ORIGINAL RESEARCH article
Front. Oncol.
Sec. Breast Cancer
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1638212
This article is part of the Research TopicAdvancing Breast Cancer Care Through Transparent AI and Federated Learning: Integrating Radiological, Histopathological, and Clinical Data for Diagnosis, Recurrence Prediction, and SurvivorshipView all 10 articles
Predicting Molecular Subtype in Breast Cancer Using Deep Learning on mammography images
Provisionally accepted
- 1Beijing Chaoyang Hospital Affiliated to Capital Medical University, Beijing, China
- 2Beijing University of Posts and Telecommunications, Beijing, China
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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 interpretability via Grad-CAM heatmaps. Results: The model achieved AUCs of 0.759 (Luminal vs. non-Luminal), 0.658 (HER2 status), and 0.668 (TN vs. non-TN) in the independent test set. For multiclass classification, the AUC was 0.649, with superior performance in distinguishing HER2+/HR− (AUC=0.78) and triple-negative (AUC=0.72) subtypes. Attention heatmaps highlighted peritumoral regions as critical discriminative features. Conclusion: The DenseNet121-CBAM model demonstrates promising capability in predicting breast cancer molecular subtypes from mammography, offering a non-invasive alternative to biopsy.
Keywords: breast cancer, Molecular subtypes, Mammography, deep learning, DenseNet121-CBAM
Received: 30 May 2025; Accepted: 27 Aug 2025.
Copyright: © 2025 Luo, Wei, Gu, Zhu and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Chuang Zhu, Beijing University of Posts and Telecommunications, Beijing, China
Feng Xu, Beijing Chaoyang Hospital Affiliated to Capital Medical University, Beijing, China
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