{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T05:27:47Z","timestamp":1786512467401,"version":"build-2736575974"},"reference-count":25,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aimed to develop and validate an interpretable machine learning model to predict the 1- to 3-year risk of cardiovascular events in breast cancer patients by integrating baseline and treatment variables, while preliminarily investigating the potential association between short-term cardiac function decline and long-term adverse cardiovascular events.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed electronic medical records from 31,878 breast cancer patients. A composite cardiovascular event outcome was used. Predictors were selected via a two-step process: removing highly correlated variables (|r|\u22650.7) and applying LASSO regression with 10-fold cross-validation, which refined 62 initial variables down to 18. Five models were built and compared using the area under the receiver operating characteristic curve (AUC-ROC). The optimal model was interpreted using SHapley Additive exPlanations (SHAP).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 31,878 breast cancer patients, 3,960 (12.4%) experienced cardiovascular events. The XGBoost model demonstrated the best overall discriminative performance (AUC\u00a0=\u00a00.790). SHAP analysis identified endocrine therapy, anemia management therapy, and history of cerebrovascular disease as the top three predictors. Crucially, short-term decline in cardiac function was also selected as a significant predictor, supporting its role as a precursor to long-term events. Model robustness was confirmed via sensitivity analysis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1814219","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T04:38:40Z","timestamp":1786509520000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting treatment-related cardiovascular risks in breast cancer patients: development and validation of an interpretable machine learning model"],"prefix":"10.3389","volume":"16","author":[{"given":"Luxin","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Yan","sequence":"additional","affiliation":[{"name":"Internet Medical and System Applications of National Engineering Laboratory, The First Affiliated Hospital of Zhengzhou University","place":["Zhengzhou, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinming","family":"Yu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangfang","family":"Cui","sequence":"additional","affiliation":[{"name":"Internet Medical and System Applications of National Engineering Laboratory, The First Affiliated Hospital of Zhengzhou University","place":["Zhengzhou, Henan, 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