Combining Conventional MRI and DCE-MRI Radiomics for Prediction of Breast Cancer Molecular Subtypes: A Retrospective Single-Center Cross-Sectional Study.

Purpose: To evaluate whether radiomic features extracted from first post-contrast DCE-MRI improve the prediction of breast cancer molecular subtypes beyond conventional MRI features, and to compare the performance of conventional MRI-based, radiomics-based, and combined models. Materials and Methods: In this retrospective, single-center cross-sectional study with prediction modelling, 206 consecutive patients with pathologically confirmed primary breast cancer who underwent pretreatment 3.0-T breast MRI between January 2018 and January 2026 were included. Molecular subtypes were assigned using immunohistochemistry, with HER2-equivocal cases confirmed by FISH/dual ISH. Conventional MRI features were assessed according to the BI-RADS MRI lexicon. Radiomic features were extracted from a manually segmented 2D ROI on the first post-contrast DCE-MRI image, harmonized with ComBat, filtered for reproducibility, and selected using LASSO. One-vs-rest logistic regression models based on MRI features alone, radiomics alone, and combined features were constructed and compared. Results: The cohort comprised 81 Luminal A (LA), 70 Luminal B (LB), 31 HER2-enriched (HER2), and 24 triple-negative breast cancers (TNBC). On multivariable analysis, LA tumors were less likely to have positive lymph nodes (OR, 0.31; p = 0.001), LB tumors were associated with multifocality (OR, 2.22; p = 0.004), HER2 tumors were less likely to present as a mass lesion (OR, 0.21; p = 0.020), and TNBC was strongly associated with rim enhancement (OR, 12.42; p < 0.001). The combined model yielded the highest AUCs for LA (0.788), LB (0.732), HER2 (0.858), and TNBC (0.890), compared with MRI models (AUC range, 0.616-0.765) and radiomics models (AUC range, 0.692-0.853). Pairwise DeLong comparisons showed subtype-dependent incremental value, with the clearest added value for HER2 and additional improvement over MRI models for LB and TNBC. Conclusion: DCE-MRI radiomics adds incremental value to conventional MRI for predicting breast cancer molecular subtypes. Integrating radiomic and morphologic MRI features provides the best discrimination, particularly for HER2 and TNBC.