ArticleIEEE transactions on medical imaging2009
Automated quantitative assessment of HER-2/neu immunohistochemical expression in breast cancer.
Article in IEEE transactions on medical imaging, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis 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
32 citing papers in PubMed, 1 synthesis or guideline pooled it, 111 citations in OpenAlex.
- Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update.Archives of pathology & laboratory medicine · 2014Guideline
- Observer variability in the interpretation of HER2/neu immunohistochemical expression with unaided and computer-aided digital microscopy.Archives of pathology & laboratory medicine · 2011Trial
- 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
- Review of In Situ Hybridization (ISH) Stain Images Using Computational Techniques.Diagnostics (Basel, Switzerland) · 2024Review
- Deep Learning-Based H-Score Quantification of Immunohistochemistry-Stained Images.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2024Article
- High contrast breast cancer biomarker semi-quantification and immunohistochemistry imaging using upconverting nanoparticles.Biomedical optics express · 2024Article
- Computational approach for counting of SISH amplification signals for HER2 status assessment.PeerJ. Computer science · 2024Article
- Immunohistochemical HER2 Recognition and Analysis of Breast Cancer Based on Deep Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Determining HER2 Status by Artificial Intelligence: An Investigation of Primary, Metastatic, and HER2 Low Breast Tumors.Diagnostics (Basel, Switzerland) · 2023Article
- Using Whole Slide Gray Value Map to Predict HER2 Expression and FISH Status in Breast Cancer.Cancers · 2022Article
- Changing Trends in Estrogen Receptors/Progesterone Receptors/Human Epidermal Growth Factor Receptor 2 Prevalence Rates Among Jordanian Patients With Breast Cancer Over the Years.JCO global oncology · 2022Article
- Artificial intelligence applied to breast pathology.Virchows Archiv : an international journal of pathology · 2022Review
- A Deep Learning Quantification Algorithm for HER2 Scoring of Gastric Cancer.Frontiers in neuroscience · 2022Article
- Comparing Deep Learning and Immunohistochemistry in Determining the Site of Origin for Well-Differentiated Neuroendocrine Tumors.Journal of pathology informatics · 2020Article
- A Clinicopathological Study of Various Oral Cancer Diagnostic Techniques.Journal of pharmacy & bioallied sciences · 2017Review
- Quantitative comparison and reproducibility of pathologist scoring and digital image analysis of estrogen receptor β2 immunohistochemistry in prostate cancer.Diagnostic pathology · 2016Article
- Free digital image analysis software helps to resolve equivocal scores in HER2 immunohistochemistry.Virchows Archiv : an international journal of pathology · 2016Article
- Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review.IEEE reviews in biomedical engineering · 2016Review
- Computer-based image analysis in breast pathology.Journal of pathology informatics · 2016Review
- Evaluation of cytokeratin-19 in breast cancer tissue samples: a comparison of automatic and manual evaluations of scanned tissue microarray cylinders.Biomedical engineering online · 2015Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
Abstract
The expression of the HER-2/neu (HER2) gene, a member of the epidermal growth factor receptor family, has been shown to be a valuable prognostic indicator for breast cancer. However, interobserver variability has been reported in the evaluation of HER2 with immunohistochemistry. It has been suggested that automated computer-based evaluation can provide a consistent and objective evaluation of HER2 expression. In this manuscript, we present an automated method for the quantitative assessment of HER2 using digital microscopy. The method processes microscopy images from tissue slides with a multistage algorithm, including steps of color pixel classification, nuclei segmentation, and cell membrane modeling, and extracts quantitative, continuous measures of cell membrane staining intensity and completeness. A minimum cluster distance classifier merges the features to classify the slides into HER2 categories. An evaluation based on agreement analysis with pathologist-derived HER2 scores, showed good agreement with the provided truth. Agreement varied within the different classes with highest agreement (up to 90%) for positive (3+) slides, and lowest agreement (72%-78%) for equivocal (2+) slides which contained ambiguous scoring. The developed automated method has the potential to be used as a computer aid for the immunohistochemical evaluation of HER2 expression with the objective of increasing observer reproducibility.
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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.