ArticleNature biomedical engineering2026
Deep-learning-based HER2 status assessment from multimodal breast cancer data predicts neoadjuvant therapy response.
Article in Nature biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Beyond pCR Prediction: Subtype-Specific Artificial Intelligence for Treatment Tailoring in Breast Cancer Neoadjuvant Therapy.Cancers · 2026Review
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- A YOLOv8 deep learning model for detecting labral injury via hip magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2026Article
- Preoperative identification of tumor deposits in rectal cancer using a transformer-based multimodal fusion model: a multicenter retrospective study.BMC medical imaging · 2026Article
- Multimodal radiomics incorporating intratumoral heterogeneity for prognostic assessment of metastatic outcomes in invasive breast cancer.Breast cancer research : BCR · 2026Article
- Noninvasive molecular subtyping of breast cancer using multimodal ultrasound spatiotemporal transformer.NPJ digital medicine · 2026Article
- Deep learning-based prediction of HER2 status from breast diffusion-weighted MRI.Frontiers in oncology · 2026Article
- HER2 Heterogeneity in Breast Cancer: Biological Basis, Clinical Implications, and Therapeutic Strategies.Breast cancer (Dove Medical Press) · 2026Review
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Authors and funding
12 authors.
Funding
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Abstract
Accurate assessment of human epidermal growth factor receptor 2 (HER2) status is crucial for effective breast cancer treatment planning and improved patient outcomes. Traditional needle biopsies, limited in tissue sampling, often lead to inaccurate assessments due to intratumoural heterogeneity. Here, to address this, we introduce the deep-learning-based HER2 multimodal alignment and prediction (MAP) model, which leverages pretreatment multimodal breast cancer images for a more comprehensive reflection of tumour characteristics and provides more accurate HER2 status prediction. We develop patient response MAP models to demonstrate the HER2 prediction performance of our model compared with needle biopsies from patients receiving neoadjuvant therapy. A large-scale multimodal breast cancer dataset from 4 centres, consisting of 14,472 images from 6,991 cases, is adopted in this study, and the results consistently demonstrate the superiority of our HER2 MAP model in predicting patient response. These findings highlight the substantial advantages of our HER2 predictions. Our study provides physicians with a crucial tool for informed clinical decisions and treatment plans, aiming to improve outcomes in patients with breast cancer.
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