A machine learning approach based on multiparametric MRI to identify the risk of non-sentinel lymph node metastasis in patients with breast cancer

Motivation: ALN status is crucial for clinical staging, prognosis assessment, and treatment decision for breast cancer patients. Goal(s): We aimed to assess feasibility of ML based on mpMRI for predicting the risk of NSLN metastasis in breast cancer patients. Approach: mpMRI including T1WI, T2WI, DWI, and DCE-MRI was acquired, and four ML models were constructed. Results: ML model incorporating mpMRI features and clinical factors can predict NSLN metastasis with fair accuracy for breast cancer, with an AUC of 0.781 in test dataset. Five factors for NSLN metastasis were found, including histological grade, cortical thickness, fatty hilum, short axis of lymph node, and age. Impact: The proposed ML model may benefit for breast cancer patients with 1-2 positive SLN but consistently negative NSLN to avoid overtreatment and improve individualized axillary management.