This study by Xu et al. developed a predictive fusion model combining multi sequence MRI radiomics, deep learning features, and habitat imaging, to predict pathological complete response (pCR) in breast cancer patients that were undergoing neoadjuvant therapy (NAT). A retrospective analysis of 203 patients (training = 162, test = 41) used T2 weighted imaging, diffusion weighted imaging, and dynamic contrast enhanced MRI to extract intra tumoral and peri tumoral radiomics features. Habitat imaging segmented tumors into biologically distinct subregions to capture heterogeneity. Deep learning features were also used to enrich tumor representation. The fusion model outperformed both single sequence and single region models, achieving an AUC of 0.913 on the test set, with an improved precision recall balance and a higher clinical benefit based off a decision curve analysis. SHAP identified DCE_LLL_DependenceUniformity as the strongest predictor of pCR, while PC72 indicated non pCR outcomes, and LIME provided patient specific interpretability. Overall, the authors concluded that combining radiomics, habitat imaging, and deep learning in this fusion model creates a comprehensive and non-invasive framework for improving pCR prediction.