JMIR Cancer

Immune checkpoint inhibitors represent an effective therapeutic approach for advanced gastric cancer. Their efficacy largely depends on the status of tumor biomarkers including HER2, PD-L1 (CPS ≥1), and MSI-H. To non-invasively evaluate these biomarkers, researchers have developed radiomic models for individual biomarker prediction. However, in clinical practice, holistic prediction of these biomarkers as an integrated system is more efficient. Currently, the feasibility of implementing radiomics-based comprehensive biomarker prediction remains unclear, requiring further investigation.

Surgeons often face challenges in distinguishing between benign and malignant follicular thyroid neoplasms (FTNs), particularly for small tumors, until diagnostic surgery is performed.

Advances in therapies have significantly improved the outcomes of patients with cancer. However, multidimensional symptoms negatively impact patients’ quality of life. Traditional symptom analysis methods fail to capture the dynamic and interactive nature of these symptoms, limiting progress in supportive care. Network analysis (NA) is a promising method to evaluate complex medical situations.

Breast cancer survivors have increased cardiovascular risk compared to those without cancer history. Cardiovascular disease is the most common cause of death in breast cancer survivors. Cardiovascular risk in breast cancer survivors is impacted by both cancer treatment-associated effects and overlap in risk factors for breast cancer and cardiovascular disease. Strategies to improve screening for and management of cardiovascular disease in breast cancer survivors are needed to improve delivery of survivorship care.

Blood tests used to identify patients at increased risk of undiagnosed cancer are commonly used in isolation, primarily by monitoring whether results fall outside the normal range. Some prediction models incorporate changes over repeated blood tests (or trends) to improve individualised cancer risk identification, as relevant trends may be confined within the normal range.

Home-based hospice care offers terminal cancer patients the comfort of receiving care in a familiar environment while enabling family members to provide personalised support. Despite the critical role families play, the literature remains underexplored in terms of their experiences, needs, and perceptions. A robust qualitative synthesis is needed to inform improvements in palliative care services.

Colorectal cancer (CRC) is now the leading cause of cancer-related deaths among young Americans. Accurate early prediction and a thorough understanding of the risk factors for early-onset colorectal cancer (EOCRC) are vital for effective prevention and treatment, particularly for patients below the recommended screening age.

Digital health interventions offer promise for scalable and accessible healthcare, but access is still limited by some participatory challenges, especially for disadvantaged families facing limited health literacy, language barriers, low income, or living in marginalized areas. These issues are particularly pronounced for colorectal cancer (CRC) patients, who often experience distressing symptoms and struggle with educational materials due to complex jargon, fatigue, or reading level mismatches. To address these issues, we developed and assessed the feasibility of a digital health platform, CRCWeb, to improve the accessibility of educational resources on symptom management for disadvantaged CRC patients and their caregivers facing limited health literacy or low income. CRCWeb was developed through a stakeholder-centered participatory design approach. Two-phase semi-structured interviews with patients, caregivers, and oncology experts informed the iterative design process. From the interviews, we developed the following five key design principles: user-friendly navigation, multimedia integration, concise and clear content, enhanced accessibility for individuals with vision and reading disabilities, and scalability for future content expansion. Initial feedback from iterative stakeholder engagements confirmed high user satisfaction, with participants rating CRCWeb an average of 3.98 out of 5 on the post-intervention survey. Additionally, using GenAI tools, including large language models (LLMs) like ChatGPT and multimedia generation tools such as Pictory, complex healthcare guidelines were transformed into concise, easily comprehensible multimedia content, and made accessible through CRCWeb. User engagement was notably higher among disadvantaged participants with limited health literacy or low income, who logged into the platform 2.52 times more frequently than non-disadvantaged participants. The structured development approach of CRCWeb demonstrates that GenAI-powered multimedia interventions can effectively address healthcare accessibility barriers faced by disadvantaged CRC patients and caregivers with limited health literacy or low income. This structured approach highlights how digital innovations can enhance healthcare.

For patients with cancer, the pathway to diagnosis will most often begin in general practice. But delays in diagnosis can occur in the absence of strong diagnostic features or in patients with non-specific symptoms. Initial presentations and routine blood tests are important in determining whether a patient requires further investigation. Quality improvement (QI) interventions including auditing tools and clinical decision support (CDS), have been developed for use in general practice to support this diagnostic process. We conducted a process evaluation of a pragmatic, cluster randomised trial which evaluated the effectiveness of a new technology, Future Health Today (FHT), implemented in general practice to assist with the appropriate follow-up of patients at risk of an undiagnosed cancer.

Commonly used digital health technologies, such as electronic health record systems and patient portals as well as custom-built digital decision aids, have the potential to enhance person-centered shared decision-making (SDM) in cancer care. SDM is a 2-way exchange of information between at least a clinician and the patient and a shared commitment to make informed decisions. However, there is little evidence in the literature on how technologies are used for SDM or how best they can be designed and integrated into workflows and practice. This may be due to the nature of SDM, which is fundamentally human interactions and conversations that produce desired human outcomes. Therefore, technology must be nonintrusive while supporting the human decision-making process.

Next-generation sequencing (NGS) has become a cornerstone of treatment for lung cancer and is recommended in current treatment guidelines for patients with advanced or metastatic disease.

The increasing demand for population-wide genomic screening (PGS) and the limited availability of genetic counseling resources have created a pressing need for innovative service delivery models. Chatbots powered by large language models (LLMs) have shown potential in genomic services, particularly in pre-test counseling, but their application in returning positive PGS results remains underexplored. Leveraging advanced LLMs like GPT-4 offers an opportunity to address this gap by delivering accurate, contextual, and user-centered communication to individuals receiving positive genetic test results. This project aimed to design, implement, and evaluate a chatbot integrated with GPT-4, tailored to support the return of positive genomic screening results in the context of South Carolina's In Our DNA SC program. This initiative offers free genetic screening to 100,000 individuals, with over 33,000 results returned and numerous positive findings for conditions such as Lynch syndrome, hereditary breast and ovarian cancer syndrome, and familial hypercholesterolemia. A three-step prompt engineering process using Retrieval-Augmented Generation (RAG) and few-shot techniques was employed to create the chatbot. Training materials included patient frequently asked questions, genetic counseling scripts, and patient-derived queries. The chatbot underwent iterative refinement based on 13 training questions, while performance was evaluated through expert ratings on responses to two hypothetical patient scenarios. The two scenarios were intended to represent common but distinct patient profiles in terms of gender, race, ethnicity, age, and background knowledge. Domain experts rated the chatbot using a 5-point Likert scale across eight predefined criteria: tone, clarity, program accuracy, domain accuracy, robustness, efficiency, boundaries, and usability. The chatbot achieved an average score of 3.88 across all evaluation metrics. The highest-rated criteria were tone (4.25) and usability (4.25), reflecting the chatbot’s ability to communicate effectively and provide a seamless user experience. Boundary management (4.0) and efficiency (3.88) also scored well, while clarity and robustness received ratings of 3.81. Domain accuracy was rated 3.63, indicating satisfactory performance in delivering genetic information, whereas program accuracy received the lowest score of 3.25, highlighting the need for improvements in delivering program-specific details. This project demonstrates the feasibility of using LLM-powered chatbots to support the return of positive genomic screening results. The chatbot effectively handled open-ended patient queries, maintained conversational boundaries, and delivered user-friendly responses. However, enhancements in program-specific accuracy are essential to maximize its utility. Future research will explore hybrid chatbot designs that combine the strengths of LLMs with rule-based components to improve scalability, accuracy, and accessibility in genomic service delivery. The findings underscore the potential of generative AI tools to address resource limitations and improve the accessibility of genomic healthcare services.