ArticleBreast cancer research : BCR2025
AI-driven MRI biomarker for triple-class HER2 expression classification in breast cancer: a large-scale multicenter study.
Article in Breast cancer research : BCR, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Lightweight Transformer-based knowledge distillation framework for high-dimensional spatiotemporal radiomics in breast cancer risk prediction.Visual computing for industry, biomedicine, and art · 2026Article
- Attention-Driven CNNs as a Strong Default for HER2 Prediction from DCE-MRI: A Comparison with Transformer Architectures.Bioengineering (Basel, Switzerland) · 2026Article
- Noninvasive molecular subtyping of breast cancer using multimodal ultrasound spatiotemporal transformer.NPJ digital medicine · 2026Article
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14 authors.
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Abstract
backgroundAccurate classification of Human epidermal growth factor receptor 2 (HER2) expression is crucial for guiding treatment in breast cancer, especially with emerging therapies like trastuzumab deruxtecan (T-DXd) for HER2-low patients. Current gold-standard methods relying on invasive biopsy and immunohistochemistry suffer from sampling bias and interobserver variability, highlighting the need for reliable non-invasive alternatives.
methodsWe developed an artificial intelligence framework that integrates a pretrained foundation model with a task-specific classifier to predict HER2 expression categories (HER2-zero, HER2-low, HER2-positive) directly from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The model was trained and validated using multicenter datasets. Model interpretability was assessed through feature visualization using t-SNE and UMAP dimensionality reduction techniques, complemented by SHAP analysis for post-hoc interpretation of critical predictive imaging features.
resultsThe developed model demonstrated robust performance across datasets, achieving micro-average AUCs of 0.821 (95% CI 0.795–0.846) and 0.835 (95% CI 0.797–0.864), and macro-average AUCs of 0.833 (95% CI 0.818–0.847) and 0.857 (95% CI 0.837–0.872) in external validation. Subgroup analysis demonstrated strong discriminative power in distinguishing HER2 categories, particularly HER2-zero and HER2-low cases. Visualization techniques revealed distinct, biologically plausible clustering patterns corresponding to HER2 expression categories.
conclusionsThis study presents a reproducible, non-invasive solution for comprehensive HER2 phenotyping using DCE-MRI, addressing fundamental limitations of biopsy-dependent assessment. Our approach enables accurate identification of HER2-low patients who may benefit from novel therapies like T-DXd. This framework represents a significant advancement in precision oncology, with potential to transform diagnostic workflows and guide targeted therapy selection in breast cancer care.
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