JMIR Cancer最新文献

{"title":"Understanding Cancer Survivorship Care Needs Using Amazon Reviews: Content Analysis, Algorithm Development, and Validation Study.","authors":"Liwei Wang, Qiuhao Lu, Rui Li, Taylor B Harrison, Heling Jia, Ming Huang, Heidi Dowst, Rui Zhang, Hoda Badr, Jungwei W Fan, Hongfang Liu","doi":"10.2196/71102","DOIUrl":"10.2196/71102","url":null,"abstract":"<p><strong>Background: </strong>Complementary therapies are being increasingly used by cancer survivors. As a channel for customers to share their feelings, outcomes, and perceived knowledge about the products purchased from e-commerce platforms, Amazon consumer reviews are a valuable real-world data source for understanding cancer survivorship care needs.</p><p><strong>Objective: </strong>In this study, we aimed to highlight the potential of using Amazon consumer reviews as a novel source for identifying cancer survivorship care needs, particularly related to symptom self-management. Specifically, we present a publicly available, manually annotated corpus derived from Amazon reviews of health-related products and develop baseline natural language processing models using deep learning and large language model (LLM) to demonstrate the usability of this dataset.</p><p><strong>Methods: </strong>We preprocessed the Amazon review dataset to identify sentences with cancer mentions through a rule-based method and conducted content analysis including text feature analysis, sentiment analysis, topic modeling, cancer type, and symptom association analysis. We then designed an annotation guideline, targeting survivorship-relevant constructs. A total of 159 reviews were annotated, and baseline models were developed based on deep learning and large language model (LLM) for named entity recognition and text classification tasks.</p><p><strong>Results: </strong>A total of 4703 sentences containing positive cancer mentions were identified, drawn from 3349 reviews associated with 2589 distinct products. The identified topics through topic modeling revealed meaningful insights into cancer symptom management and survivorship experiences. Examples included discussions of green tea use during chemotherapy, cancer prevention strategies, and product recommendations for breast cancer. Top 15 symptoms in reviews were also identified, with pain being the most frequent symptom, followed by inflammation, fatigue, etc. The annotation labels were designed to capture cancer types, indicated symptoms, and symptom management outcomes. The resulting annotation corpus contains 2067 labels from 159 Amazon reviews. It is publicly accessible, together with the annotation guideline through the Open Health Natural Language Processing (OHNLP) GitHub. Our baseline model, Bert-base-cased, achieved the highest weighted average F1-score, that is, 66.92%, for named entity recognition, and LLM gpt4-1106-preview-chat achieved the highest F1-score for text classification tasks, that is, 66.67% for \"Harmful outcome,\" 88.46% for \"Favorable outcome\" and 73.33% for \"Ambiguous outcome.\"</p><p><strong>Conclusions: </strong>Our results demonstrate the potential of Amazon consumer reviews as a novel data source for identifying persistent symptoms, concerns, and self-management strategies among cancer survivors. This corpus, along with the baseline natural language processing models developed for named ","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e71102"},"PeriodicalIF":2.7,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12456872/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132201","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":"Relationship Between Cognitive Disorder and First-Line Targeted Therapy for Oncogene Driver-Positive Patients With Non-Small Cell Lung Cancer: Prospective Cohort Study.","authors":"Wenjun Chen, Xueyang Hu, Senbang Yao, Ziran Bi, Maoxi Chen, Huaidong Cheng","doi":"10.2196/59647","DOIUrl":"10.2196/59647","url":null,"abstract":"<p><strong>Background: </strong>Previous studies have found and confirmed a correlation between cognitive disorder and chemotherapy. As genetic testing becomes more routine in clinical practice, targeted therapies are increasingly gaining prominence. The relationship between targeted treatment and cognitive function is not yet clear. This study aimed to investigate the correlation between cognitive disorder and targeted treatment by evaluating the changes in cognitive function before and after targeted therapy.</p><p><strong>Objective: </strong>This study aims to explore whether targeted therapy affects cognitive function in patients with advanced lung cancer and to explore the association between cognitive function, the inflammatory biomarker C-reactive protein, and psychological stress.</p><p><strong>Methods: </strong>From the screened cohort of 150 patients with advanced non-small cell lung cancer (NSCLC) with gene mutations, 87 (58%) were rigorously selected for the study. The evaluation instruments used were the Mini-Mental State Examination scale, the Distress Thermometer, and the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 for assessing quality of life.</p><p><strong>Results: </strong>A significantly lower progression-free survival (PFS) was observed in the group of patients surviving advanced NSCLC with cognitive disorder under targeted therapy in contrast to survivors in the group with no cognitive disorder (hazard ratio=0.347, 95% CI 0.209-0.578; P<.001). Furthermore, the objective response rate and disease control rate for the group with cognitive disorder were noted to be 37.8% and 86.7%, respectively, contrastingly lower than those in the group with no cognitive disorder, recorded at 78.6% and 97.6%, respectively. Significant variances were also noted in the Mini-Mental State Examination scores between patients with and without cognitive disorder both before and after targeted therapy (P<.001 in both cases), with a decreasing trend observed in both groups after targeted therapy. Noteworthy differences were found in quality of life scores both before and after targeted therapy (P<.001 in both cases). In addition, notable disparities were apparent in C-reactive protein levels among the 2 groups before and after treatment (P=.03 and P=.048 for each time point, respectively), with an upward trend observed in both groups after targeted therapy. The multivariate Cox regression analysis demonstrated that cognitive function is an independent risk factor for PFS in patients with NSCLC receiving targeted therapy.</p><p><strong>Conclusions: </strong>Cognitive disorder may lead to lower quality of life scores and shorter PFS in patients undergoing targeted therapy. Early screening and intervention for such patients could effectively improve clinical outcomes and quality of life.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e59647"},"PeriodicalIF":2.7,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12491898/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145087691","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":"Exposure to Radiation and Thyroid Cancer Risk Among Young Female Nurses: Longitudinal Analysis From the Korea Nurses' Health Study.","authors":"Young Taek Kim, Choa Sung, Yanghee Pang, Chiyoung Cha","doi":"10.2196/68037","DOIUrl":"10.2196/68037","url":null,"abstract":"<p><strong>Background: </strong>Thyroid cancer is one of the most commonly diagnosed malignancies in South Korea, with incidence rates among the highest globally. Young women, in particular, represent a high-risk group, likely due to a combination of biological, occupational, and environmental factors. However, the specific risk factors contributing to thyroid cancer development in this population remain poorly understood.</p><p><strong>Objective: </strong>This study aims to identify the risk factors associated with thyroid cancer among young female nurses using longitudinal survival analysis.</p><p><strong>Methods: </strong>This longitudinal study used data from the Korea Nurses' Health Study (KNHS), a prospective national cohort of female nurses. Data from the first, fifth, seventh, and ninth surveys were used to construct a person-period data set. Female nurses aged in their 20s at baseline were included. Time-varying explanatory variables included age, marital status, BMI, smoking, alcohol consumption, perceived stress, sleep problems, nursing position, night shift work, working unit, and duration of radiation exposure. The dependent variable was self-reported physician-diagnosed thyroid cancer. Kaplan-Meier survival analysis and Cox proportional hazards regression were performed to examine the association between risk factors and thyroid cancer occurrence.</p><p><strong>Results: </strong>A total of 22,759 person-period cases were analyzed, and 105 thyroid cancer events were identified. Kaplan-Meier analysis showed significant associations between thyroid cancer occurrence and age (χ²₁=51.6, P<.001), marital status (χ²₁=25.1, P<.001), sleep problems (χ²₁=20.3, P<.001), night shift work (χ²₁=20.1, P<.001), working unit (χ²₁=13.0, P<.001), and duration of radiation exposure (χ²₁=91.0, P<.001). In the Cox regression model, nurses aged in their 20s had a significantly higher risk of thyroid cancer than those aged in their 30s (hazard ratio [HR] 4.602, 95% CI 1.893-11.188). Those who worked night shifts were also at an increased risk (HR 1.923, 95% CI 1.127-3.280). Compared with no exposure, radiation exposure showed a dose-response relationship: <1 year: HR 3.449, 95% CI 1.474-8.074; ≥1 year: HR 4.178, 95% CI 2.702-6.461.</p><p><strong>Conclusions: </strong>Younger age, night shift work, and duration of radiation exposure were significantly associated with an increased risk of thyroid cancer in young female nurses. These findings highlight the importance of early screening and occupational risk management, including regular radiation monitoring and support for circadian health, in health care settings.</p><p><strong>International registered report identifier (irrid): </strong>RR2-10.4178/epih.e2024048.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e68037"},"PeriodicalIF":2.7,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12491889/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145087707","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":"Virtual Health Assistants in Preventive Cancer Care Communication: Systematic Review.","authors":"Aantaki Raisa, Xiaobei Chen, Emma G Bryan, Carma L Bylund, Jordan M Alpert, Benjamin Lok, Carla L Fisher, Lyndsey Thomas, Janice L Krieger","doi":"10.2196/73616","DOIUrl":"10.2196/73616","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Virtual health assistants (VHAs), interactive digital programs that emulate human communication, are being increasingly used in health care to improve patient education and care and to reduce the burden on health care providers. VHAs have the potential to promote cancer equity through facilitating patient engagement, providing round-the-clock access to information, and reducing language barriers. However, it is unclear to what extent audience-centeredness is being considered in the development of cancer-related applications.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This systematic review identifies and synthesizes strategies used to make VHA-based cancer prevention and screening interventions audience-centered.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, we searched 4 databases (PubMed, Embase, Web of Science, and EBSCOhost) for peer-reviewed studies on VHA interventions promoting cancer screening (January 2022). Included studies focused on adult populations in primary care settings, with interventions emphasizing interactivity and immediacy (key VHA features). Excluded studies were on cancer treatment, noninteractive decision aids, or technical VHA development. Screening, data extraction, and quality assessment (Mixed Methods Appraisal Tool) were performed independently by multiple reviewers. Thematic synthesis was used to analyze audience-centered strategies.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Of 1055 records screened, 17 studies met inclusion criteria. Most (n=11) targeted colorectal cancer, with others addressing prostate, breast, cervical, or lung cancer. A total of 16 studies were US-based; 1 study focused on Uganda. Key strategies for audience-centered design included: (1) Demographic Concordance: Race or gender alignment between VHA and users (eg, African American participants interacting with Black-coded avatars); (2) User Feedback: Iterative testing via interviews, think-aloud protocols, or pilot studies to refine interventions; (3) Preintervention Needs Assessment: Identifying cultural, linguistic, or literacy barriers (eg, myths about screening in Ugandan communities); (4) Theoretical Frameworks: The Health Belief Model (most common), the Modality, Agency, Interactivity, and Navigability (MAIN) model, or tailored messaging theories guided design; (5) Information Customization: Culturally adapted content (eg, Spanish-language interfaces, narratives addressing racial disparities); and (6) Feature Customization: Adjusting VHA appearance (eg, animations and fonts) based on user preferences. Notably, 7/17 studies focused on racially minoritized groups (eg, African Americans, Hispanic farmworkers), addressing systemic barriers like mistrust in health care. However, gaps persisted in intersectional tailoring (eg, rurality and income) and non-English languages (only 2/17 studies). Recruitment methods influenced diversity; community","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e73616"},"PeriodicalIF":2.7,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12435786/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145070326","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":"Machine Learning for Preoperative Assessment and Postoperative Prediction in Cervical Cancer: Multicenter Retrospective Model Integrating MRI and Clinicopathological Data.","authors":"Shuqi Li, Chenyan Guo, Yufei Fang, Junjun Qiu, He Zhang, Lei Ling, Jie Xu, Xinwei Peng, Chuchu Jiang, Jue Wang, Keqin Hua","doi":"10.2196/69057","DOIUrl":"10.2196/69057","url":null,"abstract":"<p><strong>Background: </strong>Machine learning (ML) has been increasingly applied to cervical cancer (CC) research. However, few studies have combined both clinical parameters and imaging data. At the same time, there remains an urgent need for more robust and accurate preoperative assessment of parametrial invasion and lymph node metastasis, as well as postoperative prognosis prediction.</p><p><strong>Objective: </strong>The objective of this study is to develop an integrated ML model combining clinicopathological variables and magnetic resonance image features for (1) preoperative parametrial invasion and lymph node metastasis detection and (2) postoperative recurrence and survival prediction.</p><p><strong>Methods: </strong>Retrospective data from 250 patients with CC (2014-2022; 2 tertiary hospitals) were analyzed. Variables were assessed for their predictive value regarding parametrial invasion, lymph node metastasis, survival, and recurrence using 7 ML models: K-nearest neighbor (KNN), support vector machine, decision tree, random forest (RF), balanced RF, weighted DT, and weighted KNN. Performance was assessed via 5-fold cross-validation using accuracy, sensitivity, specificity, precision, F1-score, and area under the receiver operating characteristic curve (AUC). The optimal models were deployed in an artificial intelligence-assisted contouring and prognosis prediction system.</p><p><strong>Results: </strong>Among 250 women, there were 11 deaths and 24 recurrences. (1) For preoperative evaluation, the integrated model using balanced RF achieved optimal performance (sensitivity 0.81, specificity 0.85) for parametrial invasion, while weighted KNN achieved the best performance for lymph node metastasis (sensitivity 0.98, AUC 0.72). (2) For postoperative prognosis, weighted KNN also demonstrated high accuracy for recurrence (accuracy 0.94, AUC 0.86) and mortality (accuracy 0.97, AUC 0.77), with relatively balanced sensitivity of 0.80 and 0.33, respectively. (3) An artificial intelligence-assisted contouring and prognosis prediction system was developed to support preoperative evaluation and postoperative prognosis prediction.</p><p><strong>Conclusions: </strong>The integration of clinical data and magnetic resonance images provides enhanced diagnostic capability to preoperatively detect parametrial invasion and lymph node metastasis detection and prognostic capability to predict recurrence and mortality for CC, facilitating personalized, precise treatment strategies.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e69057"},"PeriodicalIF":2.7,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12431160/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145055962","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":"Reducing Hallucinations and Trade-Offs in Responses in Generative AI Chatbots for Cancer Information: Development and Evaluation Study.","authors":"Sota Nishisako, Takahiro Higashi, Fumihiko Wakao","doi":"10.2196/70176","DOIUrl":"10.2196/70176","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Generative artificial intelligence (AI) is increasingly used to find information. Providing accurate information is essential to support patients with cancer and their families; however, information returned by generative AIs is sometimes wrong. Returning wrong information is called hallucination. Retrieval-augmented generation (RAG), which supplements large language model (LLM) outputs with relevant external sources, has the potential to reduce hallucinations. Although RAG has been proposed as a promising technique, its real-world performance in public health communication remains underexplored.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to examine cancer information returned by generative AIs with RAG using cancer-specific information sources and general internet searches to determine whether using RAG with reliable information sources reduces the hallucination rates of generative AI chatbots.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We developed 6 types of chatbots by combining 3 patterns of reference information with 2 versions of LLMs. Thus, GPT-4 and GPT-3.5 chatbots that use cancer information service (CIS) information, Google information, and no reference information (conventional chatbots) were developed. A total of 62 cancer-related questions in Japanese were compiled from public sources. All responses were generated automatically and independently reviewed by 2 experienced clinicians. The reviewers assessed the presence of hallucinations, defined as medically harmful or misinformation. We compared hallucination rates across chatbot types and calculated odds ratios (OR) using generalized linear mixed-effects models. Subgroup analyses were also performed based on whether questions were covered by CIS content.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;For the chatbots that used information from CIS, the hallucination rates were 0% for GPT-4 and 6% for GPT-3.5, whereas those for chatbots that used information from Google were 6% and 10% for GPT-4 and GPT-3.5, respectively. For questions on information that is not issued by CIS, the hallucination rates for Google-based chatbots were 19% for GPT-4 and 35% for GPT-3.5. The hallucination rates for conventional chatbots were approximately 40%. Using reference data from Google searches generated more hallucinations than using CIS data, with an OR of 9.4 (95% CI 1.2-17.5, P&lt;.01); the OR for the conventional chatbot was 16.1 (95% CI 3.7-50.0, P&lt;.001). While conventional chatbots always generated a response, the RAG-based chatbots sometimes declined to answer when information was lacking. The conventional chatbots responded to all questions, but the response rate decreased (36% to 81%) for RAG-based chatbots. For questions on information not covered by CIS, the CIS chatbots did not respond, while the Google chatbots generated responses in 52% of the cases for GPT-4 and 71% for GPT-3.5.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Using RAG with reliable information sources significantly","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e70176"},"PeriodicalIF":2.7,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12425422/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145041727","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":"Understanding and Addressing Challenges With Electronic Health Record Use in Gynecological Oncology: Cross-Sectional Survey of Multidisciplinary Professionals in the United Kingdom and Co-Design of an Integrated Informatics Platform to Support Clinical Decision-Making.","authors":"Laura Tookman, Rachael Lear, Yusuf S Abdullahi, Amit Samani, Phoebe Averill, Ashton Hunt, Dimitri Papadimitriou, Baleseng Elizabeth Nkolobe, Sadaf Ghaem-Maghami, Ben Glampson, Iain A McNeish, Erik K Mayer","doi":"10.2196/58657","DOIUrl":"10.2196/58657","url":null,"abstract":"<p><strong>Background: </strong>Electronic health records (EHRs) are a cornerstone of modern health care delivery, but their current configuration often fragments information across systems, impeding timely and effective clinical decision-making. In gynecological oncology, where care involves complex, multidisciplinary coordination, these limitations can significantly impact the quality and efficiency of patient management. Few studies have examined how EHR systems support clinical decision-making from the perspective of end users. This study aimed to explore multiprofessional experiences of EHR use in gynecological oncology and to develop a co-designed informatics platform to improve decision-making for ovarian cancer care.</p><p><strong>Objective: </strong>This study aims to evaluate the perspectives of health care professionals on retrieving routine clinical data from EHRs in the management of ovarian cancer and to design an integrated informatics platform that supports clinical decision-making.</p><p><strong>Methods: </strong>We conducted a national cross-sectional survey of 92 UK-based professionals working in gynecological oncology, including oncologists, nurses, radiologists, and other specialists in ovarian cancer. The web-based questionnaire, combining quantitative and free-text responses, assessed their experiences with EHR use, focusing on information retrieval, usability challenges, perceived risks, and benefits. In parallel, a human-centered design approach involving health care professionals, data engineers, and informatics experts codeveloped a digital informatics platform that integrates structured and unstructured data from multiple clinical systems into a unified patient summary view for clinical decision-making. Natural language processing was applied to extract genomic and surgical information from free-text records, with data pipelines validated by clinicians against original clinical system sources.</p><p><strong>Results: </strong>Among 92 respondents, 84 out of 91 (92%) routinely accessed multiple EHR systems, with 26 out of 91 (29%) using 5 or more. Notably, 16 out of 92 respondents (17%) reported spending more than 50% of their clinical time searching for patient information. Key challenges included lack of interoperability (35/141 reported challenges, 24.8%), difficulty locating critical data such as genetic results (57/85 respondents, 67%), and poor organization of information. Only 10 out of 92 professionals (11%) strongly agreed that their systems provided well-organized data for clinical use. While ease of access to patient data was a key benefit, 54 out of 90 respondents (60%) reported lacking access to comprehensive patient summaries. To address these issues, our co-designed informatics platform consolidates disparate patients' data from different EHR systems into a single visual display to support clinical decision-making and audit.</p><p><strong>Conclusions: </strong>Current EHR systems are suboptimal for support","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e58657"},"PeriodicalIF":2.7,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12422591/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145034413","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":"Exploring Women's Perceptions of Traditional Mammography and the Concept of AI-Driven Thermography to Improve the Breast Cancer Screening Journey: Mixed Methods Study.","authors":"Kristýna Sirka Kacafírková, Anneleen Poll, An Jacobs, Antonella Cardone, Juan-Jose Ventura","doi":"10.2196/64954","DOIUrl":"10.2196/64954","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Breast cancer is the most common cancer among women and a leading cause of mortality in Europe. Early detection through screening reduces mortality, yet participation in mammography-based programs remains suboptimal due to discomfort, radiation exposure, and accessibility issues. Thermography, particularly when driven by artificial intelligence (AI), is being explored as a noninvasive, radiation-free alternative. However, its acceptance, reliability, and impact on the screening experience remain underexplored.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to explore women's perceptions of AI-enhanced thermography (ThermoBreast) as an alternative to mammography. It aims to identify barriers and motivators related to breast cancer screening and assess how ThermoBreast might improve the screening experience.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A mixed methods approach was adopted, combining an online survey with follow-up focus groups. The survey captured women's knowledge, attitudes, and experiences related to breast cancer screening and was used to recruit participants for qualitative exploration. After the focus groups, the survey was relaunched to include additional respondents. Quantitative data were analyzed using SPSS (IBM Corp), and qualitative data were analyzed in MAXQDA (VERBI software). Findings from both strands were synthesized to redesign the breast cancer screening journey.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 228 valid survey responses were analyzed. Of 228, 154 women (68%) had previously undergone mammography, while 74 (32%) had not. The most reported motivators were belief in prevention (69/154, 45%), invitations from screening programs (68/154, 44%), and doctor recommendations (45/154, 29%). Among nonscreeners, key barriers included no recommendation from a doctor (39/74, 53%), absence of symptoms (27/74, 36%), and perceived age ineligibility (17/74, 23%). Pain, long appointment waits, and fear of radiation were also mentioned. In total, 18 women (mean age 45.3 years, SD 13.6) participated in 6 focus groups. Participants emphasized the importance of respectful and empathetic interactions with medical staff, clear communication, and emotional comfort-factors they perceived as more influential than the screening technology itself. ThermoBreast was positively received for being contactless, radiation-free, and potentially more comfortable. Participants described it as \"less traumatic,\" \"easier,\" and \"a game changer.\" However, concerns were raised regarding its novelty, lack of clinical validation, and data privacy. Some participants expressed the need for human oversight in AI-supported procedures and requested more information on how AI is used. Based on these insights, an updated screening journey was developed, highlighting improvements in preparation, appointment booking, privacy, and communication of results.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;While AI-driven thermography shows promise as","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e64954"},"PeriodicalIF":2.7,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12422528/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145034351","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 Burden of Cancer and Precancerous Conditions Among Transgender Individuals in a Large Health Care Network: Retrospective Cohort Study.","authors":"Shuang Yang, Yongqiu Li, Christopher W Wheldon, Jessica Y Islam, Mattia Prosperi, Thomas J George, Elizabeth A Shenkman, Fei Wang, Jiang Bian, Yi Guo","doi":"10.2196/73843","DOIUrl":"10.2196/73843","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Disparities in cancer burden between transgender and cisgender individuals remain an underexplored area of research.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to examine the cumulative incidence and associated risk factors for cancer and precancerous conditions among transgender individuals compared with matched cisgender individuals.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a retrospective cohort study using patient-level electronic health record (EHR) data from the University of Florida Health Integrated Data Repository between 2012 and 2023. Transgender individuals were identified using a validated, computable phenotype algorithm that used structured data and clinical notes. They matched 1:10:10 by age and calendar year of index date with cisgender women and cisgender men. The index date was the first transgender-related record for transgender individuals and a matched diagnosis date for cisgender controls. Primary outcomes included new-onset cancers associated with human papillomavirus, human immunodeficiency virus, tobacco, alcohol, lung, breast, and colorectal sites. Secondary outcomes were precancerous conditions related to the same cancer types. We calculated cumulative incidence rates and conducted time-to-event analyses using the Fine-Gray method, treating all-cause death as a competing risk, to assess associations between gender identity and the presence of cancer or precancer, adjusting for demographic and clinical covariates. Interaction analyses evaluated if associations between cancer risk factors and precancer differed by gender identity.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We identified 2745 transgender individuals (mean age at index date 25.1, SD 14.0 years) and matched them with 27,450 cisgender women and 27,450 cisgender men from the same health care system. The cumulative incidence of cancer did not differ significantly between transgender and cisgender cohorts (transgender n=28, 1.0% vs cisgender women, n=358, 1.3%; P=.13 and cisgender men, n=314, 1.1%; P=.64). However, transgender individuals exhibited significantly higher risks for precancerous conditions compared to cisgender women (subdistribution hazard ratios [sHRs] 1.1, 95% CI 1.0-1.3) and cisgender men (sHR 1.3; 95% CI 1.2-1.5). Specifically, transgender individuals were more likely to develop colorectal precancer (sHR 1.2; 95% CI 1.1-1.4) compared to cisgender women, as well as human papillomavirus-related precancer (sHR 1.8; 95% CI 1.4-2.3) and colorectal precancer (sHR 1.4; 95% CI 1.2-1.6) compared to cisgender men. Subgroup analyses showed similar patterns in both female-to-male and male-to-female individuals compared with their matched cisgender counterparts. Interaction analyses revealed stronger protective effects of private insurance or Medicare against precancers in transgender individuals than in cisgender peers, while being non-Hispanic Black or having substantial comorbidities were stronger risk factors among trans","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e73843"},"PeriodicalIF":2.7,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12416876/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145024377","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":"Retraction: \"Designing Positive Psychology Interventions for Social Media: Cross-Sectional Web-Based Experiment With Young Adults With Cancer\".","authors":"","doi":"10.2196/82724","DOIUrl":"10.2196/82724","url":null,"abstract":"","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e82724"},"PeriodicalIF":2.7,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12413141/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145006633","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}