Deep Learning Radiomics of Multiparametric MRI for Individualized Prediction of Axillary Lymph Node Response After
Shunian Li1,2, Ruimin Li3, Mengting Xu2,4
1Department of Radiology, People's Hospital of Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
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 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.
