Simultaneous Preoperative Prediction of Locally Advanced Breast Cancer, DCIS Component, and Multifocality Using Structured Mammographic Features and Gradient-Boosting Machine Learning

Background/Objectives: Accurate preoperative detection of locally advanced breast cancer is essential for neoadjuvant therapy planning. We developed and validated gradient-boosting models using structured BI-RADS mammographic features to simultaneously predict locally advanced breast cancer (LABC), DCIS component, and multifocality in a multi-center cohort. Methods: This retrospective study enrolled 2295 patients from three university-affiliated hospitals; features were coded according to BI-RADS. CatBoost and logistic regression models were built using stratified 60/20/20 splits, with performance assessed via bootstrap resampling, nested cross-validation, and sensitivity analyses. AUROC, AUPRC, Brier score, and calibration metrics assessed discrimination and clinical utility; a leakage audit and SHAP analysis supported interpretation. Results: CatBoost achieved an AUROC of 0.906 (95% CI: 0.876–0.932) for LABC. Because several top predictors overlap with the anatomical criteria defining this outcome, we repeated the analysis excluding them; the reduced model retained a mean AUROC of 0.739, indicating genuine predictive signal beyond the staging overlap. Net benefit was positive across all relevant thresholds, with calibration error of 0.053. DCIS prediction was highly accurate (AUROC 0.979; nested AUROC 0.9707), with no evidence of leakage. Multifocality prediction was more modest (AUROC 0.810), reflecting known limits of two-dimensional mammography. Sensitivity analyses confirmed stable performance across splits, training sizes, and class-weighting schemes. Conclusions: Structured mammographic features combined with gradient-boosting support clinically meaningful, though partly overlapping, risk stratification for LABC; once accounted for, the model still retains independent value. The DCIS model performed very well; multifocality prediction remains more limited, and external validation is needed before clinical use.