DCE-MRI-based machine learning model for predicting axillary lymph node metastasis in breast cancer.

ABSTRACT:

Background

Accurate evaluation of the axillary lymph node (ALN) status is needed for determining the treatment protocol for breast cancer. This study aimed to build an artificial intelligence (AI) model to predict ALN metastasis based on pre-treatment dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) of breast cancer combined with radiomics algorithms.

Methods

Pre-treatment DCE-MRI dataset of 166 patients with pathologically confirmed breast cancer diagnosis from January 2017 to August 2020 was collected, and all patients were randomly divided into a training group and test group with a ratio of 7:3. Each patient underwent pre-enhancement as well as post-enhancement 1-6 MRI, and a total of 7,224 two-dimensional (2D) and 9,863 three-dimensional (3D) features were

SUBMITTER: Zhang Q 

PROVIDER: S-EPMC11921391 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature