NILS: An Artificial Neural Network Tool for Personalized Noninvasive Lymph Node Staging in Breast Cancer

Abstract

While breast cancer commonly presents at an early stage without axillary lymphatic spread, surgical axillary staging by sentinel lymph node biopsy (SLNB) is routinely performed in all patients with clinically node-negative disease. This study presents the development and implementation of a personalized prediction tool for noninvasive lymph node staging (NILS) using artificial neural network algorithms. Routinely available clinical and tumor-related variables in the preoperative setting from a consecutive cohort of 800 breast cancer cases were included in model training and internal validation. The model was trained to handle missing data for the ten input variables, and the performance to distinguish benign/metastatic lymph nodes ranged from AUCs 0.72 to 0.74, with good calibration. The potential to abstain from axillary surgery was observed in 26% of patients using NILS for the prediction of nodal status and acknowledged a false negative rate of 10%, which is clinically accepted for the standard SLNB technique.