An intelligent MRI-based all-in-one diagnostic strategy for axillary lymph node status in breast cancer.

An integrated diagnostic strategy of preoperative identification of sentinel lymph node (SLN) metastasis, SLN metastatic burden, and non-SLN (NSLN) metastasis in breast cancer remains to be developed to guide axillary surgery de-escalation. Here we develop a magnetic resonance imaging-based hierarchical multitask deep learning model, breast cancer axillary lymph node network (BCALN-Net) to predict SLN metastasis, SLN metastatic burden, and NSLN metastasis in 6,271 breast cancer patients. BCALN-Net achieves high performance in predicting SLN metastasis, SLN metastatic burden, and NSLN metastasis and exhibits robust performance across molecular subtypes, clinical tumor stages, clinical node stages, estrogen receptor statuses, human epidermal growth factor receptor 2 statuses, menopausal statuses, and SLN metastatic burdens. In pooled analysis of 4,081 patients, BCALN-Net also shows superior performance in predicting the omission of axillary invasive procedures and added value over clinical criteria. BCALN-Net holds the potential to provide an integrated diagnostic strategy of ALN status to help axillary surgery de-escalation in breast cancer patients.