ArticleScientific reports2017
Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
77 citing papers in PubMed, 2 syntheses or guidelines pooled it, 205 citations in OpenAlex.
- 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
- Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Artificial Intelligence-Powered Human Epidermal Growth Factor Receptor 2 Quantification and Clinical Outcomes in Human Epidermal Growth Factor Receptor 2-Positive Biliary Tract Cancer Treated With Trastuzumab Plus Folinic Acid, Fluorouracil, and Oxaliplatin.JCO precision oncology · 2025Trial
- Deep Learning-Enhanced Biomarker Interpretation on Cytology Cell Blocks: Foundations and Emerging Opportunities in Spatial Pathobiology.The American journal of pathology · 2026Review
- Diagnostic Accuracy and Reproducibility of ChatGPT-4o for HER2 Immunohistochemistry Scoring in Equivocal Breast Cancer Cases.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- Digital image analysis improves diagnostic accuracy of HER2-low and HER2-ultralow breast cancer: a step towards personalised medicine.Virchows Archiv : an international journal of pathology · 2026Article
- Diagnosing orthopaedic infection by identifying neutrophils in whole histology slide images with machine learning trained on publicly available datasets.Bone & joint research · 2026Article
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- How artificial intelligence applied to digital pathology could guide treatment personalization in breast cancer.ESMO real world data and digital oncology · 2026Review
- Machine Learning in Biomarker-Driven Precision Oncology: Automated Immunohistochemistry Scoring and Emerging Directions in Genitourinary Cancers.Current oncology (Toronto, Ont.) · 2026Review
- Deep learning based automated HER2 score prediction using immunohistochemistry histopathological images: a dual-center study.Frontiers in physiology · 2026Article
- Artificial Intelligence as an Assistive Tool in Breast Cancer Screening and Diagnosis in the Digital Era.International journal of breast cancer · 2026Article
- Decoding Immunodeficiencies with Artificial Intelligence: A New Era of Precision Medicine.Biomedicines · 2025Review
- Artificial intelligence-assisted HER2 interpretation for breast cancers in a multi-laboratory study.Gland surgery · 2025Article
- Fully automatic HER2 tissue segmentation for interpretable HER2 scoring.Journal of pathology informatics · 2025Article
- Characterisation of HER2-Driven Morphometric Signature in Breast Cancer and Prediction of Risk of Recurrence.Cancer medicine · 2025Article
- Interobserver consistency and diagnostic challenges in HER2-ultralow breast cancer: a multicenter study.ESMO open · 2025Article
- Convergence of evolving artificial intelligence and machine learning techniques in precision oncology.NPJ digital medicine · 2025Article
- Artificial intelligence in cancer pathology: Applications, challenges, and future directions.CytoJournal · 2025Review
- Automated interpretation of PD-L1 CPS based on multi-AI models integration strategy in gastric cancer.Frontiers in immunology · 2025Article
17 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tissue biomarker scoring by pathologists is central to defining the appropriate therapy for patients with cancer. Yet, inter-pathologist variability in the interpretation of ambiguous cases can affect diagnostic accuracy. Modern artificial intelligence methods such as deep learning have the potential to supplement pathologist expertise to ensure constant diagnostic accuracy. We developed a computational approach based on deep learning that automatically scores HER2, a biomarker that defines patient eligibility for anti-HER2 targeted therapies in breast cancer. In a cohort of 71 breast tumour resection samples, automated scoring showed a concordance of 83% with a pathologist. The twelve discordant cases were then independently reviewed, leading to a modification of diagnosis from initial pathologist assessment for eight cases. Diagnostic discordance was found to be largely caused by perceptual differences in assessing HER2 expression due to high HER2 staining heterogeneity. This study provides evidence that deep learning aided diagnosis can facilitate clinical decision making in breast cancer by identifying cases at high risk of misdiagnosis.
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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.