Machine learning-driven ultrasound radiomics for assessing axillary lymph node burden in breast cancer.

ABSTRACT:

Objective

This study explores the value of combining intratumoral and peritumoral radiomics features from ultrasound imaging with clinical characteristics to assess axillary lymph node burden in breast cancer patients.

Methods

A total of 131 breast cancer patients with axillary lymph node metastasis (ALNM) were enrolled between June 2019 and September 2024. Patients were divided into low (n=79) and high (n=52) axillary lymph node burden (ALNB) groups. They were further split into training (n=92) and validation (n=39) cohorts. Intratumoral and peritumoral features were analyzed using the maximum relevance minimum redundancy (MRMR) and least absolute shrinkage and selection operator (LASSO) methods. Six machine learning models were evaluated, and a combined clinical-radiomics

SUBMITTER: Wang SR 

PROVIDER: S-EPMC11903272 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature