Explainable Machine Learning Based on Habitat Analysis of Axillary Lymph Node Ultrasound Images for Preoperatively Noninvasive Evaluation of Axillary Lymph Node Metastasis in Breast Cancer: A Bi-center Study

Accurate evaluation of axillary lymph node (ALN) is crucial for guiding staging and treatment strategies in breast cancer (BC) patients. This study aimed to develop an optimal machine learning model for predicting ALN status by utilizing both conventional radiomics and habitat analysis based on axillary B-mode ultrasound (BMUS) images, offering a powerful, non-invasive means to quantify and map ALN heterogeneity, providing deeper insights into biological behavior of ALN metastasis.