ArticleBMC cancer2024
Precision HER2: a comprehensive AI system for accurate and consistent evaluation of HER2 expression in invasive breast Cancer.
Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Trastuzumab deruxtecan in HER2-expressing gynecologic cancers from DESTINY-PanTumor02: antitumor activity, safety, and exploratory biomarker analyses.Journal of gynecologic oncology · 2026Trial
- AI-Assisted Scoring Improves Interobserver Agreement in Breast Cancer Biomarker Evaluation.Cancers · 2026Article
- Artificial Intelligence (AI)-Assisted Human Epidermal Growth Factor Receptor 2 (HER2) Scoring in Breast Cancer: Diagnostic Agreement and Understanding Discordance.Laboratory investigation; a journal of technical methods and pathology · 2026Article
- Deep learning-based assessment of HER2-ultralow expression in breast cancer: a multi-center validation study across primary and metastatic lesions.Virchows Archiv : an international journal of pathology · 2026Article
- Diagnostic Accuracy and Reproducibility of ChatGPT-4o for HER2 Immunohistochemistry Scoring in Equivocal Breast Cancer Cases.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- Clinical advances and challenges of antibody-mediated targeted drug delivery in breast cancer therapeutics.Discover oncology · 2026Review
- Validation of fibroblast activation protein and α-smooth muscle actin as prognostic biomarkers in prostate cancer through AI-assisted image analysis of dual-marker IHC.The journal of pathology. Clinical research · 2026Article
- Automatic grading of HER2 immunohistochemical whole-slide images based on a multi-scale hierarchical graph neural network.Frontiers in public health · 2026Article
- Artificial Intelligence-Driven Quantitative HER2 Scoring and Spatial Bystander Effect Modeling for ADC Response Stratification in HER2-Low Breast Cancer.Breast cancer (Dove Medical Press) · 2026Review
- Changes in HER2low and HER2-ultralow status in 47 advanced breast carcinoma core biopsies, matching surgical specimens, and their distant metastases assessed by conventional light microscopy, digital pathology, and artificial intelligence.Breast cancer research and treatment · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
backgroundWith the development of novel anti-HER2 targeted drugs, such as ADCs, it has become increasingly important to accurately interpret HER2 expression in breast cancer. Previous studies have demonstrated high intra-observer and inter-observer variabilities in evaluating HER2 staining by human eyes. There exists a strong requirement to develop artificial intelligence (AI) systems to achieve high-precision HER2 expression scoring for better clinical therapy.
methodsIn the present study, we collected breast cancer tissue samples and stained consecutive sections with anti-Calponin and anti-HER2 antibodies. High-quality digital images were selected from immunohistochemical slides and interpreted as HER2 3+, 2+, 1+, and 0. AI models were trained and assessed using annotated training and testing sets. The AI model was trained to automatically identify ductal carcinoma in situ (DCIS) by Calponin staining and myoepithelial annotation and filter out DCIS components in HER2-stained slides using image-overlapping techniques. Furthermore, we organized two-phase validation studies. In phase one, pathologists interpreted 112 HER2 whole-slide images (WSIs) without AI assistance, whereas in phase two, pathologists read the same slides using the AI system after a washing period of 2 weeks.
resultsOur AI model greatly improved the accuracy of reading (0.902 vs. 0.710). The number of HER2 1 + patients misdiagnosed as HER2 0 was significantly reduced (32/279 vs. 65/279), and they benefitted from ADC drugs. In addition, the AI algorithm improved the intra-group consistency of HER2 readings by pathologists with different years of experience (intra-class correlation coefficient [ICC]: 0.872-0.926 vs. 0.818-0.908), with the improvement most pronounced among junior pathologists (0.885 vs. 0.818).
conclusionsWe proposed a high-precision AI system to identify and filter out DCIS components and automatically evaluate HER2 expression in invasive breast cancer.
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