JMIR Cancer最新文献

{"title":"Evaluation of Large Language Models in Tailoring Educational Content for Cancer Survivors and Their Caregivers: Quality Analysis.","authors":"Darren Liu, Xiao Hu, Canhua Xiao, Jinbing Bai, Zahra A Barandouzi, Stephanie Lee, Caitlin Webster, La-Urshalar Brock, Lindsay Lee, Delgersuren Bold, Yufen Lin","doi":"10.2196/67914","DOIUrl":"https://doi.org/10.2196/67914","url":null,"abstract":"<p><strong>Background: </strong>Cancer survivors and their caregivers, particularly those from disadvantaged backgrounds with limited health literacy or racial and ethnic minorities facing language barriers, are at a disproportionately higher risk of experiencing symptom burdens from cancer and its treatments. Large language models (LLMs) offer a promising avenue for generating concise, linguistically appropriate, and accessible educational materials tailored to these populations. However, there is limited research evaluating how effectively LLMs perform in creating targeted content for individuals with diverse literacy and language needs.</p><p><strong>Objective: </strong>This study aimed to evaluate the overall performance of LLMs in generating tailored educational content for cancer survivors and their caregivers with limited health literacy or language barriers, compare the performances of 3 Generative Pretrained Transformer (GPT) models (ie, GPT-3.5 Turbo, GPT-4, and GPT-4 Turbo; OpenAI), and examine how different prompting approaches influence the quality of the generated content.</p><p><strong>Methods: </strong>We selected 30 topics from national guidelines on cancer care and education. GPT-3.5 Turbo, GPT-4, and GPT-4 Turbo were used to generate tailored content of up to 250 words at a 6th-grade reading level, with translations into Spanish and Chinese for each topic. Two distinct prompting approaches (textual and bulleted) were applied and evaluated. Nine oncology experts evaluated 360 generated responses based on predetermined criteria: word limit, reading level, and quality assessment (ie, clarity, accuracy, relevance, completeness, and comprehensibility). ANOVA (analysis of variance) or chi-square analyses were used to compare differences among the various GPT models and prompts.</p><p><strong>Results: </strong>Overall, LLMs showed excellent performance in tailoring educational content, with 74.2% (267/360) adhering to the specified word limit and achieving an average quality assessment score of 8.933 out of 10. However, LLMs showed moderate performance in reading level, with 41.1% (148/360) of content failing to meet the sixth-grade reading level. LLMs demonstrated strong translation capabilities, achieving an accuracy of 96.7% (87/90) for Spanish and 81.1% (73/90) for Chinese translations. Common errors included imprecise scopes, inaccuracies in definitions, and content that lacked actionable recommendations. The more advanced GPT-4 family models showed better overall performance compared to GPT-3.5 Turbo. Prompting GPTs to produce bulleted-format content was likely to result in better educational content compared with textual-format content.</p><p><strong>Conclusions: </strong>All 3 LLMs demonstrated high potential for delivering multilingual, concise, and low health literacy educational content for cancer survivors and caregivers who face limited literacy or language barriers. GPT-4 family models were notably more robust. While fur","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e67914"},"PeriodicalIF":3.3,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796509","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":"Digital Health Intervention to Reduce Malnutrition Among Individuals With Gastrointestinal Cancer Receiving Cytoreductive Surgery Combined With Hyperthermic Intraperitoneal Chemotherapy: Feasibility, Acceptability, and Usability Trial.","authors":"Yu Chen Lin, Ryan Hagen, Benjamin D Powers, Sean P Dineen, Jeanine Milano, Emma Hume, Olivia Sprow, Sophia Diaz-Carraway, Jennifer B Permuth, Jeremiah Deneve, Amir Alishahi Tabriz, Kea Turner","doi":"10.2196/67108","DOIUrl":"https://doi.org/10.2196/67108","url":null,"abstract":"<p><strong>Background: </strong>Cytoreductive surgery combined with hyperthermic intraperitoneal chemotherapy (CRS-HIPEC) can improve survival outcomes for individuals with gastrointestinal (GI) cancer and peritoneal disease (PD). Individuals with GI cancer and PD receiving CRS-HIPEC are at increased risk for malnutrition. Despite the increased risk for malnutrition, there has been limited study of nutritional interventions for individuals receiving CRS-HIPEC.</p><p><strong>Objective: </strong>We aimed to test the feasibility, acceptability, and usability of Support Through Remote Observation and Nutrition Guidance (STRONG), a multilevel digital health intervention to improve nutritional management among individuals with GI cancer and PD receiving CRS-HIPEC. We also assessed patient-reported outcomes, including malnutrition risk, health-related quality of life, and weight-related measures.</p><p><strong>Methods: </strong>STRONG is a 12-week digital intervention in which participants received biweekly nutritional counseling with a dietitian, logged food intake using a Fitbit tracker, and reported nutrition-related outcomes. Dietitians received access to a web-based dashboard and remotely monitored patients' reported food intake and nutrition-impact symptoms. Implementation outcomes were assessed against prespecified benchmarks consistent with benchmarks used in prior studies. Changes in patient-reported outcomes at baseline and follow-up were assessed using linear and ordered logistic regressions.</p><p><strong>Results: </strong>Participants (N=10) had a median age of 57.5 (IQR 54-69) years. Feasibility benchmarks were achieved for recruitment (10/17, 59% vs benchmark: 50%), study assessment completion (9/10, 90% vs benchmark: 60%), dietitian appointment attendance (7/10, 70% vs benchmark: 60%), daily food intake logging adherence (6/10, 60% vs benchmark: 60%), and participant retention (10/10, 100% vs benchmark: 60%). Most participants rated the intervention as acceptable (8/10, 80% vs benchmark: 70%) and reported a high level of usability for dietitian services (10/10, 100%). The benchmark usability for the Fitbit tracker to log food intake was not met. Compared to baseline, participants saw on average a 6.0 point reduction in malnutrition risk score (P=.01), a 20.5 point improvement in general health-related quality of life score (P=.002), and a 5.6 percentage point increase in 1-month weight change (P=.04) at the end of the study.</p><p><strong>Conclusions: </strong>The STRONG intervention demonstrated to be feasible, acceptable, and usable among individuals with GI cancer and PD receiving CRS-HIPEC. A fully powered randomized controlled trial is needed to test the effectiveness of STRONG for reducing malnutrition and improving patient outcomes.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e67108"},"PeriodicalIF":3.3,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143804432","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":"AI-Based Identification Method for Cervical Transformation Zone Within Digital Colposcopy: Development and Multicenter Validation Study.","authors":"Tong Wu, Yuting Wang, Xiaoli Cui, Peng Xue, Youlin Qiao","doi":"10.2196/69672","DOIUrl":"https://doi.org/10.2196/69672","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;In low- and middle-income countries, cervical cancer remains a leading cause of death and morbidity for women. Early detection and treatment of precancerous lesions are critical in cervical cancer prevention, and colposcopy is a primary diagnostic tool for identifying cervical lesions and guiding biopsies. The transformation zone (TZ) is where a stratified squamous epithelium develops from the metaplasia of simple columnar epithelium and is the most common site of precancerous lesions. However, inexperienced colposcopists may find it challenging to accurately identify the type and location of the TZ during a colposcopy examination.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to present an artificial intelligence (AI) method for identifying the TZ to enhance colposcopy examination and evaluate its potential clinical application.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The study retrospectively collected data from 3616 women who underwent colposcopy at 6 tertiary hospitals in China between 2019 and 2021. A dataset from 4 hospitals was collected for model conduction. An independent dataset was collected from the other 2 geographic hospitals to validate model performance. There is no overlap between the training and validation datasets. Anonymized digital records, including each colposcopy image, baseline clinical characteristics, colposcopic findings, and pathological outcomes, were collected. The classification model was proposed as a lightweight neural network with multiscale feature enhancement capabilities and designed to classify the 3 types of TZ. The pretrained FastSAM model was first implemented to identify the location of the new squamocolumnar junction for segmenting the TZ. Overall accuracy, average precision, and recall were evaluated for the classification and segmentation models. The classification performance on the external validation was assessed by sensitivity and specificity.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The optimal TZ classification model performed with 83.97% classification accuracy on the test set, which achieved average precision of 91.84%, 89.06%, and 95.62% for types 1, 2, and 3, respectively. The recall and mean average precision of the TZ segmentation model were 0.78 and 0.75, respectively. The proposed model demonstrated outstanding performance in predicting 3 types of the TZ, achieving the sensitivity with 95% CIs for TZ1, TZ2, and TZ3 of 0.78 (0.74-0.81), 0.81 (0.78-0.82), and 0.8 (0.74-0.87), respectively, with specificity with 95% CIs of 0.94 (0.92-0.96), 0.83 (0.81-0.86), and 0.91 (0.89-0.92), based on a comprehensive external dataset of 1335 cases from 2 of the 6 hospitals.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Our proposed AI-based identification system classified the type of cervical TZs and delineated their location on multicenter, colposcopic, high-resolution images. The findings of this study have shown its potential to predict TZ types and specific regions accurately. It was deve","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e69672"},"PeriodicalIF":3.3,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143755007","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":"Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review.","authors":"David Chen, Saif Addeen Alnassar, Kate Elizabeth Avison, Ryan S Huang, Srinivas Raman","doi":"10.2196/65984","DOIUrl":"10.2196/65984","url":null,"abstract":"<p><strong>Background: </strong>Natural language processing systems for data extraction from unstructured clinical text require expert-driven input for labeled annotations and model training. The natural language processing competency of large language models (LLM) can enable automated data extraction of important patient characteristics from electronic health records, which is useful for accelerating cancer clinical research and informing oncology care.</p><p><strong>Objective: </strong>This scoping review aims to map the current landscape, including definitions, frameworks, and future directions of LLMs applied to data extraction from clinical text in oncology.</p><p><strong>Methods: </strong>We queried Ovid MEDLINE for primary, peer-reviewed research studies published since 2000 on June 2, 2024, using oncology- and LLM-related keywords. This scoping review included studies that evaluated the performance of an LLM applied to data extraction from clinical text in oncology contexts. Study attributes and main outcomes were extracted to outline key trends of research in LLM-based data extraction.</p><p><strong>Results: </strong>The literature search yielded 24 studies for inclusion. The majority of studies assessed original and fine-tuned variants of the BERT LLM (n=18, 75%) followed by the Chat-GPT conversational LLM (n=6, 25%). LLMs for data extraction were commonly applied in pan-cancer clinical settings (n=11, 46%), followed by breast (n=4, 17%), and lung (n=4, 17%) cancer contexts, and were evaluated using multi-institution datasets (n=18, 75%). Comparing the studies published in 2022-2024 versus 2019-2021, both the total number of studies (18 vs 6) and the proportion of studies using prompt engineering increased (5/18, 28% vs 0/6, 0%), while the proportion using fine-tuning decreased (8/18, 44.4% vs 6/6, 100%). Advantages of LLMs included positive data extraction performance and reduced manual workload.</p><p><strong>Conclusions: </strong>LLMs applied to data extraction in oncology can serve as useful automated tools to reduce the administrative burden of reviewing patient health records and increase time for patient-facing care. Recent advances in prompt-engineering and fine-tuning methods, and multimodal data extraction present promising directions for future research. Further studies are needed to evaluate the performance of LLM-enabled data extraction in clinical domains beyond the training dataset and to assess the scope and integration of LLMs into real-world clinical environments.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e65984"},"PeriodicalIF":3.3,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11970800/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143736145","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":"Using ChatGPT to Improve the Presentation of Plain Language Summaries of Cochrane Systematic Reviews About Oncology Interventions: Cross-Sectional Study.","authors":"Jelena Šuto Pavičić, Ana Marušić, Ivan Buljan","doi":"10.2196/63347","DOIUrl":"10.2196/63347","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Plain language summaries (PLSs) of Cochrane systematic reviews are a simple format for presenting medical information to the lay public. This is particularly important in oncology, where patients have a more active role in decision-making. However, current PLS formats often exceed the readability requirements for the general population. There is still a lack of cost-effective and more automated solutions to this problem.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study assessed whether a large language model (eg, ChatGPT) can improve the readability and linguistic characteristics of Cochrane PLSs about oncology interventions, without changing evidence synthesis conclusions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The dataset included 275 scientific abstracts and corresponding PLSs of Cochrane systematic reviews about oncology interventions. ChatGPT-4 was tasked to make each scientific abstract into a PLS using 3 prompts as follows: (1) rewrite this scientific abstract into a PLS to achieve a Simple Measure of Gobbledygook (SMOG) index of 6, (2) rewrite the PLS from prompt 1 so it is more emotional, and (3) rewrite this scientific abstract so it is easier to read and more appropriate for the lay audience. ChatGPT-generated PLSs were analyzed for word count, level of readability (SMOG index), and linguistic characteristics using Linguistic Inquiry and Word Count (LIWC) software and compared with the original PLSs. Two independent assessors reviewed the conclusiveness categories of ChatGPT-generated PLSs and compared them with original abstracts to evaluate consistency. The conclusion of each abstract about the efficacy and safety of the intervention was categorized as conclusive (positive/negative/equal), inconclusive, or unclear. Group comparisons were conducted using the Friedman nonparametric test.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;ChatGPT-generated PLSs using the first prompt (SMOG index 6) were the shortest and easiest to read, with a median SMOG score of 8.2 (95% CI 8-8.4), compared with the original PLSs (median SMOG score 13.1, 95% CI 12.9-13.4). These PLSs had a median word count of 240 (95% CI 232-248) compared with the original PLSs' median word count of 364 (95% CI 339-388). The second prompt (emotional tone) generated PLSs with a median SMOG score of 11.4 (95% CI 11.1-12), again lower than the original PLSs. PLSs produced with the third prompt (write simpler and easier) had a median SMOG score of 8.7 (95% CI 8.4-8.8). ChatGPT-generated PLSs across all prompts demonstrated reduced analytical tone and increased authenticity, clout, and emotional tone compared with the original PLSs. Importantly, the conclusiveness categorization of the original abstracts was unchanged in the ChatGPT-generated PLSs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;ChatGPT can be a valuable tool in simplifying PLSs as medically related formats for lay audiences. More research is needed, including oversight mechanisms to ensure that the information is ","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e63347"},"PeriodicalIF":3.3,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11939027/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143664750","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":"Monthly Variations in Colorectal Cancer Screening Tests Among Federally Qualified Health Center Patients in Missouri: Quality Improvement Project.","authors":"Jane A McElroy, Jamie B Smith, Kevin D Everett","doi":"10.2196/64809","DOIUrl":"10.2196/64809","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Cancer is the second leading cause of death in the United States. Compelling evidence shows screening detects colorectal cancer (CRC) at earlier stages and prevents the development of CRC through the removal of precancerous polyps. The Healthy People 2030 goal for CRC screening is 68.3%, but only 36.5% of Missouri federally qualified health center patients aged 50-75 years are up-to-date on CRC screening. For average risk patients, there are three commonly used screening tests in the United States-two types of stool tests collected at home (fecal immunochemical test [FIT]-immunochemical fecal occult blood test [FOBT] and FIT-DNA, such as Cologuard) and colonoscopies completed at procedural centers.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to examine variation by month for the three types of CRC testing to evaluate consistent patient care by clinical staff.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Data from 31 federally qualified health center clinics in Missouri from 2011 to 2023 were analyzed. A sample of 34,124 unique eligible \"average risk\" patients defined as persons not having a personal history of CRC or certain types of polyps, family history of CRC, personal history of inflammatory bowel disease, and personal history of receiving radiation to the abdomen or pelvic to treat a previous cancer or confirmed or suspected hereditary CRC syndrome. Another eligibility criterion is that patients need to be seen at least once at the clinic to be included in the denominator for the screening rate calculation. Descriptive statistics characterize the sample, while bivariate analyses assess differences in screening types by month.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Completion of CRC screening yielded statistically significant differences for patients completing the different types of CRC screening by month. October-January had the highest proportions of patients (644-680 per month, 8.5%-10.2%) receiving a colonoscopy, while February-April had the lowest (509-578 per month, 6.9%-7.8%), with 614 being the average monthly number of colonoscopies. For FIT-FOBT, June-August had the higher proportions of patients receiving this test (563-613 per month, 8.9%-9.6%), whereas December-February had the lowest (453-495 per month, 7.1%-8%), with 541 being the average monthly number of FIT-FOBT kits used. For FIT-DNA, March was the most popular month with 11.3% (n=261 per month) of patients using the Cologuard test, followed by April, May, and November (207-220 per month, 8.7%-9.4%), and January and June (168-171 per month, 7.2%-7.3%) had the lowest proportion of patients using Cologuard, with 193 being the average monthly number of FIT-DNA kits used. Combining all tests, February had the fewest CRC tests completed (1153/16,173, 7.1%).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Home-based tests are becoming popular, replacing the gold standard colonoscopy, but need to be repeated more frequently. Monthly variation of screening over the course o","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e64809"},"PeriodicalIF":3.3,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11941909/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143664737","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":"Analyzing Online Search Trends for Kidney, Prostate, and Bladder Cancers in China: Infodemiology Study Using Baidu Search Data (2011-2023).","authors":"Shuangquan Lin, Lingxing Duan, Xiangda Xu, Haichao Cao, Xiongbing Lu, Xi Wen, Shanzun Wei","doi":"10.2196/57414","DOIUrl":"10.2196/57414","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Cancers of the bladder, kidney, and prostate are the 3 major genitourinary cancers that significantly contribute to the global burden of disease (GBD) and continue to show increasing rates of morbidity and mortality worldwide. In mainland China, understanding the cancer burden on patients and their families is crucial; however, public awareness and concerns about these cancers, particularly from the patient's perspective, remain predominantly focused on financial costs. A more comprehensive exploration of their needs and concerns has yet to be fully addressed.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to analyze trends in online searches and user information-seeking behaviors related to bladder, kidney, and prostate cancers-encompassing descriptive terms (eg, \"bladder cancer,\" \"kidney cancer,\" \"prostate cancer\") as well as related synonyms and variations-on both national and regional scales. This study leverages data from mainland China's leading search engine to explore the implications of these search patterns for addressing user needs and improving health management.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The study analyzed Baidu Index search trends for bladder, kidney, and prostate cancers (from January 2011 to August 2023) at national and provincial levels. Search volume data were analyzed using the joinpoint regression model to calculate annual percentage changes (APCs) and average APCs (AAPCs), identifying shifts in public interest. User demand was assessed by categorizing the top 10 related terms weekly into 13 predefined topics, including diagnosis, treatment, and traditional Chinese medicine. Data visualization and statistical analyses were performed using Prism 9. Results revealed keyword trends, demographic distributions, and public information needs, offering insights into health communication and management strategies based on online information-seeking behavior.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Three cancer topics were analyzed using 39 search keywords, yielding a total Baidu Search Index (BSI) of 43,643,453. From 2011 to 2015, the overall APC was 15.2% (P&lt;.05), followed by -2.8% from 2015 to 2021, and 8.9% from 2021 to 2023, with an AAPC of 4.9%. Bladder, kidney, and prostate cancers exhibited AAPCs of 2.8%, 3.9%, and 6.8%, respectively (P&lt;.05). The age distribution of individuals searching for these cancer topics varied across the topics. Geographically, searches for cancer were predominantly conducted by people from East China, who accounted for approximately 30% of each cancer search query. Regarding user demand, the total BSI for relevant user demand terms from August 2022 to August 2023 was 676,526,998 out of 2,570,697,380 (15.74%), representing only a limited total cancer-related search volume.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Online searches and inquiries related to genitourinary cancers are on the rise. The depth of users' information demands appears to be influenced by regional economic ","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e57414"},"PeriodicalIF":3.3,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11953601/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143630792","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":"Assisted Reproductive Technology and Risk of Childhood Cancer Among the Offspring of Parents With Infertility: Systematic Review and Meta-Analysis.","authors":"Gao Song, Cai-Qiong Zhang, Zhong-Ping Bai, Rong Li, Meng-Qun Cheng","doi":"10.2196/65820","DOIUrl":"10.2196/65820","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The relationship between assisted reproductive technology (ART) and childhood cancer risk has been widely debated. Previous meta-analyses did not adequately account for the impact of infertility, and this study addresses this gap.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;Our primary objective was to assess the relative risk (RR) of childhood cancer in infertile populations using ART versus non-ART offspring, with a secondary focus on comparing frozen embryo transfer (FET) and fresh embryo transfer (fresh-ET).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A literature review was conducted through PubMed, Embase, Cochrane, and Web of Science, with a cutoff date of July 10, 2024. The study was registered with the International Platform of Registered Systematic Review and Meta-Analysis Protocols (INPLASY 202470119). Inclusion criteria were based on the PICOS (Population, Intervention, Comparison, Outcomes, and Study Design) framework: infertile or subfertile couples (population), ART interventions (in vitro fertilization [IVF], intracytoplasmic sperm injection [ICSI], FET, and fresh-ET), non-ART comparison, and childhood cancer risk outcomes. Data abstraction focused on the primary exposures (ART vs non-ART and FET vs fresh-ET) and outcomes (childhood cancer risk). The risk of bias was assessed using the Newcastle-Ottawa Quality Assessment Scale, and the evidence quality was evaluated with the Grading of Recommendations Assessment, Development, and Evaluation (GRADE). Pooled estimates and 95% CIs were calculated using random effects models.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 18 studies were included, published between 2000 and 2024, consisting of 14 (78%) cohort studies and 4 (22%) case-control studies, all of which were of moderate to high quality. The cohort studies had follow-up periods ranging from 3 to 18 years. Compared with non-ART conception, ART conception was not significantly associated with an increased risk of childhood overall cancer (RR 0.95, 95% CI 0.71-1.27; GRADE quality: low to moderate). Subgroup analyses of IVF (RR 0.86, 95% CI 0.59-1.25), ICSI (RR 0.76, 95% CI 0.26-2.2), FET (RR 0.98, 95% CI 0.54-1.76), and fresh-ET (RR 0.75, 95% CI 0.49-1.15) showed similar findings. No significant differences were found for specific childhood cancers, including leukemia (RR 0.99, 95% CI 0.79-1.24), lymphoma (RR 1.22, 95% CI 0.64-2.34), brain cancer (RR 1.22, 95% CI 0.73-2.05), embryonal tumors (RR 1, 95% CI 0.63-1.58), retinoblastoma (RR 1.3, 95% CI 0.73-2.31), and neuroblastoma (RR 1.02, 95% CI 0.48-2.16). Additionally, no significant difference was observed in a head-to-head comparison of FET versus fresh-ET (RR 0.99, 95% CI 0.86-1.14; GRADE quality: moderate).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;In conclusion, this study found no significant difference in the risk of childhood cancer between offspring conceived through ART and those conceived through non-ART treatments (such as fertility drugs or intrauterine insemination","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e65820"},"PeriodicalIF":3.3,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11921989/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143617548","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":"Identifying Adverse Events in Outpatients With Prostate Cancer Using Pharmaceutical Care Records in Community Pharmacies: Application of Named Entity Recognition.","authors":"Yuki Yanagisawa, Satoshi Watabe, Sakura Yokoyama, Kyoko Sayama, Hayato Kizaki, Masami Tsuchiya, Shungo Imai, Mitsuhiro Someya, Ryoo Taniguchi, Shuntaro Yada, Eiji Aramaki, Satoko Hori","doi":"10.2196/69663","DOIUrl":"10.2196/69663","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Androgen receptor axis-targeting reagents (ARATs) have become key drugs for patients with castration-resistant prostate cancer (CRPC). ARATs are taken long term in outpatient settings, and effective adverse event (AE) monitoring can help prolong treatment duration for patients with CRPC. Despite the importance of monitoring, few studies have identified which AEs can be captured and assessed in community pharmacies, where pharmacists in Japan dispense medications, provide counseling, and monitor potential AEs for outpatients prescribed ARATs. Therefore, we anticipated that a named entity recognition (NER) system might be used to extract AEs recorded in pharmaceutical care records generated by community pharmacists.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to evaluate whether an NER system can effectively and systematically identify AEs in outpatients undergoing ARAT therapy by reviewing pharmaceutical care records generated by community pharmacists, focusing on assessment notes, which often contain detailed records of AEs. Additionally, the study sought to determine whether outpatient pharmacotherapy monitoring can be enhanced by using NER to systematically collect AEs from pharmaceutical care records.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We used an NER system based on the widely used Japanese medical term extraction system MedNER-CR-JA, which uses Bidirectional Encoder Representations from Transformers (BERT). To evaluate its performance for pharmaceutical care records by community pharmacists, the NER system was first applied to 1008 assessment notes in records related to anticancer drug prescriptions. Three pharmaceutically proficient researchers compared the results with the annotated notes assigned symptom tags according to annotation guidelines and evaluated the performance of the NER system on the assessment notes in the pharmaceutical care records. The system was then applied to 2193 assessment notes for patients prescribed ARATs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The F&lt;sub&gt;1&lt;/sub&gt;-score for exact matches of all symptom tags between the NER system and annotators was 0.72, confirming the NER system has sufficient performance for application to pharmaceutical care records. The NER system automatically assigned 1900 symptom tags for the 2193 assessment notes from patients prescribed ARATs; 623 tags (32.8%) were positive symptom tags (symptoms present), while 1067 tags (56.2%) were negative symptom tags (symptoms absent). Positive symptom tags included ARAT-related AEs such as \"pain,\" \"skin disorders,\" \"fatigue,\" and \"gastrointestinal symptoms.\" Many other symptoms were classified as serious AEs. Furthermore, differences in symptom tag profiles reflecting pharmacists' AE monitoring were observed between androgen synthesis inhibition and androgen receptor signaling inhibition.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The NER system successfully extracted AEs from pharmaceutical care records of patients prescribed A","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e69663"},"PeriodicalIF":3.3,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11937706/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143606115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing Public Interest in Mammography, Computed Tomography Lung Cancer Screening, and Computed Tomography Colonography Screening Examinations Using Internet Search Data: Cross-Sectional Study.","authors":"Zachary D Zippi, Isabel O Cortopassi, Rolf A Grage, Elizabeth M Johnson, Matthew R McCann, Patricia J Mergo, Sushil K Sonavane, Justin T Stowell, Brent P Little","doi":"10.2196/53328","DOIUrl":"10.2196/53328","url":null,"abstract":"<p><strong>Background: </strong>The noninvasive imaging examinations of mammography (MG), low-dose computed tomography (CT) for lung cancer screening (LCS), and CT colonography (CTC) play important roles in screening for the most common cancer types. Internet search data can be used to gauge public interest in screening techniques, assess common screening-related questions and concerns, and formulate public awareness strategies.</p><p><strong>Objective: </strong>This study aims to compare historical Google search volumes for MG, LCS, and CTC and to determine the most common search topics.</p><p><strong>Methods: </strong>Google Trends data were used to quantify relative Google search frequencies for these imaging screening modalities over the last 2 decades. A commercial search engine tracking product (keywordtool.io) was used to assess the content of related Google queries over the year from May 1, 2022, to April 30, 2023, and 2 authors used an iterative process to agree upon a list of thematic categories for these queries. Queries with at least 10 monthly instances were independently assigned to the most appropriate category by the 2 authors, with disagreements resolved by consensus.</p><p><strong>Results: </strong>The mean 20-year relative search volume for MG was approximately 10-fold higher than for LCS and 25-fold higher than for CTC. Search volumes for LCS have trended upward since 2011. The most common topics of MG-related searches included nearby screening locations (60,850/253,810, 24%) and inquiries about procedural discomfort (28,970/253,810, 11%). Most common LCS-related searches included CT-specific inquiries (5380/11,150, 48%) or general inquiries (1790/11,150, 16%), use of artificial intelligence or deep learning (1210/11,150, 11%), and eligibility criteria (1020/11,150, 9%). For CTC, the most common searches were CT-specific inquiries (1800/5590, 32%) or procedural details (1380/5590, 25%).</p><p><strong>Conclusions: </strong>Over the past 2 decades, Google search volumes have been significantly higher for MG than for either LCS or CTC, although search volumes for LCS have trended upward since 2011. Knowledge of public interest and queries related to imaging-based screening techniques may help guide public awareness efforts.</p>","PeriodicalId":45538,"journal":{"name":"JMIR Cancer","volume":"11 ","pages":"e53328"},"PeriodicalIF":3.3,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11918978/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143605989","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}