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

Shunian Li,1,2,* Ruimin Li,3,* Mengting Xu,2,4 Jun Liao,1,2 Ziwei Cao,1,2 Bo Gong,4,5 Fengshan Yan,2 Meiyun Wang,1,2 Hongna Tan1,2 1Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, People’s Republic of China; 2Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, People’s Republic of China; 3Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People’s Republic of China; 4Department of Radiology, People’s Hospital of Henan University, Zhengzhou, Henan, People’s Republic of China; 5Department of Radiology, The Second People’s Hospital of Henan Province, Zhengzhou, Henan, People’s Republic of China*These authors contributed equally to this workCorrespondence: Hongna Tan, Department of Radiology,Henan Provincial People’s Hospital, No. 7 Wei Wu Road, Jinshui District, Zhengzhou, 450003, People’s Republic of China, Tel +86 13526766042, Email [email protected]: 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 U-test, Spearman correlation analysis, and least absolute shrinkage and selection operator regression. Eight machine learning algorithms were compared, with logistic regression selected as the final classifier. Four models were constructed: clinical, radiomics, deep learning, and the DLR model. Performance was evaluated using ROC analysis, calibration curves, and decision curve analysis.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.Keywords: breast cancer, magnetic resonance imaging, deep learning, neoadjuvant therapy