Predicting axillary lymph node metastasis in breast cancer using a multimodal radiomics and deep learning model.

ABSTRACT:

Objective

To explore the value of combined radiomics and deep learning models using different machine learning algorithms based on mammography (MG) and magnetic resonance imaging (MRI) for predicting axillary lymph node metastasis (ALNM) in breast cancer (BC). The objective is to provide guidance for developing scientifically individualized treatment plans, assessing prognosis, and planning preoperative interventions.

Methods

A retrospective analysis was conducted on clinical and imaging data from 270 patients with BC confirmed by surgical pathology at the Third Hospital of Shanxi Medical University between November 2022 and April 2024. Multiple sequence images from MG and MRI were selected, and regions of interest in the lesions were delineated. Radiomics and deep learning

SUBMITTER: Guo F 

PROVIDER: S-EPMC11671510 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature