JMIR Cancer最新文献

{"title":"Evaluating the Feasibility of Web-Monitoring Methodology for Measuring Exposure to Online Cancer Misinformation.","authors":"Cindy A Turner, Andy J King, Ida Tovar, Morgan M Millar, Rachel R Codden, Jia-Wen Guo, Skyler Johnson, Anne C Kirchhoff, Margaret Raber, Xiaoming Sheng, Deanna Kepka, Echo L Warner","doi":"10.2196/65887","DOIUrl":"10.2196/65887","url":null,"abstract":"<p><strong>Unlabelled: </strong>Understanding the impact of online cancer misinformation exposure on health outcomes is an area of growing concern, but few methods exist to objectively measure this exposure. The primary aim of this paper is to describe the lessons learned in using web-monitoring software to measure exposure to online cancer misinformation among patients with cancer. These lessons learned emerged from our experience conducting a prospective pilot study from October 2022 to August 2023 wherein we adopted commercially available web-monitoring software to capture cancer-related web content. A total of 56 patients with cancer completed a baseline survey, and 17 of these participants installed web-monitoring software on their personal computer for 30 days and completed a follow-up survey. We use implementation outcomes to describe the feasibility of this methodological approach using lessons learned in 3 topic areas, namely data quality, software implementation, and participant acceptability. We found the web-monitoring data to be appropriate for our research aim to objectively measure cancer misinformation exposure, although compatibility issues with social media websites and mobile devices negatively impacted data quality. A complex installation process negatively impacted implementation and caused an unknown number of participants to drop out after the baseline survey. Among participants who completed the study, reported acceptability of web-monitoring software for research purposes was high, though potentially biased by selective retention. This pilot study testing web-monitoring software for research purposes among patients with cancer demonstrates high acceptability but low feasibility due to implementation barriers. We propose practical solutions to address these barriers and believe the lessons learned here offer a promising foundation for improving methods to objectively measure patient exposure to online cancer information. Future studies should focus on exploring perceptions of web-monitoring among nonparticipants, considering alternative approaches, and expanding web-monitoring to include mobile devices.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e65887"},"PeriodicalIF":2.7,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12306912/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144745419","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":"Public Involvement in Cancer Research: Collaborative Evaluation Using Photovoice.","authors":"Piotr Teodorowski, Melanie McInnes, Glen Dale, Linda Galbraith, Esme Radin, Karen Gold, Erica Gadsby","doi":"10.2196/75741","DOIUrl":"10.2196/75741","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;A public involvement group consisting of 4 public contributors with lived experience of cancer diagnosis contributed to 2 cancer research projects that focused on optimizing the diagnostic pathways for patients with suspected cancer. The public contributors have been involved from the start of the projects and were involved in aspects of the design, analysis, and dissemination alongside research and clinical teams. Despite public involvement in cancer research being seen as a key element of the research process, there is still a limited understanding of what works well and how to do it in a meaningful way for both researchers and public contributors.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to evaluate the public involvement process in 2 cancer research projects.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This was a collaborative evaluation with the research team and public contributors jointly evaluating the process. Data were collected throughout the lifespan of the project by public contributors through photovoice, where they collected photos that represented their experiences of involvement. At the end of the evaluation meeting, 2 separate analyses were conducted. First, public contributors reflected on their experiences using a 4-dimensional framework to capture how strong their voice was, how many ways they had an opportunity to be involved, if their feedback was implemented, and if the discussion focused on their priorities. Second, they analyzed the collected photos by organizing them alongside their narratives, explaining their meanings and comparing how they experienced the involvement process.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Narratives from 8 photos illustrate public contributors' experience of involvement in these projects, presenting them in chronological order, showing how their perspectives evolved from not knowing what form the project would take, through understanding foundations and building confidence through being satisfied with the successful projects. Results from the 4-dimensional framework showed that public contributors felt that their voices were strong, and the research and clinical team mostly implemented suggested changes. The discussion focused on topics and issues that were relevant to public contributors. However, how public contributors were involved depended mainly on the research team's decision, and they would have preferred more opportunities.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study has shown that public contributors can be meaningfully involved throughout the lifespan of cancer research projects. The evaluation demonstrated that establishing a strong relationship and trust between researchers and public contributors helps to ensure that the public contributors' voice is meaningful and makes a difference in the projects. However, it also identified improvements for future public involvement. Researchers should involve public contributors as early as the funding application ","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e75741"},"PeriodicalIF":2.7,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12303542/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144733813","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":"Design and Validation of a Chatbot-Based Cervical Cancer Screening Decision Aid for Women Experiencing Socioeconomic Disadvantage: User-Centered Approach Study.","authors":"Alice Le Bonniec, Catherine Sauvaget, Eric Lucas, Abdelhak Nassiri, Farida Selmouni","doi":"10.2196/70251","DOIUrl":"10.2196/70251","url":null,"abstract":"<p><strong>Background: </strong>Cervical cancer (CC) screening participation remains suboptimal among vulnerable populations in France. This study aimed to develop and evaluate AppDate-You, a chatbot-based decision aid, to support women from socioeconomically disadvantaged areas in the French Occitanie region to make informed decisions about CC screening, particularly human papillomavirus self-sampling (HPVss).</p><p><strong>Objective: </strong>This study aimed to explore the needs, preferences, and barriers related to CC screening and to design and validate a user-centered, empathetic, and effective chatbot-based decision aid to empower women experiencing socioeconomic challenges in France to make informed choices about HPVss.</p><p><strong>Methods: </strong>The chatbot was developed following a validated framework for developing decision aids. The process included qualitative research involving online and in-person interviews and focus groups with women and health care professionals, followed by alpha testing with both groups and beta testing with women only. Participants included women (both French and non-French speaking) aged between 30 and 65 years from socioeconomically disadvantaged areas of the Occitanie region and health care professionals (general practitioners, gynecologists, and midwives) working with these populations. AppDate-You was made accessible through WhatsApp and Facebook Messenger, offering text-based and voice-based interactions and multimedia content.</p><p><strong>Results: </strong>The exploratory phase identified key barriers to screening and digital tool preferences. Prototype testing revealed great satisfaction with the chatbot's performance, educational value, and content quality. Contrary to the expectations of health care professionals, women from diverse backgrounds, including women who were older and socioeconomically disadvantaged, were willing and able to use the tool. Users-even those with limited digital literacy-found AppDate-You innovative, user-friendly, and informative. In the beta testing phase, 80% (12/15) of the participants expressed interest in HPVss. Some limitations were identified, such as the chatbot's occasional repetitive responses and the need for clearer medical terminology.</p><p><strong>Conclusions: </strong>This study demonstrates the potential for artificial intelligence chatbots to improve access to health education and increase cervical screening intention among underserved populations. The user-centered approach resulted in a tool that effectively meets the needs of the target population.</p><p><strong>International registered report identifier (irrid): </strong>RR2-10.2196/39288.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e70251"},"PeriodicalIF":2.7,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12332448/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700021","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":"Prevalence of Frailty and Its Predictors Among Patients With Cancer at the Chemotherapy Stage: Systematic Review.","authors":"Tingting Wang, Jinxia Jiang, Zihe Song, Xianliang Liu, Minhui Zhong, Chan Yu, Runa Zhang, Xia Duan","doi":"10.2196/69936","DOIUrl":"10.2196/69936","url":null,"abstract":"<p><strong>Background: </strong>Chemotherapy causes physiological, psychological, and social impairments in patients with cancer. Frailty reduces the effectiveness of chemotherapy and increases the toxicity associated with radiotherapy and chemotherapy, the possibility of chemotherapy failure, and adverse outcomes. However, factors affecting chemotherapy-related frailty in patients with cancer remain unclarified.</p><p><strong>Objective: </strong>This systematic review aimed to identify risk factors driving frailty progression during chemotherapy in patients with cancer.</p><p><strong>Methods: </strong>A comprehensive systematic search was conducted on PubMed, Web of Science, Embase, China National Knowledge Infrastructure, China Science and Technology Journal Database (VIP), and SinoMed for observational studies (cohort, cross-sectional, or case-control) on factors affecting the debility-of-chemotherapy stage in patients with cancers between the inception of the database and February 2025, with an updated search executed in May 2025. Literature screening, quality evaluation using the Newcastle-Ottawa Scale and Agency for Healthcare Research and Quality checklist, and data extraction were conducted independently by 2 authors. Meta-analysis, effect size combination, sensitivity analysis, and publication bias analysis were performed using RevMan (version 5.4; The Cochrane Collaboration) and R (version 4.4.3; R Foundation).</p><p><strong>Results: </strong>The analysis comprised 14 studies (8 cross-sectional, 2 repeated cross-sectional, 3 cohort, and 1 mixed-design), including 3879 patients with cancer and 23 influencing factors. Methodological quality assessment using Agency for Healthcare Research and Quality (mean 8.8, SD 1.3, 95% CI 7.9-9.7; SE 0.4) and Newcastle-Ottawa Scale (mean 8.0, SD 1.0, 95% CI 6.7-9.3; SE 0.6) revealed 73% (8/11) of cross-sectional studies as high-quality. The meta-analysis showed a 35% (95% CI 22%-50%) prevalence of frailty during chemotherapy in these patients. Cancer stage (odds ratio 1.99, 95% CI 1.64-2.42), chemotherapy frequency (odds ratio 2.60, 95% CI 1.83-3.70), transfer (odds ratio 2.18, 95% CI 1.50-3.17), hemoglobin (odds ratio 0.29, 95% CI 0.18-0.47), white blood cell (odds ratio 0.37, 95% CI 0.21-0.65), comorbidity (odds ratio 1.93, 95% CI 1.30-2.86), and hypoproteinemia (odds ratio 1.74, 95% CI 1.31-2.30) were risk factors for frailty in patients at the chemotherapy stage.</p><p><strong>Conclusions: </strong>Frailty during chemotherapy was strongly associated with advanced cancer stage, frequent treatment cycles, metastasis, anemia, leukopenia, comorbidities, and hypoproteinemia. Clinically actionable findings emphasized hemoglobin and albumin monitoring as preventive targets, while heterogeneity in assessment tools and population bias limited generalizability. The integration of frailty screening into chemotherapy workflows is urgent to mitigate treatment-related functional decline.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e69936"},"PeriodicalIF":3.3,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12289293/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144709368","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 Portals for Individuals Living With or Beyond Cancer: Patient-Driven Scoping Review.","authors":"Steven Ouellet, Florian Naye, Wilfried Supper, Chloé Cachinho, Marie-Pierre Gagnon, Annie LeBlanc, Marie-Claude Laferrière, Simon Décary, Maxime Sasseville","doi":"10.2196/72862","DOIUrl":"10.2196/72862","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Digital health portals are online platforms allowing individuals to access their personal information and communicate with health care providers. While digital health portals have been associated with improved health outcomes and more streamlined health care processes, their impact on individuals living with or beyond cancer remains underexplored.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This scoping review aimed to (1) identify the portal functionalities reported in studies involving individuals living with or beyond cancer, as well as the outcomes assessed, and (2) explore the diversity of participant characteristics and potential factors associated with portal use.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a scoping review in accordance with the JBI methodology (formerly the Joanna Briggs Institute) and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. We included primary research studies published between 2014 and 2024 that involved participants living with or beyond cancer, had access to personal health information, and assessed at least one outcome related to health or the health care system. We searched the Embase, Web of Science, MEDLINE (Ovid), and CINAHL Plus with Full Text databases. Five reviewers independently screened all titles, abstracts, and full texts in duplicate using Covidence. We extracted data on study design, participant characteristics, portal functionalities, outcomes assessed, and PROGRESS-Plus (place of residence; race, ethnicity, culture, or language; occupation; gender or sex; religion; education; socioeconomic status; and social capital-Plus) equity factors.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We included 44 studies; most were conducted in the United States (n=30, 68%) and used quantitative (n=23, 52%), mixed methods (n=11, 25%), or qualitative (n=10, 23%) designs. The most common portal features were access to test results (28/44, 64%) and secure messaging (30/44, 68%). Frequently reported services included appointment-related functions (19/44, 43%), educational resources (13/44, 30%), and prescription management features (11/44, 25%). Behavioral and technology-related outcomes were the most frequently assessed (37/44, 84%), followed by system-level (19/44, 43%), psychosocial (16/44, 36%), and clinical outcomes (5/44, 11%). Overall, 43% (19/44) of the studies addressed PROGRESS-Plus factors. Age was the most frequently reported (13/19, 68%), followed by socioeconomic status (10/19, 53%), race or ethnicity (7/19, 37%), and gender or sex (7/19, 37%). Social capital (2/19, 11%), occupation (1/19, 5%), and disability (1/19, 5%) were rarely considered, and religion was not reported in any study.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;While digital health portals enhance patient engagement, their clinical impact and equity implications remain insufficiently evaluated. We found disparities in functionalities, outcomes, and PRO","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e72862"},"PeriodicalIF":2.7,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12317290/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144664022","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":"Predicting Pathological Complete Response Following Neoadjuvant Therapy in Patients With Breast Cancer: Development of Machine Learning-Based Prediction Models in a Retrospective Study.","authors":"Chun-Chi Lai, Cheng-Yu Chen, Tzu-Hao Chang","doi":"10.2196/64685","DOIUrl":"10.2196/64685","url":null,"abstract":"<p><strong>Background: </strong>Breast cancer is the most prevalent form of cancer worldwide, with 2.3 million new diagnoses in 2022. Recent advancements in treatment have led to a shift in the use of chemotherapy-targeted immunotherapy from a postoperative adjuvant to a preoperative neoadjuvant approach in select cases, resulting in enhanced survival outcomes. A pathological complete response (pCR) is a critical prognostic marker, with higher pCR rates linked to improved overall and disease-free survival.</p><p><strong>Objective: </strong>The objective of this study was to develop robust, machine learning-based prediction models for pCR following neoadjuvant therapy, leveraging clinical, laboratory, and imaging data.</p><p><strong>Methods: </strong>A retrospective cohort study was conducted using data from the Taipei Medical University Clinical Research Database from 2015 to 2022. Eligible patients were those with breast cancer who received neoadjuvant therapy followed by curative surgical resection. Machine learning models were developed using 3 distinct sets of variables. Model 1 included 14 clinical features such as age, height, weight, tumor stage, receptor status, tumor markers, and intrinsic subtype. Model 2 expanded on this by incorporating additional laboratory data and comorbidities (29 variables in total). Model 3 added breast sonography response data to the clinical variables in model 1. Algorithms including logistic regression, random forest, support vector machines, and extreme gradient boosting were used. Feature selection was performed using recursive feature elimination with cross-validation, and model performance was assessed using accuracy and area under the receiver operating characteristic curve (AUROC).</p><p><strong>Results: </strong>A total of 334 patients were analyzed, with 199 in the non-pCR group and 135 in the pCR group. The application of logistic regression with recursive feature elimination with cross-validation was found to demonstrate the optimal performance among the various algorithms that were evaluated in this study. Model 1 attained a mean accuracy of 0.66 (SD 0.02) and a mean AUROC of 0.73 (SD 0.01). The incorporation of laboratory data and comorbidities in model 2 did not yield significant enhancement, with a mean accuracy of 0.67 (SD 0.02) and a mean AUROC of 0.73 (SD 0.01). The incorporation of breast sonography response in model 3 led to a modest enhancement in predictive performance for the sonography group (accuracy 0.68; AUROC 0.60) in comparison to the nonsonography group (accuracy 0.66; AUROC 0.55). Despite the modest sample size (41 patients) of model 3, the integration of sonography data appeared to offer additional value in predicting pCR and warrants further investigation.</p><p><strong>Conclusions: </strong>This study suggests that incorporating breast sonography into models with clinical and laboratory data may modestly improve pCR prediction. It is important to note that the findings of this","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e64685"},"PeriodicalIF":2.7,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12319255/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144664023","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 Comorbidity Clusters and Mortality in Patients With Cancer: Predictive Modeling Using Machine Learning Approaches of Data From the United States and Hong Kong.","authors":"Chun Sing Lam, Rong Hua, Herbert Ho-Fung Loong, Chun-Kit Ngan, Yin Ting Cheung","doi":"10.2196/71937","DOIUrl":"10.2196/71937","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Patients with cancer and cancer survivors often experience multiple chronic health conditions, which can impact symptom burden and treatment outcomes. Despite the high prevalence of multimorbidity, research on cancer prognosis has predominantly focused on cancers in isolation. There is growing interest in machine learning techniques for cancer studies. However, these methods have not been applied in the context of supportive care for patients with cancer who have multimorbidity. Furthermore, few studies have investigated the associations between comorbidity clusters and mortality outcomes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study investigated comorbidity clusters among patients with cancer using machine learning and examined their associations with mortality outcomes in two large representative samples from the United States and Hong Kong.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This study used data from the National Health and Nutrition Examination Survey (NHANES) and the Hospital Authority Data Collaboration Laboratory (HADCL). Participants aged ≥20 years with a history of cancer were included. The study used a two-step framework to identify clusters of comorbidities in NHANES. In the first step, we used four machine learning techniques, including the Bernoulli mixture model and partition-based methods, to cluster the comorbidities. In the second step, domain experts reviewed and ranked the identified clusters to ensure clinical relevance. The clusters that had the highest average rank were selected for further analysis. The associations between comorbidity clusters and mortality outcomes were analyzed using Cox proportional hazards models. We conducted an external validation to evaluate the generalizability of the clusters identified in the NHANES cohort and their associations with mortality using HADCL. The same number of clusters was replicated based on the distinctive patterns and distribution of comorbidities observed within each cluster.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The study included 4390 participants in NHANES and 12,484 participants in HADCL. Four comorbidity clusters were identified: low comorbidity, metabolic, cardiovascular disease (CVD), and respiratory. In NHANES, participants in the respiratory cluster had the highest risk of all-cause mortality (adjusted hazard ratio [aHR] 1.62, 95% CI 1.26-2.08; P&lt;.001), followed by the CVD cluster (aHR 1.50, 95% CI 1.26-1.80; P&lt;.001) compared to the low comorbidity cluster. The 3 clusters were associated with higher risks of CVD-related mortality (aHR 1.48-3.05, 95% CI 1.14-4.07; P&lt;.003). The effects of comorbidity clusters on mortality were modified by income-to-poverty ratio (P for interaction=.04), diet quality (P for interaction=.02), and cancer prognosis (P for interaction=.005). In the HADCL (validation) cohort, participants in the respiratory and CVD clusters had a higher risk of all-cause mortality.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;High comorbidity bur","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e71937"},"PeriodicalIF":3.3,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12286590/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144650893","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 Impact of Digital Inequities on Nasal and Paranasal-Sinus Cancer Disparities in the United States: A Cohort Study.","authors":"David J Fei-Zhang, Amelia Sherron Lawrence, Daniel C Chelius, Anthony M Sheyn, Jeffrey C Rastatter","doi":"10.2196/52627","DOIUrl":"10.2196/52627","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;In the modern era, the use of technology can substantially impact care access. Despite the extent of its influence on several chronic medical conditions related to the heart, lungs, and others, the relationship between one's access to digital resources and oncologic conditions has been seldom investigated in select pathologies among gastrointestinal and head-neck regions. However, studies on the influence of this \"digital inequity\" on other cancers pertaining to nasal and paranasal sinus cancer (NPSC) have yet to be performed. This remains in stark contrast to the extent of large data approaches assessing the impact of traditional social determinants/drivers of health (SDoH), such as factors related to one's socioeconomic status, minoritized race or ethnicity, and housing-transportation status, on prognostic and treatment outcomes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to use the Digital Inequity Index (DII), a novel, comprehensive tool that quantifies digital resource access on an area- or community-based level, to assess the relationship between inequities in digital accessibility with NPSC disparities in prognosis and care in the United States.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Patients with NPSC from 2008 to 2017 in the Surveillance, Epidemiology, and End Results Program were assessed for significant regression trends in the long-term follow-up period and treatment receipt across NPSCs with increasing overall digital inequity, as measured by DII. DII was based on 17 census-tract level variables derived from the summarized values overlapping that same time period from the US Census/American Community Survey and Federal Communications Commission Annual Broadband Report. Variables were categorized as infrastructure-access (ie, electronic device ownership, internet provider availability, and income-broadband subscription ratio) or sociodemographic (education, income, age, and disability), ranked, and then averaged into a composite score to encompass direct and indirect factors related to digital inequity.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Across 8012 adult patients with NPSC, males (n=5416, 67.6%) and White race (n=4293, 53.6%) were the most represented demographics. With increasing digital inequity, as measured by increasing total DII scores, significant decreases in the length of long-term follow-up were observed with nasopharyngeal (P&lt;.01) and maxillary sinus cancers (P=.02), with decreases as high as 19% (35.2 to 28.5 months, nasopharynx). Electronic device and service availability inequities showcased higher-magnitude contributions to observed associated regression trends, while the income-broadband ratio contributed less. Significantly decreased odds of receiving indicated surgery (lowest odds ratio 0.87, 95% CI 0.80-0.95, maxillary) and radiation (lowest odds ratio 0.78, 95% CI 0.63-0.95, ethmoid) for several NPSCs were also observed.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Digital inequities are associa","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e52627"},"PeriodicalIF":3.3,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12282939/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144643807","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":"Prediction of a Panel of Programmed Cell Death Protein-1 (PD-1) Inhibitor-Sensitive Biomarkers Using Multiphase Computed Tomography Imaging Textural Features: Retrospective Cohort Analysis.","authors":"Shiqi Wang, Na Chai, Jingji Xu, Pengfei Yu, Luguang Huang, Quan Wang, Zhifeng Zhao, Bin Yang, Jiangpeng Wei, Xiangjie Wang, Gang Ji, Minwen Zheng","doi":"10.2196/67379","DOIUrl":"10.2196/67379","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Immune checkpoint inhibitors represent an effective therapeutic approach for advanced gastric cancer. Their efficacy largely depends on the status of tumor biomarkers including human epidermal growth factor receptor 2 (HER2), programmed death-ligand 1 (PD-L1; combined positive score ≥1), and microsatellite instability-high (MSI-H). To noninvasively evaluate these biomarkers, researchers have developed radiomic models for individual biomarker prediction. However, in clinical practice, holistic prediction of these biomarkers as an integrated system is more efficient. Currently, the feasibility of implementing radiomics-based comprehensive biomarker prediction remains unclear, requiring further investigation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to develop a radiomics-based predictive model using multiphase computed tomography (CT) images to holistically evaluate HER2, PD-L1, and MSI-H status in patients with gastric cancer.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A retrospective analysis was conducted on 461 patients with gastric cancer who underwent radical gastrectomy between 2019 and 2022. Clinical data, contrast-enhanced CT images (arterial phase [AP] and portal venous phase [PP]), and pathological results were collected. Patients were categorized into two groups: (1) the programmed cell death protein-1 inhibitor panel-positive group, comprising patients with HER2 overexpression, PD-L1 positive, or MSI-H status; and (2) the negative group, comprising patients without HER2 amplification, PD-L1 negative, or microsatellite instability-low or microsatellite stable condition. Radiomic features (including first-order statistics, shape features, and wavelet-derived textures) were extracted from both AP and PP images, yielding 1834 features per phase. Least absolute shrinkage and selection operator regression was applied to select key features. In total, 3 models were constructed using the Extreme Gradient Boosting algorithm: AP-only (8 features), PP-only (22 features), and a fused model combining AP and PP features (20 features: 6 AP and 14 PP features). Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, and decision curve analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Of the 461 patients, 147 patients (31.9%) were classified into the panel-positive group. The clinical features were similar between the 2 groups. The fused model demonstrated superior performance in the test set (AUC 0.82, 95% CI 0.68-0.95), significantly outperforming AP-only (AUC 0.61, 95% CI 0.47-0.74) and PP-only models (AUC 0.70, 95% CI 0.49-0.91). Sensitivity and specificity for the AP-only, PP-only, and the fused model were 0.33 and 0.85; 0.50 and 0.86; and 0.60 and 0.83, respectively. Decision curve analysis confirmed that the fused model provided higher clinical net benefit across threshold probabilities.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The construction of integrated biomarker prediction models throug","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e67379"},"PeriodicalIF":2.7,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12274051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700022","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":"Size-Specific Predictors for Malignancy Risk in Follicular Thyroid Neoplasms: Machine Learning Analysis.","authors":"Xin Li, Wen-Yu Yang, Fan Zhang, Rui Shan, Fang Mei, Shi-Bing Song, Bang-Kai Sun, Jing Chen, Run-Ze Hu, Yang Yang, Yi-Hang Yang, Jing-Yao Liu, Chun-Hui Yuan, Zheng Liu","doi":"10.2196/73069","DOIUrl":"10.2196/73069","url":null,"abstract":"<p><strong>Background: </strong>Surgeons often face challenges in distinguishing between benign and malignant follicular thyroid neoplasms (FTNs), particularly small tumors, until diagnostic surgery is performed.</p><p><strong>Objective: </strong>This study aimed to identify the size-specific predictors for the malignancy risk of FTNs preoperatively.</p><p><strong>Methods: </strong>A retrospective cohort study was conducted at Peking University Third Hospital in Beijing, China, from 2012 to 2023. Patients with a postoperative pathological diagnosis of follicular thyroid adenoma (FTA) or follicular thyroid carcinoma (FTC) were included. FTNs were classified into small- and large-sized categories based on the cutoff value of the tumor diameter derived from spline regression, which indicated the turning point of malignancy risk. We identified the 5 most important predictors from 22 variables including demography, sonography, and hormones, using machine learning methods. We also calculated the odds ratios (OR) with 95% CI for these predictors in both small- and large-sized FTNs.</p><p><strong>Results: </strong>Altogether, we included 1494 FTNs, comprising 1266 FTAs and 228 FTCs. FTNs with a maximum diameter less than 3.0 cm were grouped as small-sized tumors (n=715), while those with larger diameters were categorized as large-sized tumors (n=779). In the small-sized group, tumors with macrocalcification (OR 2.90, 95% CI 1.50-5.60), those with peripheral calcification (OR 4.50, 95% CI 1.50-13.00), and those in younger patients (OR 1.33, 95% CI 1.05-1.69) showed a higher malignancy risk. In the large-sized group, tumors presenting with a nodule-in-nodule appearance (OR 3.30, 95% CI 1.30-7.90) exhibited a higher malignancy risk. In both groups, lower thyroid-stimulating hormone levels (OR 1.49, 95% CI 1.20-1.85 for small-sized FTNs; OR 1.61, 95% CI 1.37-1.96 for large-sized FTNs) and a larger mean diameter (OR 1.40, 95% CI 1.10-1.70 for small-sized FTNs; OR 1.50 95% CI 1.20-1.70 for large-sized FTNs) were associated with the malignancy risk of FTNs.</p><p><strong>Conclusions: </strong>This study identified size-specific predictors for malignancy risk in FTNs, highlighting the importance of stratified prediction based on tumor size.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e73069"},"PeriodicalIF":3.3,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12274017/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144612336","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}