Objective: We aimed to automatically extract the staging-related information from CT reports to support accurate clinical staging of lung cancer. Methods: In this study, we developed an information extraction (IE) system to extract the staging-related information from CT reports. The system consisted of the following three parts: named entity recognition (NER), relation classification (RC), and postprocessing (PP). We first summarized 22 questions about lung cancer staging based on the TNM staging guideline. Next, three state-of-the-art NER algorithms were implemented to recognize the entities of interest. Next, we designed a novel RC method using the relation sign constraint (RSC) to classify the relations between entities. Finally, a rule-based PP module was established to obtain the formatted answers using the results of NER and RC. Results: We evaluated the developed IE system on a clinical data set containing 392 chest CT reports collected from the Department of Thoracic Surgery II in the Peking University Cancer Hospital. The experimental results showed that the bidirectional encoder representation from transformers (BERT) model outperformed the iterated dilated convolutional neural networks-conditional random field (ID-CNN-CRF) and bidirectional long short-term memory networks-conditional random field (Bi-LSTM-CRF) for NER tasks with macro-F1 scores of 80.97% and 90.06% under the exact and inexact matching schemes, respectively. For the RC task, the proposed RSC showed better performance than the baseline methods. Further, the BERT-RSC model achieved the best performance with a macro-F1 score of 97.13% and a micro-F1 score of 98.37%. Moreover, the rule-based PP module could correctly obtain the formatted results using the extractions of NER and RC, achieving a macro-F1 score of 94.57% and a micro-F1 score of 96.74% for all the 22 questions. Conclusions: We conclude that the developed IE system can effectively and accurately extract information about lung cancer staging from CT reports. Experimental results show that the extracted results have significant potential for further use in stage verification and prediction to facilitate accurate clinical staging. UR - https://medinform.jmir.org/2021/7/e27955 UR - http://dx.doi.org/10.2196/27955 UR - http://www.ncbi.nlm.nih.gov/pubmed/34287213 ID - info:doi/10.2196/27955 ER - TY - JOUR AU - Fox, Lauren AU - Gates, Jessica AU - De Vos, Ruth AU - Wiffen, Laura AU - Hicks, Alexander AU - Rupani, Hitasha AU - Williams, Jane AU - Brown, Thomas AU - Chauhan, J. Anoop PY - 2021/7/9 TI - The VICTORY (Investigation of Inflammacheck to Measure Exhaled Breath Condensate Hydrogen Peroxide in Respiratory Conditions) Study: Protocol for a Cross-sectional Observational Study JO - JMIR Res Protoc SP - e23831 VL - 10 IS - 7 KW - medical device KW - diagnosis KW - hydrogen peroxide KW - lung diseases KW - exhalation KW - asthma KW - COPD KW - bronchiectasis KW - interstitial lung disease KW - lung cancer KW - breathing pattern disorder KW - pneumonia N2 - Background: More than 7% of the world?s population is living with a chronic respiratory condition. In the United Kingdom, lung disease affects approximately 1 in 5 people, resulting in over 700,000 hospital admissions each year. People with respiratory conditions have several symptoms and can require multiple health care visits and investigations before a diagnosis is made. The tests available can be difficult to perform, especially if a person is symptomatic, leading to poor quality results. A new, easy-to-perform, point-of-care test that can be performed in any health care setting and that can differentiate between various respiratory conditions would have a significant, beneficial impact on the ability to diagnose respiratory diseases. Objective: The objective of this study is to use a new handheld device (Inflammacheck) in different respiratory conditions to measure the exhaled breath condensate hydrogen peroxide (EBC H2O2) and compare these results with those of healthy controls and with each other. This study also aims to determine whether the device can measure other parameters, including breath humidity, breath temperature, breath flow dynamics, and end tidal carbon dioxide. Methods: We will perform a single-visit, cross-sectional observational study of EBC H2O2 levels, as measured by Inflammacheck, in people with respiratory disease and volunteers with no known lung disease. Participants with a confirmed diagnosis of asthma, chronic obstructive pulmonary disease, lung cancer, bronchiectasis, pneumonia, breathing pattern disorder, and interstitial lung disease as well as volunteers with no history of lung disease will be asked to breathe into the Inflammacheck device to record their breath sample. Results: The results from this study will be available in 2022, in anticipation of COVID-19?related delays. Conclusions: This study will investigate the EBC H2O2, as well as other exhaled breath parameters, for use as a future diagnostic tool. UR - https://www.researchprotocols.org/2021/7/e23831 UR - http://dx.doi.org/10.2196/23831 UR - http://www.ncbi.nlm.nih.gov/pubmed/34255725 ID - info:doi/10.2196/23831 ER - TY - JOUR AU - Shih, Chi-Huang AU - Chou, Pai-Chien AU - Chou, Ting-Ling AU - Huang, Tsai-Wei PY - 2021/7/5 TI - Measurement of Cancer-Related Fatigue Based on Heart Rate Variability: Observational Study JO - J Med Internet Res SP - e25791 VL - 23 IS - 7 KW - cancer-related fatigue KW - heart rate variability KW - LF-HF ratio KW - photoplethysmography KW - wearables KW - chemotherapy N2 - Background: Cancer-related fatigue is a serious side effect of cancer, and its treatment can disrupt the quality of life of patients. Clinically, the standard method for assessing cancer-related fatigue relies on subjective experience retrieved from patient self-reports, such as the Brief Fatigue Inventory (BFI). However, most patients do not self-report their fatigue levels. Objective: In this study, we aim to develop an objective cancer-related fatigue assessment method to track and monitor fatigue in patients with cancer. Methods: In total, 12 patients with lung cancer who were undergoing chemotherapy or targeted therapy were enrolled. We developed frequency-domain parameters of heart rate variability (HRV) and BFI based on a wearable-based HRV measurement system. All patients completed the BFI-Taiwan version questionnaire and wore the device for 7 consecutive days to record HRV parameters such as low frequency (LF), high frequency (HF), and LF-HF ratio (LF-HF). Statistical analysis was used to map the correlation between subjective fatigue and objective data. Results: A moderate positive correlation was observed between the average LF-HF ratio and BFI in the sleep phase (?=0.86). The mapped BFI score derived by the BFI mapping method could approximate the BFI from the patient self-report. The mean absolute error rate between the subjective and objective BFI scores was 3%. Conclusions: LF-HF is highly correlated with the cancer-related fatigue experienced by patients with lung cancer undergoing chemotherapy or targeted therapy. Beyond revealing fatigue levels objectively, continuous HRV recordings through the photoplethysmography watch device and the defined parameters (LF-HF) can define the active phase and sleep phase in patients with lung cancer who undergo chemotherapy or targeted chemotherapy, allowing a deduction of their sleep patterns. UR - https://www.jmir.org/2021/7/e25791 UR - http://dx.doi.org/10.2196/25791 UR - http://www.ncbi.nlm.nih.gov/pubmed/36260384 ID - info:doi/10.2196/25791 ER - TY - JOUR AU - Neil, M. Jordan AU - Chang, Yuchiao AU - Goshe, Brett AU - Rigotti, Nancy AU - Gonzalez, Irina AU - Hawari, Saif AU - Ballini, Lauren AU - Haas, S. Jennifer AU - Marotta, Caylin AU - Wint, Amy AU - Harris, Kim AU - Crute, Sydney AU - Flores, Efren AU - Park, R. Elyse PY - 2021/6/30 TI - A Web-Based Intervention to Increase Smokers? Intentions to Participate in a Cessation Study Offered at the Point of Lung Screening: Factorial Randomized Trial JO - JMIR Form Res SP - e28952 VL - 5 IS - 6 KW - clinical trials recruitment KW - digital outreach KW - message design experiment KW - smoking cessation KW - lung cancer screening KW - prospect theory N2 - Background: Screen ASSIST is a cessation trial offered to current smokers at the point of lung cancer screening. Because of the unique position of promoting a prevention behavior (smoking cessation) within the context of a detection behavior (lung cancer screening), this study employed prospect theory to design and formatively evaluate a targeted recruitment video prior to trial launch. Objective: The aim of this study was to identify which message frames were most effective at promoting intent to participate in a smoking cessation study. Methods: Participants were recruited from a proprietary opt-in online panel company and randomized to a 2 (benefits of quitting vs risks of continuing to smoke at the time of lung screening; BvR) × 2 (gains of participating vs losses of not participating in a cessation study; GvL) message design experiment (N=314). The primary outcome was self-assessed intent to participate in a smoking cessation study. Message effectiveness and lung cancer risk perception measures were also collected. Analysis of variance examined the main effect of the 2 message factors and a least absolute shrinkage and selection operator (LASSO) approach identified predictors of intent to participate in a multivariable model. A mediation analysis was conducted to determine the direct and indirect effects of message factors on intent to participate in a cessation study. Results: A total of 296 participants completed the intervention. There were no significant differences in intent to participate in a smoking cessation study between message frames (P=.12 and P=.61). In the multivariable model, quit importance (P<.001), perceived message relevance (P<.001), and affective risk response (ie, worry about developing lung cancer; P<.001) were significant predictors of intent to participate. The benefits of quitting frame significantly increased affective risk response (Meanbenefits 2.60 vs Meanrisk 2.40; P=.03), which mediated the relationship between message frame and intent to participate (b=0.24; 95% CI 0.01-0.47; P=.03). Conclusions: This study provides theoretical and practical guidance on how to design and evaluate proactive recruitment messages for a cessation trial. Based on our findings, we conclude that heavy smokers are more responsive to recruitment messages that frame the benefits of quitting as it increased affective risk response, which predicted greater intention to participate in a smoking cessation study. UR - https://formative.jmir.org/2021/6/e28952 UR - http://dx.doi.org/10.2196/28952 UR - http://www.ncbi.nlm.nih.gov/pubmed/34255651 ID - info:doi/10.2196/28952 ER - TY - JOUR AU - Grabner, Michael AU - Molife, Cliff AU - Wang, Liya AU - Winfree, B. Katherine AU - Cui, Lin Zhanglin AU - Cuyun Carter, Gebra AU - Hess, M. Lisa PY - 2021/4/12 TI - Data Integration to Improve Real-world Health Outcomes Research for Non?Small Cell Lung Cancer in the United States: Descriptive and Qualitative Exploration JO - JMIR Cancer SP - e23161 VL - 7 IS - 2 KW - non?small cell lung cancer KW - cancer KW - data aggregation KW - real-world data KW - administrative claims data KW - medical records KW - electronic health record KW - retrospective study KW - population health KW - health services research N2 - Background: The integration of data from disparate sources could help alleviate data insufficiency in real-world studies and compensate for the inadequacies of single data sources and short-duration, small sample size studies while improving the utility of data for research. Objective: This study aims to describe and evaluate a process of integrating data from several complementary sources to conduct health outcomes research in patients with non?small cell lung cancer (NSCLC). The integrated data set is also used to describe patient demographics, clinical characteristics, treatment patterns, and mortality rates. Methods: This retrospective cohort study integrated data from 4 sources: administrative claims from the HealthCore Integrated Research Database, clinical data from a Cancer Care Quality Program (CCQP), clinical data from abstracted medical records (MRs), and mortality data from the US Social Security Administration. Patients with lung cancer who initiated second-line (2L) therapy between November 01, 2015, and April 13, 2018, were identified in the claims and CCQP data. Eligible patients were 18 years or older and received atezolizumab, docetaxel, erlotinib, nivolumab, pembrolizumab, pemetrexed, or ramucirumab in the 2L setting. The main analysis cohort included patients with claims data and data from at least one additional data source (CCQP or MR). Patients without integrated data (claims only) were reported separately. Descriptive and univariate statistics were reported. Results: Data integration resulted in a main analysis cohort of 2195 patients with NSCLC; 2106 patients had CCQP and 407 patients had MR data. The claims-only cohort included 931 eligible patients. For the main analysis cohort, the mean age was 62.1 (SD 9.27) years, 48.56% (1066/2195) were female, the median length of follow-up was 6.8 months, and for 37.77% (829/2195), death was observed. For the claims-only cohort, the mean age was 66.6 (SD 12.69) years, 52.1% (485/931) were female, the median length of follow-up was 8.6 months, and for 29.3% (273/931), death was observed. The most frequent 2L treatment was immunotherapy (1094/2195, 49.84%), followed by platinum-based regimens (472/2195, 21.50%) and single-agent chemotherapy (441/2195, 20.09%); mean duration of 2L therapy was 5.6 (SD 4.9, median 4) months. We describe challenges and learnings from the data integration process, and the benefits of the integrated data set, which includes a richer set of clinical and outcome data to supplement the utilization metrics available in administrative claims. Conclusions: The management of patients with NSCLC requires care from a multidisciplinary team, leading to a lack of a single aggregated data source in real-world settings. The availability of integrated clinical data from MRs, health plan claims, and other sources of clinical care may improve the ability to assess emerging treatments. UR - https://cancer.jmir.org/2021/2/e23161 UR - http://dx.doi.org/10.2196/23161 UR - http://www.ncbi.nlm.nih.gov/pubmed/33843600 ID - info:doi/10.2196/23161 ER - TY - JOUR AU - Kataoka, Yuki AU - Takemura, Tomoyasu AU - Sasajima, Munehiko AU - Katoh, Naoki PY - 2021/3/10 TI - Development and Early Feasibility of Chatbots for Educating Patients With Lung Cancer and Their Caregivers in Japan: Mixed Methods Study JO - JMIR Cancer SP - e26911 VL - 7 IS - 1 KW - cancer KW - caregivers KW - chatbot KW - lung cancer KW - mixed methods approach KW - online health KW - patients KW - symptom management education KW - web-based platform N2 - Background: Chatbots are artificial intelligence?driven programs that interact with people. The applications of this technology include the collection and delivery of information, generation of and responding to inquiries, collection of end user feedback, and the delivery of personalized health and medical information to patients through cellphone- and web-based platforms. However, no chatbots have been developed for patients with lung cancer and their caregivers. Objective: This study aimed to develop and evaluate the early feasibility of a chatbot designed to improve the knowledge of symptom management among patients with lung cancer in Japan and their caregivers. Methods: We conducted a sequential mixed methods study that included a web-based anonymized questionnaire survey administered to physicians and paramedics from June to July 2019 (phase 1). Two physicians conducted a content analysis of the questionnaire to curate frequently asked questions (FAQs; phase 2). Based on these FAQs, we developed and integrated a chatbot into a social network service (phase 3). The physicians and paramedics involved in phase I then tested this chatbot (? test; phase 4). Thereafter, patients with lung cancer and their caregivers tested this chatbot (? test; phase 5). Results: We obtained 246 questions from 15 health care providers in phase 1. We curated 91 FAQs and their corresponding responses in phase 2. In total, 11 patients and 1 caregiver participated in the ? test in phase 5. The participants were asked 60 questions, 8 (13%) of which did not match the appropriate categories. After the ? test, 7 (64%) participants responded to the postexperimental questionnaire. The mean satisfaction score was 2.7 (SD 0.5) points out of 5. Conclusions: Medical staff providing care to patients with lung cancer can use the categories specified in this chatbot to educate patients on how they can manage their symptoms. Further studies are required to improve chatbots in terms of interaction with patients. UR - https://cancer.jmir.org/2021/1/e26911 UR - http://dx.doi.org/10.2196/26911 UR - http://www.ncbi.nlm.nih.gov/pubmed/33688839 ID - info:doi/10.2196/26911 ER - TY - JOUR AU - Liu, Ziqing AU - He, Haiyang AU - Yan, Shixing AU - Wang, Yong AU - Yang, Tao AU - Li, Guo-Zheng PY - 2020/6/16 TI - End-to-End Models to Imitate Traditional Chinese Medicine Syndrome Differentiation in Lung Cancer Diagnosis: Model Development and Validation JO - JMIR Med Inform SP - e17821 VL - 8 IS - 6 KW - traditional Chinese medicine KW - syndrome differentiation KW - lung cancer KW - medical record KW - deep learning KW - model fusion N2 - Background: Traditional Chinese medicine (TCM) has been shown to be an efficient mode to manage advanced lung cancer, and accurate syndrome differentiation is crucial to treatment. Documented evidence of TCM treatment cases and the progress of artificial intelligence technology are enabling the development of intelligent TCM syndrome differentiation models. This is expected to expand the benefits of TCM to lung cancer patients. Objective: The objective of this work was to establish end-to-end TCM diagnostic models to imitate lung cancer syndrome differentiation. The proposed models used unstructured medical records as inputs to capitalize on data collected for practical TCM treatment cases by lung cancer experts. The resulting models were expected to be more efficient than approaches that leverage structured TCM datasets. Methods: We approached lung cancer TCM syndrome differentiation as a multilabel text classification problem. First, entity representation was conducted with Bidirectional Encoder Representations from Transformers and conditional random fields models. Then, five deep learning?based text classification models were applied to the construction of a medical record multilabel classifier, during which two data augmentation strategies were adopted to address overfitting issues. Finally, a fusion model approach was used to elevate the performance of the models. Results: The F1 score of the recurrent convolutional neural network (RCNN) model with augmentation was 0.8650, a 2.41% improvement over the unaugmented model. The Hamming loss for RCNN with augmentation was 0.0987, which is 1.8% lower than that of the same model without augmentation. Among the models, the text-hierarchical attention network (Text-HAN) model achieved the highest F1 scores of 0.8676 and 0.8751. The mean average precision for the word encoding?based RCNN was 10% higher than that of the character encoding?based representation. A fusion model of the text-convolutional neural network, text-recurrent neural network, and Text-HAN models achieved an F1 score of 0.8884, which showed the best performance among the models. Conclusions: Medical records could be used more productively by constructing end-to-end models to facilitate TCM diagnosis. With the aid of entity-level representation, data augmentation, and model fusion, deep learning?based multilabel classification approaches can better imitate TCM syndrome differentiation in complex cases such as advanced lung cancer. UR - https://medinform.jmir.org/2020/6/e17821 UR - http://dx.doi.org/10.2196/17821 UR - http://www.ncbi.nlm.nih.gov/pubmed/32543445 ID - info:doi/10.2196/17821 ER - TY - JOUR AU - Chen, Songjing AU - Wu, Sizhu PY - 2020/3/17 TI - Identifying Lung Cancer Risk Factors in the Elderly Using Deep Neural Networks: Quantitative Analysis of Web-Based Survey Data JO - J Med Internet Res SP - e17695 VL - 22 IS - 3 KW - deep learning KW - lung cancer KW - risk factors KW - aged KW - primary prevention N2 - Background: Lung cancer is one of the most dangerous malignant tumors, with the fastest-growing morbidity and mortality, especially in the elderly. With a rapid growth of the elderly population in recent years, lung cancer prevention and control are increasingly of fundamental importance, but are complicated by the fact that the pathogenesis of lung cancer is a complex process involving a variety of risk factors. Objective: This study aimed at identifying key risk factors of lung cancer incidence in the elderly and quantitatively analyzing these risk factors? degree of influence using a deep learning method. Methods: Based on Web-based survey data, we integrated multidisciplinary risk factors, including behavioral risk factors, disease history factors, environmental factors, and demographic factors, and then preprocessed these integrated data. We trained deep neural network models in a stratified elderly population. We then extracted risk factors of lung cancer in the elderly and conducted quantitative analyses of the degree of influence using the deep neural network models. Results: The proposed model quantitatively identified risk factors based on 235,673 adults. The proposed deep neural network models of 4 groups (age ?65 years, women ?65 years old, men ?65 years old, and the whole population) achieved good performance in identifying lung cancer risk factors, with accuracy ranging from 0.927 (95% CI 0.223-0.525; P=.002) to 0.962 (95% CI 0.530-0.751; P=.002) and the area under curve ranging from 0.913 (95% CI 0.564-0.803) to 0.931(95% CI 0.499-0.593). Smoking frequency was the leading risk factor for lung cancer in men 65 years and older. Time since quitting and smoking at least 100 cigarettes in their lifetime were the main risk factors for lung cancer in women 65 years and older. Men 65 years and older had the highest lung cancer incidence among the stratified groups, particularly non?small cell lung cancer incidence. Lung cancer incidence decreased more obviously in men than in women with smoking rate decline. Conclusions: This study demonstrated a quantitative method to identify risk factors of lung cancer in the elderly. The proposed models provided intervention indicators to prevent lung cancer, especially in older men. This approach might be used as a risk factor identification tool to apply in other cancers and help physicians make decisions on cancer prevention. UR - http://www.jmir.org/2020/3/e17695/ UR - http://dx.doi.org/10.2196/17695 UR - http://www.ncbi.nlm.nih.gov/pubmed/32181751 ID - info:doi/10.2196/17695 ER -