Multichannel Deep Learning Method Based on Multi-Regional DCE-MRI for Predicting Axillary Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer

Objectives : To develop and compare multi-regional 2D and 2.5D deep learning models based on DCE-MR for noninvasive prediction of axillary pathological complete response (pCR) after neoadjuvant chemotherapy (NAC). Methods : This retrospective study enrolled 305 patients with invasive breast cancer and ipsilateral ALN metastasis, which were randomly assigned to a training set (n = 214) and a validation set (n = 91). The principal component analysis, U test, Spearman correlation analysis, mRMR and least absolute shrinkage and selection operator were used to select the most significant features. Seven supervised predictive models were constructed based on regions of interest from the primary tumor and ALNs. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve analysis, and decision curve analysis. Results : Among the 305 patients, axillary pCR accounted for 46.6% (142/305). ER status, tumor MP grade and HER2 status were selected as independent predictors for axillary pCR(P < 0.05). Both the 2.5D _T and the 2.5D _T + ALN models had higher AUC than those of the 2.5D _T and 2D _T + ALN models in the validation set (AUC: 0.797 vs 0.706, 0.834 vs 0.815). When combining clinicopathological factors, the 2.5D _T + ALN +Clinic model achieved the highest performance among all models (AUC = 0.861). Model-guided decision-making reduced the estimated rate of unnecessary ALND from 40.7% to 8.8% and increased the overall clinical benefit rate from 59.3% to 74.7%. Conclusion The 2.5D _T + ALN +Clinic model could accurate, noninvasive prediction of axillary pCR after NAC, which has the potential to support individualized axillary surgical decision-making.