JMIR Cancer最新文献

{"title":"Supporting Medication Adherence in Pediatric Patients Undergoing Hematopoietic Stem Cell Transplant Using the BMT4me mHealth App: Mixed Methods Usability Study.","authors":"Mariam Kochashvili, Parishma Guttoo, Emre Sezgin, Ahna Pai, Rajinder Bajwa, Wendy Landier, Cynthia Gerhardt, Micah Skeens","doi":"10.2196/66847","DOIUrl":"https://doi.org/10.2196/66847","url":null,"abstract":"<p><strong>Background: </strong>Due to multifaceted outpatient regimens, children receiving hematopoietic stem cell transplants (HCTs) are at high risk of medication nonadherence, leading to life-threatening complications. Mobile health (mHealth) interventions have proven effective in improving adherence in various pediatric conditions; however, adherence intervention literature on HCT is limited.</p><p><strong>Objective: </strong>This study aimed to assess the usability of a mHealth intervention (BMT4me) designed to serve as a real-time, personalized tool for medication management or adherence, symptom tracking, and journal keeping.</p><p><strong>Methods: </strong>Following a mixed methods approach, 14 caregivers (n=11, 79% female; n=10, 71% White) of children aged 2-18 (mean age 8.51, SD 5.18) years in the acute phase (first 100 d) post-HCT were recruited. Caregivers were asked to use the BMT4me app for 100 days or until weaning of the immunosuppressant medications to measure usability. The System Usability Scale (assessing functionality and acceptability), reaction cards (assessing desirability), caregiver satisfaction (assessing satisfaction) with the app, and semistructured interviews (assessing participant experience using the app and feedback regarding features) were conducted at two time points, at enrollment and study completion.</p><p><strong>Results: </strong>The mean System Usability Scale score was 86.15 (SD 12.81) at enrollment and 73.13 (SD 16.13) at study completion, with most participants reporting the app easy to use and accepable during both time points. At enrollment, 80% (n=12) of caregivers reported that the app was effective in motivating them to stay on schedule, and 87% (n=13) indicated they would recommend it to others. At study completion, 75% (n=6) of caregivers found the app helpful for tracking their child's medication schedule, and 64% (n=5) would recommend it to others. Caregivers described the app as \"accessible,\" \"useful,\" and \"valuable.\" Qualitative interviews during both time points revealed caregivers' positive reactions to the app, particularly regarding medication reminders, tracking symptoms, and notes features, while also providing suggestions for improvements, such as integrating the BMT4me app with electronic medical records, incorporating educational content, adding fields for recording vital signs, and important phone numbers.</p><p><strong>Conclusions: </strong>The BMT4me app demonstrated promising usability as a mHealth intervention among pediatric patients undergoing HCT. Caregivers considered the app user-friendly and valuable, with positive feedback on its features, such as medication reminders and symptom tracking. Despite minor reported issues with app functionality, the overall acceptance of the app suggests its potential to support families in managing complex treatment. The findings from this study will inform the feasibility of testing in larger randomized controlled trials.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e66847"},"PeriodicalIF":3.3,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144181723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Digital Twins for Stratification of Patients with Breast Cancer and Treatment Optimization in Geriatric Oncology: Multivariate Clustering Analysis.","authors":"Pierre Heudel, Mashal Ahmed, Felix Renard, Arnaud Attye","doi":"10.2196/64000","DOIUrl":"https://doi.org/10.2196/64000","url":null,"abstract":"<p><strong>Background: </strong>Defining optimal adjuvant therapeutic strategies for older adult patients with breast cancer remains a challenge, given that this population is often overlooked and underserved in clinical research and decision-making tools.</p><p><strong>Objectives: </strong>This study aimed to develop a prognostic and treatment guidance tool tailored to older adult patients using artificial intelligence (AI) and a combination of clinical and biological features.</p><p><strong>Methods: </strong>A retrospective analysis was conducted on data from women aged 70+ years with HER2-negative early-stage breast cancer treated at the French Léon Bérard Cancer Center between 1997 and 2016. Manifold learning and machine learning algorithms were applied to uncover complex data relationships and develop predictive models. Predictors included age, BMI, comorbidities, hemoglobin levels, lymphocyte counts, hormone receptor status, Scarff-Bloom-Richardson grade, tumor size, and lymph node involvement. The dimension reduction technique PaCMAP was used to map patient profiles into a 3D space, allowing comparison with similar cases to estimate prognoses and potential treatment benefits.</p><p><strong>Results: </strong>Out of 1229 initial patients, 793 were included after data refinement. The selected predictors demonstrated high predictive efficacy for 5-year mortality, with mean area under the curve scores of 0.81 for Random Forest Classification and 0.76 for Support Vector Classifier. The tool categorized patients into prognostic clusters and enabled the estimation of treatment outcomes, such as chemotherapy benefits. Unlike traditional models that focus on isolated factors, this AI-based approach integrates multiple clinical and biological features to generate a comprehensive biomedical profile.</p><p><strong>Conclusions: </strong>This study introduces a novel AI-driven prognostic tool for older adult patients with breast cancer, enhancing treatment guidance by leveraging advanced machine learning techniques. The model provides a more nuanced understanding of disease dynamics and therapeutic strategies, emphasizing the importance of personalized oncology care.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e64000"},"PeriodicalIF":3.3,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144133195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Co-Designing a User-Centered Digital Health Tool for Supportive Care Needs of Patients With Brain Tumors and Their Caregivers: Interview Analysis.","authors":"Mahima Kalla, Ashleigh Bradford, Verena Schadewaldt, Kara Burns, Sarah C E Bray, Sarah Cain, Heidi McAlpine, Rana S Dhillon, Wendy Chapman, James R Whittle, Katharine J Drummond, Meinir Krishnasamy","doi":"10.2196/53690","DOIUrl":"https://doi.org/10.2196/53690","url":null,"abstract":"<p><strong>Background: </strong>Brain tumors are characterized by the high burden of disease that profoundly impacts the quality of life in patients and their families. Digital health tools hold tremendous potential to enhance supportive care and quality of life for patients with brain tumors and their caregivers.</p><p><strong>Objective: </strong>This study aims to generate ideas and concepts, through a co-design paradigm, to inform the development of a digital health tool to address the unmet needs of people affected by brain tumors.</p><p><strong>Methods: </strong>Patients with brain tumors, caregivers, and health professionals from 2 large public tertiary hospitals in Victoria, Australia, were invited to complete a qualitative interview discussing their unmet needs of care. Overall, 35 qualitative interviews focusing on unmet needs and concepts for a digital health tool were conducted with 13 patients, 11 caregivers, and 11 health professionals. Interviews were audio recorded and transcribed, and a 5-step framework analysis approach was used to analyze data.</p><p><strong>Results: </strong>Four themes of unmet supportive care needs emerged: (1) emotional and psychological, (2) information, (3) physical and practical, and (4) social connectedness. Participants expressed the desire for early and proactive mental health intervention, noted the importance of providing mental health support to caregivers, and emphasized the need for positive stories and affirmative language. From an information perspective, participants noted a sense of information overload, especially at the beginning. They also underscored the variety of information needed on an ongoing basis, including life after treatment, and comprehensive care assistance to maintain quality of life. Participants also described unmet supportive care needs relating to symptom burden, and practical and administrative support to facilitate the logistics of accessing treatment and accomplishing daily life tasks. Finally, they expressed the desire for greater social connectedness and safe spaces to engage with other people in a similar situation. Our findings are consistent with previous research on this subject and were integrated into the development of a web-based platform.</p><p><strong>Conclusions: </strong>Participants' perspectives informed the development of content for a web-based digital health platform called \"Brain Tumours Online.\" The platform comprises three pillars-(1) \"LEARN\": a repository of vetted information about a range of biomedical and psychosocial care topics; (2) \"CONNECT\": a digital peer support community with a health care professional interface; and (3) \"TOOLBOX\": an emerging library of validated digital therapeutics for symptom management.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e53690"},"PeriodicalIF":3.3,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144133190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating an AI Chatbot \"Prostate Cancer Info\" for Providing Quality Prostate Cancer Screening Information: Cross-Sectional Study.","authors":"Otis L Owens, Michael S Leonard","doi":"10.2196/72522","DOIUrl":"10.2196/72522","url":null,"abstract":"<p><strong>Background: </strong>Generative artificial intelligence (AI) chatbots may be useful tools for supporting shared prostate cancer (PrCA) screening decisions, but the information produced by these tools sometimes lack quality or credibility. \"Prostate Cancer Info\" is a custom GPT chatbot developed to provide plain-language PrCA information only from websites of key authorities on cancer and peer-reviewed literature.</p><p><strong>Objective: </strong>The objective of this paper was to evaluate the accuracy, completeness, and readability of Prostate Cancer Info's responses to frequently asked PrCA screening questions.</p><p><strong>Methods: </strong>A total of 23 frequently asked PrCA questions were individually input into Prostate Cancer Info. Responses were recorded in Microsoft Word and reviewed by 2 raters for their accuracy and completeness. Readability of content was determined by pasting responses into a web-based Flesch Kincaid Reading Ease Scores calculator.</p><p><strong>Results: </strong>Responses to all questions were accurate and culturally appropriate. In total, 17 of the 23 questions (74%) had complete responses. The average readability of responses was 64.5 (SD 8.7; written at an 8th-grade level).</p><p><strong>Conclusions: </strong>Generative AI chatbots, such as Prostate Cancer Info, are great starting places for learning about PrCA screening and preparing men to engage in shared decision-making but should not be used as independent sources of PrCA information because key information may be omitted. Men are encouraged to use these tools to complement information received from a health care provider.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e72522"},"PeriodicalIF":3.3,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12118940/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Deep Learning-Enabled Workflow to Estimate Real-World Progression-Free Survival in Patients With Metastatic Breast Cancer: Study Using Deidentified Electronic Health Records.","authors":"Gowtham Varma, Rohit Kumar Yenukoti, Praveen Kumar M, Bandlamudi Sai Ashrit, K Purushotham, C Subash, Sunil Kumar Ravi, Verghese Kurien, Avinash Aman, Mithun Manoharan, Shashank Jaiswal, Akash Anand, Rakesh Barve, Viswanathan Thiagarajan, Patrick Lenehan, Scott A Soefje, Venky Soundararajan","doi":"10.2196/64697","DOIUrl":"10.2196/64697","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Progression-free survival (PFS) is a crucial endpoint in cancer drug research. Clinician-confirmed cancer progression, namely real-world PFS (rwPFS) in unstructured text (ie, clinical notes), serves as a reasonable surrogate for real-world indicators in ascertaining progression endpoints. Response evaluation criteria in solid tumors (RECIST) is traditionally used in clinical trials using serial imaging evaluations but is impractical when working with real-world data. Manual abstraction of clinical progression from unstructured notes remains the gold standard. However, this process is a resource-intensive, time-consuming process. Natural language processing (NLP), a subdomain of machine learning, has shown promise in accelerating the extraction of tumor progression from real-world data in recent years.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objectives: &lt;/strong&gt;We aim to configure a pretrained, general-purpose health care NLP framework to transform free-text clinical notes and radiology reports into structured progression events for studying rwPFS on metastatic breast cancer (mBC) cohorts.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This study developed and validated a novel semiautomated workflow to estimate rwPFS in patients with mBC using deidentified electronic health record data from the Nference nSights platform. The developed workflow was validated in a cohort of 316 patients with hormone receptor-positive, human epidermal growth factor receptor-2 (HER-2) 2-negative mBC, who were started on palbociclib and letrozole combination therapy between January 2015 and December 2021. Ground-truth datasets were curated to evaluate the workflow's performance at both the sentence and patient levels. NLP-captured progression or a change in therapy line were considered outcome events, while death, loss to follow-up, and end of the study period were considered censoring events for rwPFS computation. Peak reduction and cumulative decline in Patient Health Questionnaire-8 (PHQ-8) scores were analyzed in the progressed and nonprogressed patient subgroups.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The configured clinical NLP engine achieved a sentence-level progression capture accuracy of 98.2%. At the patient level, initial progression was captured within ±30 days with 88% accuracy. The median rwPFS for the study cohort (N=316) was 20 (95% CI 18-25) months. In a validation subset (n=100), rwPFS determined by manual curation was 25 (95% CI 15-35) months, closely aligning with the computational workflow's 22 (95% CI 15-35) months. A subanalysis revealed rwPFS estimates of 30 (95% CI 24-39) months from radiology reports and 23 (95% CI 19-28) months from clinical notes, highlighting the importance of integrating multiple note sources. External validation also demonstrated high accuracy (92.5% sentence level; 90.2% patient level). Sensitivity analysis revealed stable rwPFS estimates across varying levels of missing source data and event definitions. Peak reduction in PHQ-8 scor","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e64697"},"PeriodicalIF":3.3,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12097284/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of Artificial Intelligence in Cardio-Oncology Imaging for Cancer Therapy-Related Cardiovascular Toxicity: Systematic Review.","authors":"Hayat Mushcab, Mohammed Al Ramis, Abdulrahman AlRujaib, Rawan Eskandarani, Tamara Sunbul, Anwar AlOtaibi, Mohammed Obaidan, Reman Al Harbi, Duaa Aljabri","doi":"10.2196/63964","DOIUrl":"10.2196/63964","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) is a revolutionary tool yet to be fully integrated into several health care sectors, including medical imaging. AI can transform how medical imaging is conducted and interpreted, especially in cardio-oncology.</p><p><strong>Objective: </strong>This study aims to systematically review the available literature on the use of AI in cardio-oncology imaging to predict cardiotoxicity and describe the possible improvement of different imaging modalities that can be achieved if AI is successfully deployed to routine practice.</p><p><strong>Methods: </strong>We conducted a database search in PubMed, Ovid MEDLINE, Cochrane Library, CINAHL, and Google Scholar from inception to 2023 using the AI research assistant tool (Elicit) to search for original studies reporting AI outcomes in adult patients diagnosed with any cancer and undergoing cardiotoxicity assessment. Outcomes included incidence of cardiotoxicity, left ventricular ejection fraction, risk factors associated with cardiotoxicity, heart failure, myocardial dysfunction, signs of cancer therapy-related cardiovascular toxicity, echocardiography, and cardiac magnetic resonance imaging. Descriptive information about each study was recorded, including imaging technique, AI model, outcomes, and limitations.</p><p><strong>Results: </strong>The systematic search resulted in 7 studies conducted between 2018 and 2023, which are included in this review. Most of these studies were conducted in the United States (71%), included patients with breast cancer (86%), and used magnetic resonance imaging as the imaging modality (57%). The quality assessment of the studies had an average of 86% compliance in all of the tool's sections. In conclusion, this systematic review demonstrates the potential of AI to enhance cardio-oncology imaging for predicting cardiotoxicity in patients with cancer.</p><p><strong>Conclusions: </strong>Our findings suggest that AI can enhance the accuracy and efficiency of cardiotoxicity assessments. However, further research through larger, multicenter trials is needed to validate these applications and refine AI technologies for routine use, paving the way for improved patient outcomes in cancer survivors at risk of cardiotoxicity.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e63964"},"PeriodicalIF":3.3,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12083731/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144049935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of Electronic Surveillance With Routine Monitoring for Patients With Lymphoma at High Risk of Relapse: Prospective Randomized Controlled Phase 3 Trial (Sentinel Lymphoma).","authors":"Katell Le Dû, Adrien Chauchet, Sophie Sadot-Lebouvier, Olivier Fitoussi, Bijou Fontanet, Arnaud Saint-Lezer, Frédéric Maloisel, Cédric Rossi, Sylvain Carras, Anne Parcelier, Magali Balavoine, Anne-Lise Septans","doi":"10.2196/65960","DOIUrl":"10.2196/65960","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Relapse is a major event in patients with lymphoma. Therefore, early detection may have an impact on quality of life and overall survival. Patient-reported outcome measures have demonstrated clinical benefits for patients with lung cancer; however, evidence is lacking in patients with lymphoma. We evaluated the effect of a web-mediated follow-up application for patients with lymphoma at high risk of relapse.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to demonstrate that monitoring patients via a web application enables the detection of at least 30% more significant events occurring between 2 systematic follow-up consultations with the specialist using an electronic questionnaire.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a prospective, randomized phase 3 trial comparing the impact of web-based follow-up (experimental arm) with a standard follow-up (control arm). The trial was based on a 2-step triangular test and was designed to have a power of 90% to detect a 30% improvement in the detection of significant events. A significant event was defined as a relapse, progression, or a serious adverse event. The study covered the follow-up period after completion of first-line treatment or relapse (24 months). Eligible patients were aged 18 years and older and had lymphoma at a high risk of relapse. In the experimental arm, patients received a 16-symptom questionnaire by email every 2 weeks. An email alert was sent to the medical team based on a predefined algorithm. The primary objective was assessed after the inclusion of the 40th patient. The study was continued for the duration of the analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 52 patients were included between July 12, 2017, and April 7, 2020, at 11 centers in France, with 27 in the experimental arm and 25 in the control arm. The median follow-up was 21.3 (range 1.3-25.6) months, and 121 events were reported during the study period. Most events occurred in the experimental arm (83/119, 69.7%) compared with 30.2% (36/119) in the control arm. A median number of 3.5 (range 1-8) events per patient occurred in the experimental arm, and 1.8 (range 1-6) occurred in the control arm (P=.01). Progression and infection were the most frequently reported events. Further, 19 patients relapsed during follow-up: 6 in the experimental arm and 13 in the control arm (P&lt;.001), with a median follow-up of 7.7 (range 2.8-20.6) months and 6.7 (range 1.9-16.4) months (P=.94), respectively. Statistical analysis was conducted after including the 40th patient, which showed no superiority of the experimental arm over the control arm. The study was therefore stopped after the 52nd patient was enrolled.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The primary objective was not reached; however, patient-reported outcome measures remain essential for detecting adverse events in patients with cancer, and the electronic monitoring method needs to demonstrate its effectiveness and comply with ","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e65960"},"PeriodicalIF":3.3,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12070818/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144049785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Artificial Intelligence for Digital Symptom Management in Oncology: The Development of CRCWeb.","authors":"Sizuo Liu, Yufen Lin, Runze Yan, Zhiyuan Wang, Delgersuren Bold, Xiao Hu","doi":"10.2196/68516","DOIUrl":"https://doi.org/10.2196/68516","url":null,"abstract":"<p><strong>Unstructured: </strong>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.</p><p><strong>International registered report: </strong>RR2-10.2196/48499.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143987280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Process Re-Engineering and Data Integration Using Fast Healthcare Interoperability Resources for the Multidisciplinary Treatment of Lung Cancer.","authors":"Ching-Hsiung Lin, Bing-Yen Wang, Sheng-Hao Lin, Pei Hsuan Shih, Chin-Jing Lee, Yung Ting Huang, Shih Chieh Chen, Mei-Lien Pan","doi":"10.2196/53887","DOIUrl":"https://doi.org/10.2196/53887","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Unlabelled: &lt;/strong&gt;Multidisciplinary team (MDT) meetings play a critical role in cancer care by fostering collaboration between different health care professionals to develop optimal treatment recommendations. However, meeting scheduling and coordination rely heavily on manual work, making information-sharing and integration challenging. This results in incomplete information, affecting decision-making efficiency and impacting the progress of MDT. This project aimed to optimize and digitize the MDT workflow by interviewing the members of an MDT and implementing an integrated information platform using the Fast Healthcare Interoperability Resources (FHIR) standard. MDT process re-engineering was conducted at a central Taiwan medical center. To digitize the workflow, our hospital adopted the NAVIFY Tumor Board (NTB), a cloud-based platform integrating medical data using international standards, including Logical Object Identifiers, Names, and Codes, Systemized Nomenclature of Medicine-Clinical Terms, M-code, and FHIR. We improved our hospital's information system using application programming interfaces to consolidate data from various systems, excluding sensitive cases. Using FHIR, we aggregated, analyzed, and converted the data for seamless integration. Using a user experience design, we gained insights into the lung cancer MDT's processes and needs. We conducted 2 phases: pre- and post-NTB integration. Ethnographic observations and stakeholder interviews revealed pain points. The affinity diagram method categorizes the pain points during the discussion process, leading to efficient solutions. We divided the observation period into 2 phases: before and after integrating the NTB with the hospital information system. In phase 1, there were 83 steps across the 6 MDT activities, leading to inefficiencies and potential delays in patient care. In phase 2, we streamlined the tumor board process into 33 steps by introducing new functions and optimizing the data entry for pathologists. We converted the related medical data to the FHIR format using 6 FHIR resources and improved our hospital information system by developing functions and application programming interfaces to interoperate among various systems; consolidating data from different sources, excluding sensitive cases; and enhancing overall system efficiency. The MDT workflow reduced steps by 60% (50/83), lowering the coordinated activity time from 30 to 5 minutes. Improved efficiency boosted productivity and coordination in each case of manager feedback. This study optimized and digitized the workflow of MDT meetings, significantly enhancing the efficiency and accuracy of the tumor board process to benefit both medical professionals and patients. Based on FHIR, we integrated the data scattered across different information systems in our hospital and established a system interoperability interface that conformed to the standard. While digitizing the work of MDT meetings, we also promo","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e53887"},"PeriodicalIF":3.3,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12068834/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144001377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Examining Demographic, Geographic, and Temporal Patterns of Melanoma Incidence in Texas From 2000 to 2018: Retrospective Study.","authors":"Kehe Zhang, Madison M Taylor, Jocelyn Hunyadi, Hung Q Doan, Adewole S Adamson, Paige Miller, Kelly C Nelson, Cici Bauer","doi":"10.2196/67902","DOIUrl":"https://doi.org/10.2196/67902","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Melanoma currently ranks as the fifth leading cancer diagnosis and is projected to become the second most common cancer in the United States by 2040. Melanoma detected at earlier stages may be treated with less-risky and less-costly therapeutic options.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to analyze temporal and spatial trends in melanoma incidence by stage at diagnosis (overall, early, and late) in Texas from 2000 to 2018, focusing on demographic and geographic variations to identify high-risk populations and regions for targeted prevention efforts.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We used melanoma incidence data from all 254 Texas counties from the Texas Cancer Registry (TCR) from 2000 to 2018, aggregated by county and year. Among these, 250 counties reported melanoma cases during the period. Counties with no cases reported in a certain year were treated as having no cases. Melanoma cases were classified by SEER Summary Stage and stratified by the following four key covariates: age, sex, race and ethnicity, and stage at diagnosis. Incidence rates (IRs) were calculated per 100,000 population, and temporal trends were analyzed using joinpoint regression to determine average annual percentage changes (AAPCs) with 95% CIs for the whole time period (2000-2018), the most recent 10-year period (2009-2018), and the most recent 5-year period (2014-2018). Heat map visualizations were developed to assess temporal trends by patient age, year of diagnosis, stage at diagnosis, sex, and race and ethnicity. Spatial cluster analysis was conducted using Getis-Ord Gi* statistics to identify county-level geographic clusters of high and low melanoma incidence by stage at diagnosis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 82,462 melanoma cases were recorded, of which 74.7% (n=61,588) were early stage, 11.3% (n=9,352) were late stage, and 14% (n=11,522) were of unknown stage. Most cases were identified as males and non-Hispanic White individuals. Melanoma IRs increased from 2000 to 2018, particularly among older adults (60+ years; AAPC range 1.20%-1.84%; all P values were &lt;.001), males (AAPC 1.59%; P&lt;.001), and non-Hispanic White individuals (AAPC of 3.24% for early stage and 2.38% for late stage; P&lt;.001 for early stage and P = .03 for late state). Early-stage diagnoses increased while the rates of late-stage diagnoses remained stable for the overall population. The spatial analysis showed that urban areas had higher early-stage incidence rates (P=.06), whereas rural areas showed higher late-stage incidence rates (P=.05), indicating possible geographic-based differences in access to dermatologic care.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Melanoma incidence in Texas increased over the study time period, with the most-at-risk populations being non-Hispanic White individuals, males, and individuals aged 50 years and older. The stable rates of late-stage melanoma among racial and ethnic minority populations and rural populat","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e67902"},"PeriodicalIF":3.3,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12064134/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144037828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}