ArticleCancers2022
Using Whole Slide Gray Value Map to Predict HER2 Expression and FISH Status in Breast Cancer.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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Who cites it
6 citing papers in PubMed.
- Machine learning prediction of HER2-low expression in breast cancers based on hematoxylin-eosin-stained slides.Breast cancer research : BCR · 2025Article
- Fully automatic HER2 tissue segmentation for interpretable HER2 scoring.Journal of pathology informatics · 2025Article
- Systematic review and meta-analysis of artificial intelligence in classifying HER2 status in breast cancer immunohistochemistry.NPJ digital medicine · 2025Article
- Deep learning application in prediction of cancer molecular alterations based on pathological images: a bibliographic analysis via CiteSpace.Journal of cancer research and clinical oncology · 2024Article
- The Performance and Clinical Applicability of HER2 Digital Image Analysis in Breast Cancer: A Systematic Review.Cancers · 2024Review
- AI-Powered Segmentation of Invasive Carcinoma Regions in Breast Cancer Immunohistochemical Whole-Slide Images.Cancers · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Accurate detection of HER2 expression through immunohistochemistry (IHC) is of great clinical significance in the treatment of breast cancer. However, manual interpretation of HER2 is challenging, due to the interobserver variability among pathologists. We sought to explore a deep learning method to predict HER2 expression level and gene status based on a Whole Slide Image (WSI) of the HER2 IHC section. When applied to 228 invasive breast carcinoma of no special type (IBC-NST) DAB-stained slides, our GrayMap+ convolutional neural network (CNN) model accurately classified HER2 IHC level with mean accuracy 0.952 ± 0.029 and predicted HER2 FISH status with mean accuracy 0.921 ± 0.029. Our result also demonstrated strong consistency in HER2 expression score between our system and experienced pathologists (intraclass correlation coefficient (ICC) = 0.903, Cohen's
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Registered trials
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