Multiparametric MRI-based radiomics combined with machine learning for preoperative differentiation of luminal A and luminal B breast cancer: a multicenter study

Accurate preoperative differentiation of luminal A from luminal B breast cancer is critical for individualized treatment planning, yet current assessment relies on invasive biopsy with inherent sampling limitations. This study aimed to develop and validate a multiparametric MRI-based radiomics model for non-invasive luminal subtype classification across independent institutions. This retrospective multicenter study included 130 patients with pathologically confirmed luminal A ( n = 44) or luminal B ( n = 86) invasive ductal carcinoma from three centers. Radiomics features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI (DCE-MRI) using PyRadiomics. A four-stage feature selection pipeline (variance thresholding, Pearson correlation, ANOVA F-test, and L1-regularized logistic regression with five-fold cross-validation) was applied for dimensionality reduction. Synthetic Minority Over-sampling Technique (SMOTE) was used within training folds to address class imbalance. Nine machine learning classifiers were systematically compared. The model was developed using Center 1 data ( n = 64) with five-fold cross-validation and externally validated in two independent cohorts [Center 2 ( n = 36) and Center 3 ( n = 30)]. Performance was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and decision curve analysis (DCA). Among nine classifiers, XGBoost demonstrated the best performance. The clinical-radiomics fusion model achieved AUCs of 0.8944 ± 0.0711, 0.8483 ± 0.0326, and 0.7269 ± 0.0241 in Centers 1, 2, and 3, respectively. This model significantly outperformed single-sequence approaches in pairwise DeLong tests ( P < 0.05) and demonstrated superior net benefit in DCA. SHAP analysis revealed that radiomics features from DWI and DCE-MRI were the most influential predictors, with consistent importance rankings across all three centers. The multiparametric MRI radiomics model integrating T2WI, DWI, and DCE-MRI shows favorable performance for differentiating luminal A from luminal B breast cancer across multiple independent centers. SHAP-based interpretability analysis improves model transparency by identifying key predictive features. The model may serve as a non-invasive tool to support breast cancer subtype stratification in clinical settings and as a complementary approach to immunohistochemistry by capturing whole-tumor heterogeneity.