ArticleNPJ digital medicine2025
Systematic review and meta-analysis of artificial intelligence in classifying HER2 status in breast cancer immunohistochemistry.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational methods, clinical evidence, and translational readiness for artificial intelligence for HER2 assessment using silver in situ hybridization (SISH).Breast cancer research : BCR · 2026Pooled it
- Review
- 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
- AI-powered and manual assessment of tumor-infiltrating lymphocytes in early HER2-positive breast cancer in NSABP B-41.NPJ breast cancer · 2026Article
- Integrating Artificial Intelligence into Breast Cancer Histopathology: Toward Improved Diagnosis and Prognosis.Cancers · 2026Review
- Spatial omics and AI for clinically actionable cancer biomarkers.PLoS medicine · 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
- HER2 Heterogeneity in Breast Cancer: Biological Basis, Clinical Implications, and Therapeutic Strategies.Breast cancer (Dove Medical Press) · 2026Review
- HER2-Low Breast Cancer at the Interface of Pathology and Technology: Toward Precision Management.Biomedicines · 2025Review
- HER2 expression in breast cancer: evidence gaps and challenges.NPJ precision oncology · 2025Review
- Artificial intelligence in cancer: applications, challenges, and future perspectives.Molecular cancer · 2025Review
- AI-driven MRI biomarker for triple-class HER2 expression classification in breast cancer: a large-scale multicenter study.Breast cancer research : BCR · 2025Article
- HER2-Low Breast Cancer-Current Knowledge and Future Directions.Medicina (Kaunas, Lithuania) · 2025Review
- Real-world analysis of HER2-ultralow in HR+/HER2- metastatic breast cancer: prevalence and first-line chemotherapy outcomes.Therapeutic advances in medical oncology · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The DESTINY-Breast04 trial has recently demonstrated survival benefits of trastuzumab-deruxtecan (T-DXd) in metastatic breast cancer patients with low Human Epidermal Growth Factor Receptor 2 (HER2) expression. Accurate differentiation of HER2 scores has now become crucial. However, visual immunohistochemistry (IHC) scoring is labour-intensive and prone to high interobserver variability, and artificial intelligence (AI) has emerged as a promising tool in diagnostic medicine. We conducted a diagnostic meta-analysis to evaluate AI's performance in classifying HER2 IHC scores, demonstrating high accuracy in predicting T-DXd eligibility, with a pooled sensitivity of 0.97 [95% CI 0.96-0.98] and specificity of 0.82 [95% CI 0.73-0.88]. Meta-regression revealed better performance with deep learning and patch-based analysis, while performance declined in externally validated and those utilising commercially available algorithms. Our findings indicate that AI holds promising potential in accurately identifying HER2-low patients and excels in distinguishing 2+ and 3+ scores.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.