[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124416-en":3,"doc-seo-124416-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124416,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",7,"Healthcare","Development and validation of an interpretable machine learning model for acute radiation dermatitis in breast cancer","Radiation dermatitis is a frequent, debilitating adverse reaction during breast cancer radiotherapy, and accurate early risk identification is essential for prevention and resource planning. This research develops and validates an interpretable machine learning model using 691 postoperative radiotherapy patients, evaluating 14 algorithms with 10-fold cross-validation and SHAP-based explainability. Results show robust discrimination and calibration, with decision-curve evidence of clinical net benefit. Key predictors include CTVsc, CTVim, TNM stage II, and diabetes status, supporting individualized radiotherapy planning to reduce severe ARD risk.","TYPE Original Research PUBLISHED 17 October 2025 DOI 10.3389/fonc.2025.1663293  \nOPEN ACCESS  \nEDITED BY  \nJehee Isabelle Choi,  \nMemorial Sloan Kettering Cancer Center, United States  \nREVIEWED BY  \nManijeh Beigi,  \nIran University of Medical Sciences, Iran Abraham Peele Karlapudi,  \nVignan Group, India  \n*CORRESPONDENCE  \nZhikun Liu  \n [victory.liu@aliyun.com](victory.liu@aliyun.com)[ ](victory.liu@aliyun.com)Liguo Liu  \n [drmcdreamy@163.com](drmcdreamy@163.com)  \nRECEIVED 10 July 2025  \nACCEPTED 07 October 2025  \nPUBLISHED 17 October 2025  \nCITATION  \nDuan X, Liu Y, Shang Y, Lu X, Zhou Y, Liu Land Liu Z (2025) Development and validation of an interpretable machine learning model for acute radiation dermatitis in breast cancer. Front. Oncol. 15:1663293 .  \ndoi: 10.3389/fonc.2025.1663293  \nCOPYRIGHT  \n© 2025 Duan, Liu, Shang, Lu, Zhou, Liu and Liu. 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.  \nDevelopment and validation of an interpretable machine learning model for acute radiation dermatitis in breast cancer  \nXuejuan Duan 1, Yadong Liu 2, Yuguang Shang 1, Xiaomeng Lu 1, Yanhong Zhou 1, Liguo Liu 3* and Zhikun Liu 1*  \n1 Department of Radiation Oncology, Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China, 2 Department of Oncology, Hebei General Hospital, Shijiazhuang, Hebei, China, 3Second Department of Hepatopancreatobiliary Surgery, China-Japan Friendship Hospital, Beijing, China  \nBackground and Purpose: Radiation dermatitis (RD), a common adverse reaction in breast cancer radiotherapy, impairs quality of life and increases healthcare burdens. Developing an effective risk prediction model is crucial for early high-risk patient identification and preventive interventions.  \nMaterials and Methods: This study enrolled 691 breast cancer patients undergoing postoperative radiotherapy at our center from February 1 to December 19, 2024 . RD severity and correlates were monitored during and 2 weeks after radiotherapy. The dataset was divided into training (n=552) and test (n=139) cohorts. Fourteen machine learning algorithms were evaluated via 10-fold cross-validation, with model selection based on Area Under the Curve (AUC) and other metrics. Model reliability was verified using an internal hold-out test set, and SHAP analysis ensured interpretability.  \nResults: Among 691 patients,52 . 68%(n=364) developed grade ≥2 acute RD. The random forest model performed best, achieving an AUC of 0 . 84 (95% CI: 0 .807– 0.873) in training and 0.748 (0.665–0.831) in testing, with training/testing sensitivity/specificity of 0 . 811/0 .747 and 0 . 877/0 . 576, respectively. Calibration curves confirmed prediction-observation consistency. Decision curve analysis indicated 0 .2–0.4 higher net benefits than “treat-all” or “treat-none” strategies at 25%–75% treatment thresholds. Shapley Additive exPlanations (SHAP) analysis identified Clinical Target Volume-Supraclavicular (CTVsc), Clinical Target Volume-Internal Mammary (CTVim), TNM stage II, and diabetic status as key predictors.  \nConclusion: This explainable machine learning model demonstrates robust discriminative power and clinical utility. Interpretability analysis revealed feature nonlinearities, providing a theoretical basis for personalized radiotherapy planning to reduce severe RD risk.  \nKEYWORDS  \nradiation dermatitis, predictive model, breast cancer, SHAP, radiotherapy  \nFrontiers in Oncology 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nBreast cancer remains the most prevalent malignancy among women, with its incidence and mortality rates continuing to rise r","cbCainJ9Qta9e7ZD","https://ap.wps.com/l/cbCainJ9Qta9e7ZD","pdf",3174961,1,10,"English","en",105,"# Introduction\n## Background and Purpose\n# Materials and Methods\n## Study Cohort and Modeling Approach\n## Interpretability Methods\n# Results\n## Model Performance and Calibration\n## Decision Curve and Key Predictors\n# Conclusion","[{\"question\":\"What was the main goal of the study?\",\"answer\":\"To develop and validate an interpretable machine learning model that predicts the risk of acute radiation dermatitis in breast cancer patients so clinicians can identify high-risk patients early.\"},{\"question\":\"How was the model built and validated?\",\"answer\":\"The study used 691 postoperative radiotherapy patients, split into training (n=552) and test (n=139) cohorts. Fourteen machine learning algorithms were compared using 10-fold cross-validation, and reliability was checked with an internal hold-out test set; SHAP was used for interpretability.\"},{\"question\":\"Which factors were identified as key predictors?\",\"answer\":\"SHAP analysis highlighted Clinical Target Volume-Supraclavicular (CTVsc), Clinical Target Volume-Internal Mammary (CTVim), TNM stage II, and diabetic status as important predictors of acute radiation dermatitis severity.\"}]","Development and validation of an interpretable machine learning model for acute radiation dermatitis in breast cancer | PDF",1785822142,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"development-and-validation-of-an-interpretable-machine-learning-model-for-acute-radiation-dermatitis-in-breast-cancer","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/development-and-validation-of-an-interpretable-machine-learning-model-for-acute-radiation-dermatitis-in-breast-cancer/124416/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What was the main goal of the study?","Question",{"text":75,"@type":76},"To develop and validate an interpretable machine learning model that predicts the risk of acute radiation dermatitis in breast cancer patients so clinicians can identify high-risk patients early.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the model built and validated?",{"text":80,"@type":76},"The study used 691 postoperative radiotherapy patients, split into training (n=552) and test (n=139) cohorts. Fourteen machine learning algorithms were compared using 10-fold cross-validation, and reliability was checked with an internal hold-out test set; SHAP was used for interpretability.",{"name":82,"@type":73,"acceptedAnswer":83},"Which factors were identified as key predictors?",{"text":84,"@type":76},"SHAP analysis highlighted Clinical Target Volume-Supraclavicular (CTVsc), Clinical Target Volume-Internal Mammary (CTVim), TNM stage II, and diabetic status as important predictors of acute radiation dermatitis severity.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]