MRI-based machine learning radiomics for prediction of HER2 expression status in breast invasive ductal carcinoma.

ABSTRACT:

Background

Human epidermal growth factor receptor 2 (HER2) is a tumor biomarker with significant prognostic and therapeutic implications for invasive ductal breast carcinoma (IDC).

Objective

This study aimed to explore the effectiveness of a multisequence magnetic resonance imaging (MRI)-based machine learning radiomics model in classifying the expression status of HER2, including HER2-positive, HER2-low, and HER2 completely negative (HER2-zero), among patients with IDC.

Methods

A total of 402 female patients with IDC confirmed through surgical pathology were enrolled and subsequently divided into a training group (n = 250, center I) and a validation group (n = 152, center II). Radiomics features were extracted from the preoperative MRI. A simulated annealing algorith

SUBMITTER: Luo HJ 

PROVIDER: S-EPMC11324846 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature