Machine Learning Model Based on Multiparametric MRI for Distinguishing HER2 Expression Level in Breast Cancer.

ABSTRACT: This study aimed to develop machine learning models based on conventional MRI features to classify HER2 expression levels in invasive breast cancer and explore their association with disease-free survival (DFS). A total of 678 patients from two centers were included, with Center 1 divided into training and internal test sets and Center 2 serving as an external test set. Random Forest models were trained to distinguish HER2-positive vs. HER2-negative (Task 1) and HER2-low vs. HER2-zero tumors (Task 2) using BI-RADS-based MRI features. SHapley Additive exPlanations were applied to rank feature importance, assist feature selection, and enhance model interpretability. DFS was analyzed using Kaplan-Meier curves and log-rank tests. In Task 1, key features included tumor size, axillary lymph node

SUBMITTER: Chen Y 

PROVIDER: S-EPMC12839683 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature