JMIR Cancer最新文献

{"title":"Cancer Information Burden Among US Cancer Survivors Using Health Information National Trends Survey 2022 and 2024: Cross-Sectional Study.","authors":"Jingjing Gao, Sharon V Munroe, Yue Zhang, Jason H Windett","doi":"10.2196/101310","DOIUrl":"https://doi.org/10.2196/101310","url":null,"abstract":"<p><strong>Background: </strong>Digital health platforms have become increasingly important sources of health information for cancer survivors. Cancer survivors frequently rely on digital resources to obtain information related to diagnosis, treatment, survivorship, and follow-up care. In this study, cancer information burden is defined as the perceived challenges associated with seeking, understanding, and evaluating cancer-related information, including the effort required to obtain information, frustration during information seeking, difficulty understanding information, and concern regarding information quality. As the online health information environment becomes increasingly complex, some survivors may experience difficulty locating, evaluating, and understanding cancer-related information. However, limited nationally representative research has examined how cancer information burden differs across demographic and socioeconomic groups.</p><p><strong>Objective: </strong>This study examined cancer information burden and associated demographic and socioeconomic factors among US cancer survivors using nationally representative data from the Health Information National Trends Survey (HINTS) 2022 and 2024.</p><p><strong>Methods: </strong>Data were drawn from pooled cycles of the HINTS conducted in 2022 and 2024. Survey-weighted regression analyses were used to evaluate factors associated with cancer information burden among cancer survivors. Outcomes included the perceived effort required to obtain cancer information, frustration during information seeking, difficulty understanding cancer information, and concerns regarding information quality. Composite measures of cancer information burden were also examined. Models were adjusted for demographic and socioeconomic characteristics, including age, sex, race and ethnicity, educational attainment, household income, geographic region, and survey cycle.</p><p><strong>Results: </strong>Hispanic cancer survivors reported a significantly greater cancer information burden, including higher levels of perceived effort and frustration associated with obtaining and understanding cancer-related information, compared with non-Hispanic White survivors. Non-Hispanic Asian survivors also demonstrated elevated levels of information burden, although estimates were less precise because of smaller subgroup sample sizes. Postbaccalaureate education was associated with lower frustration during cancer information seeking. Female survivors reported lower levels of difficulty understanding cancer-related information and lower overall information burden than male survivors. Differences between the 2022 and 2024 survey cycles were not statistically significant after adjustment.</p><p><strong>Conclusions: </strong>Differences in cancer information burden persist among US cancer survivors. Hispanic and non-Hispanic Asian survivors, and individuals with lower educational attainment, may experience greater challenges obtaining a","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"12 ","pages":"e101310"},"PeriodicalIF":2.6,"publicationDate":"2026-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13717679/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149301021","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":"Psychosocial Distress and Radiation Treatment Interruptions in Patients With Cancer Using Machine Learning and Regression: Retrospective Cohort Study.","authors":"Soheil Hashtarkhani, Christopher L Brett, Fekede Asefa Kumsa, Rezaur Rashid, Parnian Kheirkhah Rahimabad, Saba Kheirinejad, Robert L Davis, David L Schwartz, Arash Shaban-Nejad","doi":"10.2196/92899","DOIUrl":"10.2196/92899","url":null,"abstract":"<p><strong>Background: </strong>Uninterrupted delivery of radiation therapy is critical to cancer treatment quality. Unplanned radiation treatment interruptions compromise tumor control and survival. Although previous studies have explored clinical and sociodemographic factors influencing treatment adherence, the role of psychosocial distress and related symptoms remains unclear.</p><p><strong>Objective: </strong>This study aims to identify patient-reported psychosocial predictors of radiation treatment interruptions from patient-reported distress screening data integrated with clinical and socioeconomic correlates via traditional and machine learning-enhanced modeling.</p><p><strong>Methods: </strong>This institutional cohort study included 1953 patients who underwent external beam radiation therapy between August 2020 and May 2024. Patient demographics, clinical characteristics, treatment details, and baseline distress levels were extracted from a hospital-level electronic health record data repository. Distress was assessed using the Distress Thermometer developed by the National Comprehensive Cancer Network. These data were linked with census tract-level socioeconomic indicators. Predictors of treatment interruption (measured as missed treatment days) were evaluated using negative binomial regression and an extreme gradient boosting (XGBoost) model with a Poisson objective function.</p><p><strong>Results: </strong>The study cohort missed a mean of 1.19 (SD 2.18) treatment days (range: 0-15). Higher distress scores, the presence of learning or communication barriers, and genitourinary malignancy were significantly associated with increased interruption risk in the negative binomial regression model. The XGBoost model confirmed these features as top contributors to prediction performance, while yielding an incrementally lower root mean squared error of 2.19 (vs 2.21 for the regression model).</p><p><strong>Conclusions: </strong>Patient-reported distress and health education barriers were modestly associated with treatment interruptions, underscoring the importance of incorporating psychosocial assessments into radiation oncology workflows. Both parametric and machine learning models provided stable and closely aligned predictions of missed radiation treatment days. Key predictors identified as statistically significant in the regression model were also among the top contributors in the XGBoost model, reinforcing the robustness of these findings and supporting integration of both approaches into future intervention strategies.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"12 ","pages":"e92899"},"PeriodicalIF":2.6,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13679867/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225150","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":"Metastasis Extraction From Non-Small Cell Lung Cancer Clinical Notes Using Large Language Model-Based Classification: Retrospective Comparative Study.","authors":"Sweta Balaji, Kiersten Campbell, Rou-Zhen Chen, Daniel Smith, Matthew A Reyna, Abeed Sarker, Joshua D Wallach, Ravi B Parikh, Selen Bozkurt","doi":"10.2196/99573","DOIUrl":"https://doi.org/10.2196/99573","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Identification of metastasis status in non-small cell lung cancer (NSCLC) is a critical part of understanding disease prognosis, treatment courses, trial eligibility, and population-level cancer surveillance. However, metastasis status is inconsistently recorded in structured cancer registry fields, since manual abstraction of clinical notes is often an error-prone process. This challenge highlights an opportunity for leveraging large language models (LLMs) to conduct high-scale metastasis extraction from real-world clinical documentation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We conducted a retrospective, multicohort comparative evaluation of 3 distinct LLMs for 2 independent classification tasks: overall metastasis presence at any site and brain/central nervous system (CNS) metastasis presence. We evaluated model performance on 2 independent NSCLC cohorts: one with registry labels and another with manual annotations. We further explored whether our methods could analyze clinical documentation and recover missing information in structured registry fields.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Patient cohorts were derived from the Winship Cancer Institute. Cohort 1 (n=579 patients; 24,887 notes across 69 note types; 2023-2025) used registry-linked metastasis fields as the reference standard. Cohort 2 (n=22 patients; 644 radiology notes; 2010-2021) was drawn from 2 completed randomized trials and used dual-annotated labels (Cohen κ: 0.93 overall metastasis; 0.88 CNS metastasis) as the reference standard. We fine-tuned the GatorTron-base encoder model for each independent binary classification task. We evaluated MedGemma-27B-text and Llama 3.1-70B using zero-shot prompting. A separate cohort of 675 patients with missing registry labels was used for an exploratory missingness-recovery analysis, validated against annotations of a random subsample. Our best-performing model was used to conduct further site-specific metastasis classification, based on available registry data.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;More than half (675/1260, 54%) of initially identified cohort 1 patients had missing or unknown registry metastasis labels. MedGemma demonstrated the best performance in overall metastasis classification (cohort 1: F&lt;sub&gt;1&lt;/sub&gt;-score=0.80; cohort 2 patient-level: F&lt;sub&gt;1&lt;/sub&gt;-score=1.0; cohort 2 note-level: F&lt;sub&gt;1&lt;/sub&gt;-score=0.93). For brain/CNS metastasis, Llama3 performed best in both cohorts (cohort 1: F&lt;sub&gt;1&lt;/sub&gt;-score=0.79; cohort 2 patient-level: F&lt;sub&gt;1&lt;/sub&gt;-score=0.93; cohort 2 note-level: F&lt;sub&gt;1&lt;/sub&gt;-score=0.86). The fine-tuned GatorTron model showed strong performance for classification of overall metastasis in cohort 1 (F&lt;sub&gt;1&lt;/sub&gt;-score=0.72). Error analysis indicated that several model errors reflected incomplete registry labels, ambiguous clinical language, or missing documentation. In the exploratory recovery analysis, model predictions agreed with manual annotations at an accuracy of 0.90 and F&lt;sub&gt;1&lt;","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"12 ","pages":"e99573"},"PeriodicalIF":2.6,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149207926","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":"Cost-Effectiveness of AI-Powered Decentralized Production for Advanced Therapies in the Hospital-Generated Chimeric Antigen Receptor T-Cell Therapy for Multiple Myeloma From a German Hospital Perspective: Early Health Technology Assessment Study.","authors":"Iris W A Boot, Anna Louisa Weltin, Ysanne de Graaf, Hubertus J M Vrijhoef","doi":"10.2196/92253","DOIUrl":"https://doi.org/10.2196/92253","url":null,"abstract":"<p><strong>Background: </strong>Although effective, current chimeric antigen receptor T-cell (CAR-T) production methods-centralized, manual, and complex-are cost-intensive, time-consuming, and prone to variability. AIDPATH (AI-Powered Decentralized Production for Advanced Therapies in the Hospital) proposes a decentralized, automated alternative that integrates patient-specific data; optimizes resource use; and potentially improves cell viability, manufacturing efficiency, and patient outcomes.</p><p><strong>Objective: </strong>The aim of this study was to compare CAR-T therapy produced by the AIDPATH platform to both ciltacabtagene autoleucel (cilta-cel) and standard of care (SoC) for patients with triple-class refractory multiple myeloma (MM) over a 40-year time horizon from the hospital perspective in Germany.</p><p><strong>Methods: </strong>A partitioned survival model reflecting 3 health states (progression-free disease, progressed disease, and death) was used. The analysis used clinical trial data for cilta-cel, real-world data for SoC, and estimated parameters for AIDPATH due to the developmental status of the platform. The primary outcome was the incremental cost-effectiveness ratio; secondary outcomes included sensitivity and scenario analyses.</p><p><strong>Results: </strong>On the basis of the model used in this analysis, AIDPATH was dominant compared to both cilta-cel and SoC. Most costs for CAR-T therapies were driven by acquisition and adverse events. Sensitivity analyses showed that the results were most influenced by discount rates and assumptions about progression-free survival. Scenario analyses, including reduced adverse events and shorter vein-to-vein times for AIDPATH, further supported its cost-effectiveness.</p><p><strong>Conclusions: </strong>This is the first study to assess the cost-effectiveness of a CAR-T product generated with AI support in Germany from the hospital perspective. AIDPATH was found to be a cost-effective alternative to both cilta-cel and SoC, making it a promising option for future implementation. Although further data are needed, this study provides valuable guidance for health care stakeholders, reimbursement discussions, and future research.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"12 ","pages":"e92253"},"PeriodicalIF":2.6,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184886","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":"Detection of Cervical Nodal Metastasis in Oral Squamous Cell Carcinoma Using Elastic Scattering Spectroscopy: Ex Vivo Diagnostic Modeling Study.","authors":"Waseem Jerjes, Zaid Hamdoon, Dara Rashed, Colin Hopper","doi":"10.2196/103960","DOIUrl":"https://doi.org/10.2196/103960","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Cervical nodal metastasis remains one of the strongest determinants of treatment planning and prognosis in oral squamous cell carcinoma. Histopathology remains the reference standard, but it does not provide immediate information at the point of surgical specimen assessment. Elastic scattering spectroscopy (ESS) offers rapid, label-free optical interrogation of tissue microstructure, and may support ex vivo nodal assessment.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to evaluate the diagnostic accuracy of ESS combined with machine learning classification for ex vivo detection of cervical lymph node metastasis in patients with oral squamous cell carcinoma.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a single-center observational ex vivo diagnostic modeling study in patients with oral squamous cell carcinoma undergoing primary tumor resection and neck dissection. A total of 3 ESS spectra were acquired from each bivalved lymph node before routine histopathological processing. Internal validation used repeated nested 5-fold cross-validation grouped by patient, so every spectrum and lymph node from a patient remained in the same fold. All preprocessing, scaling, dimensionality reduction, within-model-family configuration, and hyperparameter tuning, probability calibration where required, and decision-threshold selection were restricted to training data within the nested validation process; comparisons between candidate model families were exploratory and were based on pooled patient-grouped out-of-fold predictions. Spectrum-level probabilities were aggregated within each lymph node, and histopathology was the reference standard.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 52 patients were assessed for eligibility, 45 were included, and 280 fresh lymph nodes were assessed by ESS; 96 were pathology positive, and 184 were pathology negative. Of 840 acquired spectra, 760 passed quality control. Of the 280 nodes, 274 retained at least 2 usable spectra and comprised the analytical cohort; 6 pathology-negative nodes yielded no analyzable node-level ESS result. The hybrid model was selected because it had the highest pooled patient-grouped out-of-fold node-level area under the receiver operating characteristic curve (AUROC), with an internally validated, exploratory estimate of 0.966 (95% CI 0.929-0.988). The same out-of-fold predictions informed model-family selection and performance reporting; this estimate does not represent independent validation of a prospectively fixed final model. At the cross-validation-derived summary threshold, secondary descriptive operating characteristics were sensitivity 93.8% (95% CI 86.4%-97.6%), specificity 95.5% (95% CI 90.1%-98.3%), accuracy 94.9% (95% CI 90.4%-97.3%), positive predictive value 91.8% (95% CI 82.9%-96.3%), and negative predictive value 96.6% (95% CI 92.4%-98.8%). The Brier score was 0.063, with calibration-in-the-large of -0.03 and a calibration slope of 0.94. Patient-","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"12 ","pages":"e103960"},"PeriodicalIF":2.6,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13661036/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184977","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":"Symptom Management Following Radioactive Iodine Treatment in Adults With Thyroid Cancer: Usability Evaluation and Proof-of-Concept Study.","authors":"Alaina Carr, Angela M Jenkins, Valeria Gómez, Janine Alexandria Lorenzo, Ryan Moclair, James Pocchia, Devansh Gandhi, Kristen Miller, Kristi D Graves, Jacqueline Jonklaas","doi":"10.2196/84151","DOIUrl":"10.2196/84151","url":null,"abstract":"<p><strong>Background: </strong>Thyroid cancer is often labeled a \"good cancer\" due to high survival rates. However, many patients diagnosed with differentiated thyroid cancer (DTC) treated with radioactive iodine (RAI) experience short- and long-term symptoms as a result of treatment. These include salivary dysfunction, dry mouth, dry eyes, and nausea. Despite these challenges, patients report limited knowledge and unmet informational needs regarding RAI symptoms and symptom management. Evidence-based, patient-centered education to support symptom management during DTC survivorship is needed. Our team developed Radioactive Iodine Support (RAI Support), a prototype of a 4-week web-based intervention for RAI symptom management among survivors of DTC.</p><p><strong>Objective: </strong>This study aimed to evaluate the usability of the RAI Support intervention among survivors of DTC who completed RAI treatment and assess preliminary effect sizes of change in RAI symptom burden, self-efficacy for managing chronic symptoms, informational needs, global physical and mental health, and health-related quality of life (HRQOL).</p><p><strong>Methods: </strong>We conducted a longitudinal usability testing study with 8 participants over a 4-week period. At baseline (T1), participants completed an online survey and a 90-minute virtual usability testing session. During usability testing sessions, study staff used a think-aloud protocol to elicit feedback on the content, language, and visual appeal of the prototype across 5 tasks. Participants also completed the System Usability Scale (SUS). Participants were encouraged to continue using the intervention over the 4-week period, during which use data were recorded. A follow-up after assessment (T2) was completed at the end of the 4-week period of home use. Survey outcomes included RAI symptom burden, self-efficacy for managing chronic symptoms, informational needs, global physical and mental health, and HRQOL.</p><p><strong>Results: </strong>Among the 8 survivors of DTC who had undergone RAI treatment (age: mean 45, SD 8.2 years), the mean SUS score was 88.1 (SD 8.7), indicating above-average usability. All participants effectively completed each task, with ease of use ranging from 100% to 87.5% and error rates ranging from 0% to 25%. Preliminary r effect size signals of change were found for improved self-efficacy, global mental health, and HRQOL (r values ranged from 0.31 to 0.45).</p><p><strong>Conclusions: </strong>Survivors of DTC reported above-average usability of the RAI Support prototype and completed each task successfully. Variability in error rates and ease of use suggests that refinements are needed to further optimize usability and patient engagement. RAI Support also shows preliminary promise in improving self-efficacy for symptom management, global mental health, and HRQOL among survivors of DTC. Findings support future pilot testing.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"12 ","pages":"e84151"},"PeriodicalIF":2.6,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149185094","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":"Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer Using Routinely Collected Clinical Data: Multicenter Retrospective Machine Learning Model Development and External Validation Study.","authors":"Gehui Xu, Xiaohe Sun, Jiaxin Jiang, Jin Sun, Liu Li, Haibo Cheng, Yuekun Zhu, Haiyi Liu","doi":"10.2196/101162","DOIUrl":"10.2196/101162","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Preoperative nodal assessment in colorectal cancer (CRC) remains imperfect, and uncertainty regarding pathological lymph node metastasis (LNM) may persist despite conventional imaging. Machine learning using routinely available preoperative information may provide complementary patient-level risk estimates without requiring radiomic feature extraction or molecular testing.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to develop and externally validate machine learning models for preoperative estimation of pathologically confirmed LNM risk in patients with newly diagnosed CRC undergoing direct radical surgery without preoperative anticancer treatment, and to assess whether multivariable prediction adds information beyond clinical tumor stage (cT) alone.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This multicenter retrospective study included 2725 patients from 2 hospitals. The internal cohort (n=1074) was split into training (n=753) and internal test (n=321) sets, while 1651 patients from a second center formed the external validation cohort. Candidate predictors were routinely available preoperative clinical, laboratory, tumor-marker, imaging-based, and endoscopic biopsy variables. Predictors were selected using univariable and multivariable logistic regression (LR). Seven algorithms were evaluated: random forest (RF), support vector machine (SVM), LR, decision tree (DT), extreme gradient boosting (XGBoost), naive Bayes (NB), and light gradient boosting machine (LightGBM). Performance was assessed using discrimination, threshold-dependent classification metrics, calibration, and decision curve analysis (DCA). Shapley additive explanations (SHAP) were used for interpretation. Sensitivity analyses compared the selected predictors with all candidate predictors and cT alone.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Six predictors were retained: BMI, preoperative carcinoembryonic antigen, primary tumor site, cT, histological type, and tumor differentiation. RF was selected based on internal test performance. The area under the receiver operating characteristic curve (AUROC) was 0.803 (95% CI 0.756-0.849) in the internal test set and 0.776 (95% CI 0.755-0.797) in external validation. In external validation, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.687, 0.677, 0.693, 0.569, and 0.781, respectively. The Brier score was 0.190, and the calibration slope was 1.061. cT contributed most in SHAP analysis; however, the 6-predictor RF model showed significantly higher discrimination than cT alone in both the internal test set (AUROC 0.803 vs 0.712; ΔAUROC=0.091; &lt;i&gt;P&lt;/i&gt;&lt;.001) and external validation cohort (AUROC 0.776 vs 0.707; ΔAUROC=0.069; &lt;i&gt;P&lt;/i&gt;&lt;.001). Discrimination did not differ significantly between the 6-predictor and all-predictor RF models.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;We developed and externally validated a 6-predictor RF model for preoperative LNM risk ","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"12 ","pages":"e101162"},"PeriodicalIF":2.6,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13644950/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149128787","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":"Experiences of Informal Caregivers Facing Cancer While Caring for Someone With Dementia: Qualitative Interview Study.","authors":"Elizabeth Travis, Mollie Louise Price, Millie Slessor, Jacqueline Crewe, Laura Ashley","doi":"10.2196/93688","DOIUrl":"https://doi.org/10.2196/93688","url":null,"abstract":"<p><strong>Background: </strong>Informal caregivers play a fundamental role in the diagnosis, treatment, and care received by their family members. Caring for someone with dementia is particularly burdensome and can have negative effects on caregivers' health. Despite caregivers being widely recognized as vulnerable, the psychosocial needs of caregivers who are managing cancer themselves remain unknown.</p><p><strong>Objective: </strong>This study aims to investigate the experiences of UK family caregivers who have received a cancer diagnosis while caring for someone with dementia or memory problems.</p><p><strong>Methods: </strong>Twenty-five UK informal family caregivers who had received a cancer diagnosis were recruited through convenience sampling to take part in in-depth semistructured interviews. The interviews explored participants' experiences of managing cancer alongside dementia caregiving, including the impact on treatment and caregiving, emotional challenges, support needs, and experiences of formal and informal support. Ethical approval was obtained from the Leeds Beckett University Research Ethics Committee, and all participants provided informed consent. Reflexive thematic analysis was conducted.</p><p><strong>Results: </strong>Three key themes were identified: (1) caring responsibilities can complicate and take priority over cancer care, (2) caregiving can intensify cancer-related distress and anxieties, and (3) managing cancer while caring increases the need for support and coping resources. For dementia caregivers, having cancer can surface/heighten feelings of predeath grief and worries about future care planning for their care recipient. Dementia caregivers viewed multifaceted social support as vital for enabling access to and coping with cancer treatment.</p><p><strong>Conclusions: </strong>Dementia caregivers who are also living with cancer continue to shoulder the burden of illness work on behalf of their care recipients while simultaneously managing their own cancer diagnosis and treatment. Caregiving responsibilities often take priority over cancer care, intensify the practical and emotional challenges of cancer, and increase the need for and reliance on support resources. These findings highlight the need for person-centered adjustments to cancer care, dementia-friendly health care environments, and tailored psycho-oncology support that address the practical and emotional needs of dementia caregivers.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"12 ","pages":"e93688"},"PeriodicalIF":2.6,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13637987/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149116255","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 Impacts of Social Marketing Campaigns on Breast Cancer Awareness and Screening: Scoping Review.","authors":"Sahar Abduljawad","doi":"10.2196/88568","DOIUrl":"10.2196/88568","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Promotional activities, such as social marketing approaches, are important for raising cancer awareness and increasing breast cancer screening participation. Such interventions are designed to facilitate change and maintain positive health behaviors at both the community and society level. With the growing use of these promotional activities in public health, it is pertinent to map available studies to inform future evaluations and the development of effective interventions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to review existing studies on how social marketing interventions have been used to promote breast cancer screening and early diagnosis. The specific objectives were to assess the characteristics of social marketing interventions aimed at promoting breast cancer screening, identify the methodological approaches used in evaluating the interventions, identify the outcomes used to measure the impact of social marketing interventions for breast cancer screening, and assess the impacts of social marketing interventions when promoting breast cancer screening.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A comprehensive literature search was performed in 6 electronic databases to identify qualitative and quantitative evaluations of social marketing interventions targeting breast cancer screening and early diagnosis. Titles and abstracts were reviewed independently by two reviewers, followed by data extraction and verification. The methodological quality was evaluated using the Cochrane Handbook for Systematic Reviews of Interventions. Outcomes were categorized using an adapted version of key performance indicators and metrics related to social media use in health promotion.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Overall, 15 research articles were eligible and analyzed. The 4 types of social marketing interventions identified were health professional and educational strategies, social media or mass media campaigns, community outreach campaigns, and video-based marketing. The predominant intervention source was public health government bodies, followed by regional health services, cancer charities, and educational institutions. Most interventions were designed to influence breast cancer screening behaviors by raising awareness through advertisements, educational interventions, and fundraising activities. Most interventions reached a large audience (&gt;500 people), but participants had mixed awareness and perceptions toward social marketing strategies, and the outcomes were shaped by demographic factors such as cultural backgrounds, age, education, marital status, and occupation. Knowledge gaps, cost concerns, and the lack of physician recommendations also constituted key barriers to screening uptake. Behavior change outcomes were sparingly assessed in most interventions, thereby contributing to limited understanding of mechanisms affecting screening uptake.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Available evidence suggests that ","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"12 ","pages":"e88568"},"PeriodicalIF":2.6,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13637986/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149116303","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":"Explainable Machine Learning-Based Prediction of Progression-Free Survival in Prostate Cancer: Retrospective Cohort Study.","authors":"Hein Minn Tun, Lin Naing, Owais Ahmed Malik, Muhammad Syafiq Abdullah, Thu Ta, Hanif Abdul Rahman","doi":"10.2196/91510","DOIUrl":"10.2196/91510","url":null,"abstract":"<p><strong>Background: </strong>Progression-free survival (PFS) is a critical end point in oncology, yet real-world applications of individualized, explainable machine learning (ML) predictions remain limited.</p><p><strong>Objective: </strong>This study aimed to develop and validate explainable ML models to predict PFS using retrospective data from a national prostate cancer cohort in Brunei Darussalam.</p><p><strong>Methods: </strong>We analyzed a retrospective cohort of 212 patients (478 longitudinal observations) treated at the Brunei Cancer Centre (January 2018 to December 2024). Clinical, laboratory, and treatment data were harmonized, with missing values imputed via extremely randomized trees. Longitudinal patterns were captured using a recurrent autoencoder to generate latent representations. We compared 4 modeling approaches: Cox proportional hazards, random survival forest (RSF), gradient boosting survival (GBS), and deep neural network survival models. Performance was evaluated using time-dependent area under the receiver operating characteristic curve (AUC), Harrell C-index, and integrated Brier score (IBS), with Shapley additive explanations (SHAP) used for interpretability.</p><p><strong>Results: </strong>RSF demonstrated improved discriminative performance and balanced calibration, achieving a C-index of 0.906 and AUCs of 0.941 and 0.917 at 4 and 5 years (IBS=0.0698). In contrast, the traditional CPH model performed poorly (C-index 0.531 and AUC 0.706 at 4 years and 0.833 at 5 years). Deep survival (AUCs of 0.941 at 4 years and 0.917 at 5 years, C-index 0.719, and IBS=0.0887) and GBS (AUCs of 0.765 at 4 years and 0.833 at 5 years, C-index 0.844, and IBS=0.0590) models showed moderate performance. SHAP analysis identified sodium, alanine aminotransferase, mean corpuscular hemoglobin, platelet count, and specific treatment categories as key drivers of increased progression risk.</p><p><strong>Conclusions: </strong>Tree-based ensemble approaches, particularly RSF integrated with SHAP, offer high accuracy for personalized risk stratification in prostate cancer. These findings highlight the potential of explainable ML to enhance clinical decision-making. However, external validation in a larger multi-institutional, multiomics dataset is required before routine clinical implementation.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"12 ","pages":"e91510"},"PeriodicalIF":2.6,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13680908/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149116239","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}