Deep Learning Radiomics of Multiparametric MRI for Individualized Prediction of Axillary Lymph Node Response After Neoadjuvant Chemotherapy in Breast Cancer.

Purpose: To develop a deep learning radiomics (DLR) model based on longitudinal multiparametric breast MRI to predict axillary lymph node (ALN) response following neoadjuvant therapy (NAT) in breast cancer patients. Patients and Methods: This single-center retrospective study included 254 breast cancer patients who underwent NAT followed by surgery from January 2017 to October 2023. Pre- and post-NAT multiparametric MRI scans were analyzed to extract radiomics and deep learning features. The dataset was randomly divided into a training cohort (n = 144) and a validation cohort (n = 110). Feature selection was performed using the Mann-Whitney Results: Estrogen receptor status, HER2 status, and clinical T stage were independent predictors of axillary pathological complete response (apCR). The DLR model achieved the highest predictive performance, with AUCs of 0.939 (95% CI: 0.905-0.974) in the training set and 0.856 (95% CI: 0.774-0.938) in the validation set. DeLong tests showed that the DLR model outperformed only the clinical model (p < 0.0001). A bootstrap analysis (2000 iterations) further showed that the AUC difference between the training and validation cohorts was statistically significant (difference = 0.083; 95% CI: 0.0019-0.1786; p = 0.043). Conclusion: This study is among the first to integrate longitudinal multiparametric MRI with deep learning-based radiomics for predicting ALN response after NAT. The proposed DLR model may provide a noninvasive aid to individualized axillary decision-making, pending external validation.