ArticleScientific reports2023
Deep learning to automatically evaluate HER2 gene amplification status from fluorescence in situ hybridization images.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 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
- Artificial intelligence enhances cancer cell selection for HER2 gene amplification assessment in breast carcinoma.Virchows Archiv : an international journal of pathology · 2026Article
- Enhanced Precision of Fluorescence In Situ Hybridization (FISH) Analysis Using Neural Network-Based Nuclear Segmentation for Digital Microscopy Samples.Sensors (Basel, Switzerland) · 2026Article
- BDNet: A Real-Time Biomedical Image Denoising Network with Gradient Information Enhancement Loss.Biosensors · 2026Article
- HER2-Ultralow: Prevalence, Characteristics, and Treatment Choices Among Advanced Breast Cancer Patients With Tumors Initially Scored as IHC 0.The breast journal · 2026Article
- Multimodal AI reading of genetic complexity and residual marrow composition in acute promyelocytic leukemia.Frontiers in immunology · 2026Article
- Enhanced FISH Image Classification via CBAM-PPM-Optimized ResNet50 for Precision Cytogenetic Diagnosis.Sensors (Basel, Switzerland) · 2025Article
- Corr-A-Net: Interpretable Attention-Based Correlated Feature Learning framework for predicting of HER2 Score in Breast Cancer from H&E Images.medRxiv : the preprint server for health sciences · 2025Article
- Enhancing HER2 testing in breast cancer: predicting fluorescence in situ hybridization (FISH) scores from immunohistochemistry images via deep learning.The journal of pathology. Clinical research · 2025Article
- Next-generation sequencing based deep learning model for prediction of HER2 status and response to HER2-targeted neoadjuvant chemotherapy.Journal of cancer research and clinical oncology · 2025Article
- Research advances in the adjunctive diagnosis of acute myeloid leukemia.Frontiers in oncology · 2025Review
- Deep learning method for detecting fluorescence spots in cancer diagnostics via fluorescence in situ hybridization.Scientific reports · 2024Article
- Current status and prospects of artificial intelligence in breast cancer pathology: convolutional neural networks to prospective Vision Transformers.International journal of clinical oncology · 2024Review
- A glimpse into the future: Integrating artificial intelligence for precision HER2-positive breast cancer management.iMetaOmics · 2024Review
- Weakly-supervised deep learning models enable HER2-low prediction from H &E stained slides.Breast cancer research : BCR · 2024Article
- An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation.Sensors (Basel, Switzerland) · 2024Article
- Review
- Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine.Journal of breast cancer · 2023Review
- Deep Learning in Cytopathology: The Potential for Multimodal Synergy of Clinical and Cytological Input Data.Journal of cytologyReview
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14 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Human epidermal growth factor receptor 2 (HER2) gene amplification helps identify breast cancer patients who may respond to targeted anti-HER2 therapy. This study aims to develop an automated method for quantifying HER2 fluorescence in situ hybridization (FISH) signals and improve the working efficiency of pathologists. An Aitrox artificial intelligence (AI) model based on deep learning was constructed, and a comparison between the AI model and traditional manual counting was performed. In total, 918 FISH images from 320 consecutive invasive breast cancers were analysed and automatically classified into 5 groups according to the 2018 ASCO/CAP guidelines. The overall classification accuracy was 85.33% (157/184) with a mean average precision of 0.735. In Group 5, the most common group, the consistency was as high as 95.90% (117/122), while the consistency was low in the other groups due to the limited number of cases. The causes of this inconsistency, including clustered HER2 signals, coarse CEP17 signals and some section quality problems, were analysed. The developed AI model is a reliable tool for evaluating HER2 amplification statuses, especially for breast cancer in Group 5; additional cases from multiple centres could further improve the accuracy achieved for other groups.
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