Predicting molecular subtype in breast cancer using deep learning on mammography images.

ABSTRACT:

Objectives

This study aimed to develop and evaluate a deep learning model for predicting molecular subtypes of breast cancer using conventional mammography images, offering a potential alternative to invasive diagnostic techniques.

Methods

A retrospective analysis was conducted on 390 patients with pathologically confirmed invasive breast cancer who underwent preoperative mammography. The proposed DenseNet121-CBAM model, integrating Convolutional Block Attention Modules (CBAM) with DenseNet121, was trained and validated for binary (Luminal vs. non-Luminal, HER2-positive vs. HER2-negative, triple-negative vs. non-TN) and multiclass (Luminal A, Luminal B, HER2+/HR+, HER2+/HR-, TN) classification tasks. Performance metrics included AUC, accuracy, sensitivity, specificity, and i

SUBMITTER: Luo Y 

PROVIDER: S-EPMC12479247 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature