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Choosing between mastectomy and breast-conserving surgery (BCS) is complex due to heterogeneous pre-operative clinical factors. We developed BrCaM (\n                    <jats:italic>Br<\/jats:italic>\n                    east\n                    <jats:italic>Ca<\/jats:italic>\n                    ncer\n                    <jats:italic>M<\/jats:italic>\n                    odel), a machine learning\u2013based Clinical Prediction Model designed to analyze pre-operative surgical decision patterns. A dataset of 5100 patients (age range: 18\u201396 years) treated at a Breast Unit with standardized protocols was used. Surgeon-guided feature selection and an end-to-end machine learning pipeline were implemented. Multiple algorithms were evaluated; AdaBoost performed best using 10-fold cross-validation. BrCaM achieved an overall accuracy of 95% in distinguishing BCS from mastectomy. Feature selection identified clinically meaningful predictors that reflect established criteria influencing surgical decisions. In this retrospective setting, BrCaM captures real-world surgical decision patterns based on clinical factors. These findings support the consistency of current clinical practice and provide a foundation for future prospective validation as a clinical decision-support adjunct.\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-43281-6","type":"journal-article","created":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T14:47:04Z","timestamp":1773672424000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["BrCaM an artificial intelligence model for surgical decision making in breast cancer"],"prefix":"10.1038","volume":"16","author":[{"given":"Daniela","family":"Evangelista","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vasuk","family":"Gautam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luca","family":"Silvestri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mario","family":"Zanfardino","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Monica","family":"Franzese","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Massimiliano","family":"D\u2019Aiuto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,16]]},"reference":[{"key":"43281_CR1","first-page":"7","volume":"66","author":"KD Miller","year":"2016","unstructured":"Miller, K. 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The anonymous nature of the predictive model does not allow to trace in any way sensitive personal data. Therefore, the present web-survey study does not require approval by the Ethics Committee. Moreover, we declare, The study was conducted using historical medical records (2009\u20132015) that were fully anonymized prior to analysis. According to institutional policy and applicable regulations, no formal ethics approval was required for the use of anonymized data as they were acquired before the GDPR - (UE) n. 2016\/679. To document this, a self-declaration signed by the Principal Investigator (PI) has been prepared, confirming that the study did not require IRB\/EC approval under local regulations. This declaration is provided in the supplementary materials.With regard to anonymization and access control processes, all patient identifiers were removed prior to data integration. Indirect identifiers were also aggregated to prevent re-identification. Data was stored in a secure MySQL database with access restricted to authorized team members through individual credentials and role-based permissions. The GitHub repository referenced in the manuscript contains only scripts and documentation, not raw or sensitive data.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics declarations"}}],"article-number":"13598"}}