Novel deep learning-based prediction of HER2 expression in breast cancer using multimodal MRI, nomogram, and decision curve analysis.

ABSTRACT:

Objective

This study aimed to develop a robust, automated framework for predicting HER2 expression in breast cancer by integrating multi-sequence breast MRI with deep learning-based feature extraction and clinical data. The goal was to improve prediction accuracy for HER2 status, which is crucial for guiding targeted therapies.

Materials and methods

A retrospective analysis was conducted on 6,438 breast cancer patients (2006-2024), with 2,400 cases (1,286 HER2-positive, 1,114 HER2-negative) selected based on complete imaging and molecular data. Patients underwent 3T MRI scans with T1, T2, and contrast-enhanced (DCE) sequences. Imaging data from four medical centers were standardized through preprocessing steps, including intensity normalization, registration, and motion corr

SUBMITTER: Qiu S 

PROVIDER: S-EPMC12605381 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature