Optimizing surgical decisions in breast cancer is critical. Choosing between mastectomy and breast-conserving surgery (BCS) is complex due to heterogeneous pre-operative clinical factors. We developed BrCaM (Breast Cancer Model), a machine learning–based Clinical Prediction Model designed to analyze pre-operative surgical decision patterns. A dataset of 5100 patients (age range: 18–96 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.
Keywords: Breast cancer; Breast conserving surgery (BCS); Clinical prediction models (CPM); Machine learning; Mastectomy.