[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128769-en":3,"doc-seo-128769-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128769,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","A machine learning-based model for predicting recurrence in intermediate-and high-risk differentiated thyroid cancer - insights from a retrospective single-center study of 2388 patients","A retrospective single-center study develops an interpretable machine learning framework to improve recurrence risk stratification for intermediate- and high-risk differentiated thyroid cancer (DTC). Using 2,388 patients randomly split into training and validation cohorts, the work identifies recurrence-associated factors and trains six ML models. Random forest shows the strongest predictive performance and supports clinical decision-making through a random forest-based online calculator and decision curve utility assessment.","TYPE Original Research PUBLISHED 17 June 2025  \nDOI 10.3389/fendo.2025.1552479  \nOPEN ACCESS  \nEDITED BY  \nAngeliki Chorti,  \nAristotle University of Thessaloniki, Greece  \nREVIEWED BY  \nJincao Yao,  \nUniversity of Chinese Academy of Sciences, China  \nGeorgios Markantes,  \nUniversity of Patras, Patras, Greece Selen Soylu,  \nIstanbul University-Cerrahpasa, Türkiye  \n*CORRESPONDENCE  \nJie Ming  \n [mingjiewh@126.com](mingjiewh@126.com)  \n†These authors have contributed equally to this work  \nRECEIVED 28 December 2024  \nACCEPTED 21 May 2025  \nPUBLISHED 17 June 2025  \nCITATION  \nLi Y, Tang Z, Ren A, Tian G, Zhang J, Wang Y, Liu J and Ming J (2025) A machine learningbased model for predicting recurrence in intermediate-and high-risk differentiated thyroid cancer: insights from a retrospective single-center study of 2388 patients.  \nFront. Endocrinol. 16:1552479 .  \ndoi: 10.3389/fendo.2025.1552479  \nCOPYRIGHT  \n© 2025 Li, Tang, Ren, Tian, Zhang, Wang, Liu and Ming. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nA machine learning-based model for predicting recurrence in intermediate-and high-risk differentiated thyroid cancer:  \ninsights from a retrospective single-center study of 2388 patients  \nYi Li 1†, Zimei Tang 1†, Anwen Ren 1, Gang Tian 1, Jianing Zhang 1, Yiran Wang 1, Jie Liu 2 and Jie Ming 1*  \n1 Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China, 2 Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China  \nPurpose: Current guidelines provide a recognized yet broad framework for stratifying recurrence risk in differentiated thyroid cancer (DTC) patients. More precise tools are needed for intermediate-and high-risk groups. This study aims to identify recurrence-associated risk factors and develop a machine learningbased predictive model.  \nMethods: In this retrospective analysis, 2,388 DTC patients were randomly assigned to a training group (1,910 cases) and a validation group (478 cases) . Predictive factors were identified using univariate and multivariate analyses. Six machine learning models were trained and validated, with performance evaluated through accuracy, area under the curve, and clinical utility via decision curve analysis.  \nResults: Independent risk factors for recurrence included intraglandular dissemination, total tumor size, bilateral cervical lymph node involvement, and Hashimoto’s thyroiditis, while normal/elevated TSH and multifocal nodules were protective. The random forest model demonstrated the best performance (training accuracy: 0 . 801; validation accuracy: 0 . 808) . A random forest-based online calculator was developed to facilitate individualized risk assessment in clinical settings.  \nConclusions: The random forest model effectively predicts DTC recurrence, offering a practical tool for individualized risk assessment and aiding clinical decision-making.  \nKEYWORDS  \ndifferentiated thyroid cancer (DTC), cancer recurrence, predictive models, machine learning, risk factors, random forest  \nFrontiers in Endocrinology 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nDifferentiated thyroid cancer (DTC), primarily comprising papillary and follicular subtypes, is the most common endocrine malignancy, accounting for approximately 90% of thyroid cancer cases (1). Despite its generally favorable prognosis, with a five-year survival rate exceeding 95%, a subset of DTC patients experiences a higher probability of recurrence, which significantly imp","cbCaidj2NcpvXOX2","https://ap.wps.com/l/cbCaidj2NcpvXOX2","pdf",4490064,3,1,12,"English","en",105,"# Introduction\n## Machine learning for recurrence prediction in DTC\n## Limitations of existing ATA risk stratification\n# Methods\n## Retrospective cohort and dataset split\n## Model training and validation\n## Evaluation metrics and decision curve analysis\n# Results\n## Independent recurrence risk factors\n## Protective factors\n## Best-performing model and online calculator\n# Conclusions","[{\"question\":\"What was the purpose of the machine learning model in this study?\",\"answer\":\"To identify recurrence-associated risk factors and develop a machine learning-based predictive model tailored for intermediate- and high-risk differentiated thyroid cancer patients.\"},{\"question\":\"How was the dataset organized for model training and validation?\",\"answer\":\"The study retrospectively analyzed 2,388 patients and randomly assigned them into a training group (1,910) and a validation group (478).\"},{\"question\":\"Which model performed best and what tool was created for clinical use?\",\"answer\":\"The random forest model achieved the best performance, and the study developed a random forest-based online calculator to support individualized risk assessment.\"}]","A machine learning-based model for predicting recurrence in intermediate-and high-risk differentiated thyroid cancer - 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