JMIR Cancer最新文献

{"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":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>A total of 82,462 melanoma cases were recorded, of which 74.7% (n=61,588) were early stage, 11.3% (n=9,352) were late stage, and 14% (n=11,522) were of unknown stage. Most cases were identified as males and non-Hispanic White individuals. Melanoma IRs increased from 2000 to 2018, particularly among older adults (60+ years; AAPC range 1.20%-1.84%; all P values were <.001), males (AAPC 1.59%; P<.001), and non-Hispanic White individuals (AAPC of 3.24% for early stage and 2.38% for late stage; P<.001 for early stage and P = .03 for late state). Early-stage diagnoses increased while the rates of late-stage diagnoses remained stable for the overall population. The spatial analysis showed that urban areas had higher early-stage incidence rates (P=.06), whereas rural areas showed higher late-stage incidence rates (P=.05), indicating possible geographic-based differences in access to dermatologic care.</p><p><strong>Conclusions: </strong>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}

{"title":"A Digital Home-Based Health Care Center for Remote Monitoring of Side Effects During Breast Cancer Therapy: Prospective, Single-Arm, Monocentric Feasibility Study.","authors":"Hanna Huebner, Lena A Wurmthaler, Chloë Goossens, Mathias Ernst, Alexander Mocker, Annika Krückel, Maximilian Kallert, Jürgen Geck, Milena Limpert, Katharina Seitz, Matthias Ruebner, Philipp Kreis, Felix Heindl, Manuel Hörner, Bernhard Volz, Eduard Roth, Carolin C Hack, Matthias W Beckmann, Sabrina Uhrig, Peter A Fasching","doi":"10.2196/64083","DOIUrl":"https://doi.org/10.2196/64083","url":null,"abstract":"<p><strong>Background: </strong>The introduction of oral anticancer therapies has, at least partially, shifted treatment from clinician-supervised hospital care to patient-managed home regimens. However, patients with breast cancer receiving oral cyclin-dependent kinase 4/6 inhibitor therapy still require regular hospital visits to monitor side effects. Telemonitoring has the potential to reduce hospital visits while maintaining quality care.</p><p><strong>Objective: </strong>This study aims to develop a digital home-based health care center (DHHC) for acquiring electrocardiograms (ECGs), white blood cell (WBC) counts, side effect photo documentation, and patient-reported quality of life (QoL) data.</p><p><strong>Methods: </strong>The DHHC was set up using an Apple Watch Series 6 (ECG measurements), a HemoCue WBC DIFF Analyzer (WBC counts), an iPhone SE (QoL assessments and photo documentation), a TP-Link M7350-4G Wi-Fi router, and a Raspberry Pi 4 Model B. A custom-built app stored and synchronized remotely collected data with the clinic. The feasibility and acceptance of the DHHC among patients with breast cancer undergoing cyclin-dependent kinase 4/6 inhibitor therapy were evaluated in a prospective, single-arm, monocentric study. Patients (n=76) monitored side effects-ECGs, WBC counts, photo documentation, and QoL-at 3 predefined time points: study inclusion (on-site), day 14 (remote), and day 28 (remote). After the study completion, patients completed a comprehensive questionnaire on user perception and feasibility. Adherence to scheduled visits, the success rate of the data transfer, user perception and feasibility, and the clinical relevance of remote measurements were evaluated.</p><p><strong>Results: </strong>Mean adherence to the planned remote visits was 63% on day 14 and 37% on day 28. ECG measurements were performed most frequently (day 14: 57/76, 75%; day 28: 31/76, 41%). The primary patient-reported reason for nonadherence was device malfunction. The expected versus the received data transfer per patient was as follows: ECGs: 3 versus 3.04 (SD 1.9); WBC counts: 3 versus 2.14 (SD 1.14); QoL questionnaires: 3 versus 2.5 (SD 1.14); and photo documentation: 6 versus 4.4 (SD 3.36). Among patients, 81% (55/68) found ECG measurements easy, 82% (55/67) found photo documentation easy, and 48% (33/69) found WBC measurements easy. Additionally, 61% (40/66) of patients felt comfortable with self-monitoring and 79% (54/68) were willing to integrate remote monitoring into their future cancer care. Therapy-induced decreased neutrophil count was successfully detected (P<.001; mean baseline: 4.3, SD 2.2, ×109/L; on-treatment: 1.8, SD 0.8, ×109/L). All-grade neutropenia and corrected QT interval prolongations were detected in 80% (55/68) and 2% (1/42) of patients, respectively.</p><p><strong>Conclusions: </strong>Adherence to scheduled remote visits was moderate, with nonadherence primarily attributed to device-related complications, which may have ","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e64083"},"PeriodicalIF":3.3,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12064077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144038986","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":"Association Between Risk Factors and Major Cancers: Explainable Machine Learning Approach.","authors":"Xiayuan Huang, Shushun Ren, Xinyue Mao, Sirui Chen, Elle Chen, Yuqi He, Yun Jiang","doi":"10.2196/62833","DOIUrl":"https://doi.org/10.2196/62833","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;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&lt;.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&lt;.01), diabetes (OR 1.34, 95% CI 1.29-1.39; P&lt;.01), depressive disorders (OR 1.11, 95% CI 1.06-1.16; P&lt;.01), heart diseases (OR 1.42, 95% CI 1.32-1.52; P&lt;.01), and anemia (OR 1.22, 95% CI 1.14-1.30; P&lt;.01). The similarity analysis indicated the unique risk factor pattern for lung cancer from other cancer types.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;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 re","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e62833"},"PeriodicalIF":3.3,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12064211/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144023868","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":"Co-Designing Priority Components of an mHealth Intervention to Enhance Follow-Up Care in Young Adult Survivors of Childhood Cancer and Health Care Providers: Qualitative Descriptive Study.","authors":"Sharon H J Hou, Brianna Henry, Rachelle Drummond, Caitlin Forbes, Kyle Mendonça, Holly Wright, Iqra Rahamatullah, Perri R Tutelman, Hailey Zwicker, Mehak Stokoe, Jenny Duong, Emily K Drake, Craig Erker, Michael S Taccone, Liam Sutherland, Paul Nathan, Maria Spavor, Karen Goddard, Kathleen Reynolds, Fiona S M Schulte","doi":"10.2196/57834","DOIUrl":"https://doi.org/10.2196/57834","url":null,"abstract":"<p><strong>Background: </strong>Survivors of childhood cancer are at risk of medical, psychological, and social late effects. To screen for their risks, receipt of consistent, cancer-specific follow-up care is crucial. However, <50% of survivors attend their aftercare, and only 35% of them recognize that they could have a serious health problem. The use of mobile health (mHealth) is a promising form of intervention to educate, connect, and empower survivors of childhood cancer on the importance of follow-up care.</p><p><strong>Objective: </strong>This study aimed to use co-design to identify the priority components to include in an mHealth intervention with young adult (aged between 18 and 39 years) survivors of childhood cancer and health care providers.</p><p><strong>Methods: </strong>This study was conducted between January and November 2022 in Canada and used patient-oriented research methods. Participants were recruited through local or provincial long-term follow-up clinics, using convenience sampling from patient partners who assisted in recruiting survivors across geographical areas in western, central, and eastern Canada, and social media outreach (X, formally known as Twitter; Facebook; and Instagram). Qualitative descriptive data (focus group interviews) from survivors of childhood cancer and health care providers (individual interviews) were gathered. We analyzed the collected data using reflexive thematic analysis and verified it through member checking techniques through an online community engagement event.</p><p><strong>Results: </strong>We conducted with patient partners 5 online (Zoom) focus groups with 22 survivors of childhood cancer (mean age 29.19, SD 4.78 y). We conducted individual telephone interviews with 7 health care providers. Participants identified five priority areas to be included in an mHealth intervention: (1) connections, (2) education and information, (3) engagement, (4) personalization, and (5) resources. Results were shared with and validated by survivors of childhood cancer, their families, health care providers, and academic researchers as part of a community engagement event. Small and large group discussions were facilitated to allow participants to review and discuss the accuracy of the themes derived regarding the core components to be included in mHealth. A graphic recording artist visually captured key ideas from the event. A subset of the participants also completed a web-based satisfaction survey, and responses indicated that the community engagement event was generally well received.</p><p><strong>Conclusions: </strong>Results from this study have provided the necessary foundation to progress in intervention development. The next step of this multiphased project is to build an innovative and accessible mHealth intervention prototype that is based on the identified core components and is grounded in an established conceptual framework for co-design of mHealth.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e57834"},"PeriodicalIF":3.3,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12064980/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144044260","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":"Assessing the Quality and Reliability of ChatGPT's Responses to Radiotherapy-Related Patient Queries: Comparative Study With GPT-3.5 and GPT-4.","authors":"Ana Grilo, Catarina Marques, Maria Corte-Real, Elisabete Carolino, Marco Caetano","doi":"10.2196/63677","DOIUrl":"https://doi.org/10.2196/63677","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Patients frequently resort to the internet to access information about cancer. However, these websites often lack content accuracy and readability. Recently, ChatGPT, an artificial intelligence-powered chatbot, has signified a potential paradigm shift in how patients with cancer can access vast amounts of medical information, including insights into radiotherapy. However, the quality of the information provided by ChatGPT remains unclear. This is particularly significant given the general public's limited knowledge of this treatment and concerns about its possible side effects. Furthermore, evaluating the quality of responses is crucial, as misinformation can foster a false sense of knowledge and security, lead to noncompliance, and result in delays in receiving appropriate treatment.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to evaluate the quality and reliability of ChatGPT's responses to common patient queries about radiotherapy, comparing the performance of ChatGPT's two versions: GPT-3.5 and GPT-4.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We selected 40 commonly asked radiotherapy questions and entered the queries in both versions of ChatGPT. Response quality and reliability were evaluated by 16 radiotherapy experts using the General Quality Score (GQS), a 5-point Likert scale, with the median GQS determined based on the experts' ratings. Consistency and similarity of responses were assessed using the cosine similarity score, which ranges from 0 (complete dissimilarity) to 1 (complete similarity). Readability was analyzed using the Flesch Reading Ease Score, ranging from 0 to 100, and the Flesch-Kincaid Grade Level, reflecting the average number of years of education required for comprehension. Statistical analyses were performed using the Mann-Whitney test and effect size, with results deemed significant at a 5% level (P=.05). To assess agreement between experts, Krippendorff α and Fleiss κ were used.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;GPT-4 demonstrated superior performance, with a higher GQS and a lower number of scores of 1 and 2, compared to GPT-3.5. The Mann-Whitney test revealed statistically significant differences in some questions, with GPT-4 generally receiving higher ratings. The median (IQR) cosine similarity score indicated substantial similarity (0.81, IQR 0.05) and consistency in the responses of both versions (GPT-3.5: 0.85, IQR 0.04; GPT-4: 0.83, IQR 0.04). Readability scores for both versions were considered college level, with GPT-4 scoring slightly better in the Flesch Reading Ease Score (34.61) and Flesch-Kincaid Grade Level (12.32) compared to GPT-3.5 (32.98 and 13.32, respectively). Responses by both versions were deemed challenging for the general public.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Both GPT-3.5 and GPT-4 demonstrated having the capability to address radiotherapy concepts, with GPT-4 showing superior performance. However, both models present readability challenges for the general p","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e63677"},"PeriodicalIF":3.3,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12017613/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144049957","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":"The Efficacy of Digital Interventions on Adherence to Oral Systemic Anticancer Therapy Among Patients With Cancer: Systematic Review and Meta-Analysis.","authors":"Wan-Chuen Liao, Fiona Angus, Jane Conley, Li-Chia Chen","doi":"10.2196/64208","DOIUrl":"https://doi.org/10.2196/64208","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Digital interventions have been increasingly applied in multidisciplinary care plans to improve medication adherence to oral systemic anticancer therapy (SACT), the crucial lifesaving treatments for many cancers. However, there is still a lack of consensus on the efficacy of those digital interventions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objectives: &lt;/strong&gt;This systematic review and meta-analysis aimed to investigate the efficacy of digital interventions in improving adherence to oral SACTs in patients with cancer.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This systematic review and meta-analysis followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement guidelines. The protocol has been registered at PROSPERO (no. CRD42024550203). Fully published, randomized controlled trials (RCTs) in English on adults with cancer assessing digital interventions for improving adherence to oral SACTs were retrieved from MEDLINE, Embase, APA PsycINFO, and CINAHL Plus up to May 31, 2024. Adherence measures compared between digital intervention users and nonusers were extracted. The proportions of poor adherence were synthesized using a random-effects model. The pooled results were reported as the odds ratio and 95% CI. The heterogeneity was assessed with the I2 test (%). The mean difference and 95% CI were calculated from the mean adherence score and SD. A risk of bias assessment was conducted using version 2 of the Cochrane Risk of Bias Assessment Tool (RoB 2) for RCTs, which ensured that a quality assessment of all included studies was conducted as recommended by the Cochrane Collaboration.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;This study included 13 RCTs on digital interventions for improving adherence to oral SACTs in patients with cancer. The 13 RCTs, published between 2016 and 2024, were conducted in the United States, South Korea, France, Egypt, Finland, Australia, Colombia, Singapore, and Turkey. The technologies used were mobile apps (n=4), reminder systems (n=4), telephone follow-ups (n=3), and interactive multimedia platforms (n=2). Adherence was measured by surveys (n=8), relative dose intensity (n=2), pill count (n=1), self-reported missed doses (n=1), a smart pill bottle (n=1), and urine aromatase inhibitor metabolite assays (n=1). Concerns regarding risk of bias primarily involved randomization, missing outcome data, and outcome measurement, including nonblinded randomization, subjective patient-reported data, and difficulties in distinguishing between missed appointments and actual medication nonadherence. Pooled results from 11 trials showed that digital technology users had significantly lower risk of poor adherence (odds ratio 0.60, 95% CI 0.47-0.77). Two studies reported positive mean differences in adherence scores comparing digital intervention users and nonusers. However, due to considerable heterogeneity (I²=73.1%), it is difficult to make a definitive conclusion from the pooled results about the effect ","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e64208"},"PeriodicalIF":3.3,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12017607/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144050699","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":"Caring Through the Final Phase: A Meta-Synthesis of Family Experiences, Needs and Perceptions in Home-Based Hospice Care for Terminal Cancer Patients.","authors":"Xin Ming Deng, Kanokwan Hounsri, Violeta Lopez, Wilson Wai-San Tam","doi":"10.2196/71596","DOIUrl":"10.2196/71596","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This meta-synthesis aims to systematically review and synthesise qualitative evidence regarding the experiences, needs, and perceptions of family caregivers in home-based hospice care for terminal cancer patients. The goal is identifying key themes that can improve caregiver support and service delivery.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A systematic search was conducted across MEDLINE, EMBASE, SCOPUS, PsycINFO, CINAHL, Google Scholar and relevant grey literature sources up to 14 March 2025. Studies were included if they focused on family caregivers' experiences in home-based hospice care settings, excluding those that addressed only patients or healthcare providers. Two independent reviewers performed study selection, data extraction, and quality assessment using the Critical Appraisal Skills Programme (CASP) checklist. Data were synthesised using a three-step thematic synthesis approach, and the confidence in the findings was assessed via the GRADE-CERQual framework.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Five studies published between 1989 and 2022 from diverse geographical regions (including Asia and Western settings) met the inclusion criteria. Two major themes emerged: (1) Being Physically and Emotionally Present-where caregivers expressed a strong commitment to remain with their loved ones, providing emotional support and maintaining a sense of control; and (2) Sharing Responsibilities-which underscored the importance of both formal support from palliative care teams and informal support from family and friends in mitigating caregiver burden. These findings directly address the study's aims by illustrating how caregivers balance emotional commitment with the practical challenges of providing home-based care.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;While family caregivers are dedicated to delivering high-quality, personalised care, they encounter significant emotional and logistical challenges. Variability in study settings, potential recall bias from retrospective interviews, and limited grey literature access may affect the generalisability of the findings. This meta-synthesis underscores the essential role of family involvement in home-based hospice care for terminal cancer patients. The combined reliance on emotional commitment and shared responsibilities-with support from professional care teams-is vital for optimal care delivery. Future interventions should enhance formal and informal support systems to meet family caregivers' diverse needs better.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Clinicaltrial: &lt;/strong&gt;Pro","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144024091","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":"Ethical Design of Data-Driven Decision Support Tools for Improving Cancer Care: Embedded Ethics Review of the 4D PICTURE Project.","authors":"Marieke Bak, Laura Hartman, Charlotte Graafland, Ida J Korfage, Alena Buyx, Maartje Schermer","doi":"10.2196/65566","DOIUrl":"https://doi.org/10.2196/65566","url":null,"abstract":"<p><p>Oncology patients often face complex choices between treatment regimens with different risk-benefit ratios. The 4D PICTURE (Producing Improved Cancer Outcomes Through User-Centered Research) project aims to support patients, their families, and clinicians with these complex decisions by developing data-driven decision support tools (DSTs) for patients with breast cancer, prostate cancer, and melanoma as part of care path redesign using a methodology called MetroMapping. There are myriad ethical issues to consider as the project will create data-driven prognostic models and develop conversation tools using artificial intelligence while including patient perspectives by setting up boards of experiential experts in 8 different countries. This paper aims to review the key ethical challenges related to the design and development of DSTs in oncology. To explore the ethics of DSTs in cancer care, the project adopted the Embedded Ethics approach-embedding ethicists into research teams to sensitize team members to ethical aspects and assist in reflecting on those aspects throughout the project. We conducted what we call an embedded review of the project drawing from key literature on topics related to the different work packages of the 4D PICTURE project, whereas the analysis was an iterative process involving discussions with researchers in the project. Our review identified 13 key ethical challenges related to the development of DSTs and the redesigning of care paths for more personalized cancer care. Several ethical aspects were related to general potential issues of data bias and privacy but prompted specific research questions, for instance, about the inclusion of certain demographic variables in models. Design methodology in the 4D PICTURE project can provide insights related to design justice, a novel consideration in health care DSTs. Ethical points of attention related to health care policy, such as cost-effectiveness, financial sustainability, and environmental impact, were also identified, along with challenges in the research process itself, emphasizing the importance of epistemic justice, the role of embedded ethicists, and psychological safety. This viewpoint highlights ethical aspects previously neglected in the digital health ethics literature and zooms in on real-world challenges in an ongoing project. It underscores the need for researchers and leaders in data-driven medical research projects to address ethical challenges beyond the scientific core of the project. More generally, our tailored review approach provides a model for embedding ethics into large data-driven oncology research projects from the start, which helps ensure that technological innovations are designed and developed in an appropriate and patient-centered manner.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e65566"},"PeriodicalIF":3.3,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12022531/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144053407","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":"Evaluation of Large Language Models in Tailoring Educational Content for Cancer Survivors and Their Caregivers: Quality Analysis.","authors":"Darren Liu, Xiao Hu, Canhua Xiao, Jinbing Bai, Zahra A Barandouzi, Stephanie Lee, Caitlin Webster, La-Urshalar Brock, Lindsay Lee, Delgersuren Bold, Yufen Lin","doi":"10.2196/67914","DOIUrl":"10.2196/67914","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Cancer survivors and their caregivers, particularly those from disadvantaged backgrounds with limited health literacy or racial and ethnic minorities facing language barriers, are at a disproportionately higher risk of experiencing symptom burdens from cancer and its treatments. Large language models (LLMs) offer a promising avenue for generating concise, linguistically appropriate, and accessible educational materials tailored to these populations. However, there is limited research evaluating how effectively LLMs perform in creating targeted content for individuals with diverse literacy and language needs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to evaluate the overall performance of LLMs in generating tailored educational content for cancer survivors and their caregivers with limited health literacy or language barriers, compare the performances of 3 Generative Pretrained Transformer (GPT) models (ie, GPT-3.5 Turbo, GPT-4, and GPT-4 Turbo; OpenAI), and examine how different prompting approaches influence the quality of the generated content.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We selected 30 topics from national guidelines on cancer care and education. GPT-3.5 Turbo, GPT-4, and GPT-4 Turbo were used to generate tailored content of up to 250 words at a 6th-grade reading level, with translations into Spanish and Chinese for each topic. Two distinct prompting approaches (textual and bulleted) were applied and evaluated. Nine oncology experts evaluated 360 generated responses based on predetermined criteria: word limit, reading level, and quality assessment (ie, clarity, accuracy, relevance, completeness, and comprehensibility). ANOVA (analysis of variance) or chi-square analyses were used to compare differences among the various GPT models and prompts.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Overall, LLMs showed excellent performance in tailoring educational content, with 74.2% (267/360) adhering to the specified word limit and achieving an average quality assessment score of 8.933 out of 10. However, LLMs showed moderate performance in reading level, with 41.1% (148/360) of content failing to meet the sixth-grade reading level. LLMs demonstrated strong translation capabilities, achieving an accuracy of 96.7% (87/90) for Spanish and 81.1% (73/90) for Chinese translations. Common errors included imprecise scopes, inaccuracies in definitions, and content that lacked actionable recommendations. The more advanced GPT-4 family models showed better overall performance compared to GPT-3.5 Turbo. Prompting GPTs to produce bulleted-format content was likely to result in better educational content compared with textual-format content.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;All 3 LLMs demonstrated high potential for delivering multilingual, concise, and low health literacy educational content for cancer survivors and caregivers who face limited literacy or language barriers. GPT-4 family models were notably more robust. While fur","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e67914"},"PeriodicalIF":2.7,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11995809/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796509","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":"Digital Health Intervention to Reduce Malnutrition Among Individuals With Gastrointestinal Cancer Receiving Cytoreductive Surgery Combined With Hyperthermic Intraperitoneal Chemotherapy: Feasibility, Acceptability, and Usability Trial.","authors":"Yu Chen Lin, Ryan Hagen, Benjamin D Powers, Sean P Dineen, Jeanine Milano, Emma Hume, Olivia Sprow, Sophia Diaz-Carraway, Jennifer B Permuth, Jeremiah Deneve, Amir Alishahi Tabriz, Kea Turner","doi":"10.2196/67108","DOIUrl":"10.2196/67108","url":null,"abstract":"<p><strong>Background: </strong>Cytoreductive surgery combined with hyperthermic intraperitoneal chemotherapy (CRS-HIPEC) can improve survival outcomes for individuals with gastrointestinal (GI) cancer and peritoneal disease (PD). Individuals with GI cancer and PD receiving CRS-HIPEC are at increased risk for malnutrition. Despite the increased risk for malnutrition, there has been limited study of nutritional interventions for individuals receiving CRS-HIPEC.</p><p><strong>Objective: </strong>We aimed to test the feasibility, acceptability, and usability of Support Through Remote Observation and Nutrition Guidance (STRONG), a multilevel digital health intervention to improve nutritional management among individuals with GI cancer and PD receiving CRS-HIPEC. We also assessed patient-reported outcomes, including malnutrition risk, health-related quality of life, and weight-related measures.</p><p><strong>Methods: </strong>STRONG is a 12-week digital intervention in which participants received biweekly nutritional counseling with a dietitian, logged food intake using a Fitbit tracker, and reported nutrition-related outcomes. Dietitians received access to a web-based dashboard and remotely monitored patients' reported food intake and nutrition-impact symptoms. Implementation outcomes were assessed against prespecified benchmarks consistent with benchmarks used in prior studies. Changes in patient-reported outcomes at baseline and follow-up were assessed using linear and ordered logistic regressions.</p><p><strong>Results: </strong>Participants (N=10) had a median age of 57.5 (IQR 54-69) years. Feasibility benchmarks were achieved for recruitment (10/17, 59% vs benchmark: 50%), study assessment completion (9/10, 90% vs benchmark: 60%), dietitian appointment attendance (7/10, 70% vs benchmark: 60%), daily food intake logging adherence (6/10, 60% vs benchmark: 60%), and participant retention (10/10, 100% vs benchmark: 60%). Most participants rated the intervention as acceptable (8/10, 80% vs benchmark: 70%) and reported a high level of usability for dietitian services (10/10, 100%). The benchmark usability for the Fitbit tracker to log food intake was not met. Compared to baseline, participants saw on average a 6.0 point reduction in malnutrition risk score (P=.01), a 20.5 point improvement in general health-related quality of life score (P=.002), and a 5.6 percentage point increase in 1-month weight change (P=.04) at the end of the study.</p><p><strong>Conclusions: </strong>The STRONG intervention demonstrated to be feasible, acceptable, and usable among individuals with GI cancer and PD receiving CRS-HIPEC. A fully powered randomized controlled trial is needed to test the effectiveness of STRONG for reducing malnutrition and improving patient outcomes.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e67108"},"PeriodicalIF":3.3,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11996150/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143804432","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}