Integrating multiparametric MRI radiomics and clinical models to assess sensitivity to neoadjuvant chemotherapy in breast cancer: A multicenter study

Objective To develop and externally validate an interpretable multiparametric MRI‐based radiomic‐clinical model using Shapley Additive Explanations (SHAP) methodology for early prediction of breast cancer sensitivity to neoadjuvant chemotherapy (NAC). Methods This retrospective multicentric study enrolled 223 breast cancer patients from three medical centers. Patients underwent pretreatment multiparametric MRI (DCE‐MRI and DWI sequences) with Miller‐Payne grades 4‐5 defining NAC‐sensitive. Manual tumor segmentation generated regions of interest for extracting 2,396 radiomic features per patient. Feature selection integrated reproducibility analysis (ICC > 0.7), univariable significance testing ( p < 0.01), LASSO regression, and hierarchical clustering. A support vector machine (SVM) model incorporated optimized radiomic signatures and clinical variables. SHAP methodology provided global feature importance interpretation and individualized prediction explanations. Results The integrated radiomic‐clinical model demonstrated superior performance to standalone clinical (AUC 0.720) and radiomic (AUC 0.833) models in the internal validation set, achieving an AUC of 0.904 (95% CI: 0.816–0.991). This advantage persisted in external validation (AUC 0.928, 95% CI: 0.874–0.982). SHAP analysis identified wavelet_HHL_glcm_Correlation_DCE as the predominant predictive feature, with high values significantly correlating to NAC‐sensitive. A clinical nomogram translated model outputs into quantifiable risk probabilities, where total scores ≥130 indicated > 95% sensitivity likelihood. Conclusion The SHAP‐explainable radiomic‐clinical model provides a clinically applicable, noninvasive tool for pretreatment stratification of NAC sensitivity. This approach enhances personalized therapeutic decision‐making while minimizing unnecessary treatment toxicity.