ArticleThe journal of pathology. Clinical research2015
Performance of automated scoring of ER, PR, HER2, CK5/6 and EGFR in breast cancer tissue microarrays in the Breast Cancer Association Consortium.
Article in The journal of pathology. Clinical research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 29 citations in OpenAlex.
- Machine Learning in Biomarker-Driven Precision Oncology: Automated Immunohistochemistry Scoring and Emerging Directions in Genitourinary Cancers.Current oncology (Toronto, Ont.) · 2026Review
- Reliability of stromal markers multiplex immunofluorescent staining: pathologist assessment compared to quantitative image analysis.American journal of cancer research · 2026Article
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- Histopathological Images Analysis and Predictive Modeling Implemented in Digital Pathology-Current Affairs and Perspectives.Diagnostics (Basel, Switzerland) · 2023Review
- TMA-Mate: An open-source modular toolkit for constructing tissue microarrays of arbitrary layouts.HardwareX · 2023Article
- Associations of alcohol consumption with breast tissue composition.Breast cancer research : BCR · 2023Article
- Reliability of CD44, CD24, and ALDH1A1 immunohistochemical staining: Pathologist assessment compared to quantitative image analysis.Frontiers in medicine · 2022Article
- Immunohistochemistry scoring of breast tumor tissue microarrays: A comparison study across three software applications.Journal of pathology informatics · 2022Article
- Reliability of a computational platform as a surrogate for manually interpreted immunohistochemical markers in breast tumor tissue microarrays.Cancer epidemiology · 2021Article
- Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association.Applied immunohistochemistry & molecular morphology : AIMM · 2021Article
- Detection of Human Cytomegalovirus Proteins in Paraffin-Embedded Breast Cancer Tissue Specimens-A Novel, Automated Immunohistochemical Staining Protocol.Microorganisms · 2021Article
- Browser-based Data Annotation, Active Learning, and Real-Time Distribution of Artificial Intelligence Models: From Tumor Tissue Microarrays to COVID-19 Radiology.Journal of pathology informatics · 2021Article
- Proliferation Tumour Marker Network (PTM-NET) for the identification of tumour region in Ki67 stained breast cancer whole slide images.Scientific reports · 2019Article
- Combined quantitative measures of ER, PR, HER2, and KI67 provide more prognostic information than categorical combinations in luminal breast cancer.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2019Article
- The use of digital pathology and image analysis in clinical trials.The journal of pathology. Clinical research · 2019Article
- Targeting Tissue Factor for Immunotherapy of Triple-Negative Breast Cancer Using a Second-Generation ICON.Cancer immunology research · 2018Article
- The use of automated Ki67 analysis to predict Oncotype DX risk-of-recurrence categories in early-stage breast cancer.PloS one · 2018Article
- Crowdsourcing scoring of immunohistochemistry images: Evaluating Performance of the Crowd and an Automated Computational Method.Scientific reports · 2017Article
- Crowdsourcing for translational research: analysis of biomarker expression using cancer microarrays.British journal of cancer · 2017Article
- Prognostic value of automated KI67 scoring in breast cancer: a centralised evaluation of 8088 patients from 10 study groups.Breast cancer research : BCR · 2016Article
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Authors and funding
52 authors at 20 institutions in 6 countries.
Funding
Abstract
Breast cancer risk factors and clinical outcomes vary by tumour marker expression. However, individual studies often lack the power required to assess these relationships, and large-scale analyses are limited by the need for high throughput, standardized scoring methods. To address these limitations, we assessed whether automated image analysis of immunohistochemically stained tissue microarrays can permit rapid, standardized scoring of tumour markers from multiple studies. Tissue microarray sections prepared in nine studies containing 20 263 cores from 8267 breast cancers stained for two nuclear (oestrogen receptor, progesterone receptor), two membranous (human epidermal growth factor receptor 2 and epidermal growth factor receptor) and one cytoplasmic (cytokeratin 5/6) marker were scanned as digital images. Automated algorithms were used to score markers in tumour cells using the Ariol system. We compared automated scores against visual reads, and their associations with breast cancer survival. Approximately 65-70% of tissue microarray cores were satisfactory for scoring. Among satisfactory cores, agreement between dichotomous automated and visual scores was highest for oestrogen receptor (Kappa = 0.76), followed by human epidermal growth factor receptor 2 (Kappa = 0.69) and progesterone receptor (Kappa = 0.67). Automated quantitative scores for these markers were associated with hazard ratios for breast cancer mortality in a dose-response manner. Considering visual scores of epidermal growth factor receptor or cytokeratin 5/6 as the reference, automated scoring achieved excellent negative predictive value (96-98%), but yielded many false positives (positive predictive value = 30-32%). For all markers, we observed substantial heterogeneity in automated scoring performance across tissue microarrays. Automated analysis is a potentially useful tool for large-scale, quantitative scoring of immunohistochemically stained tissue microarrays available in consortia. However, continued optimization, rigorous marker-specific quality control measures and standardization of tissue microarray designs, staining and scoring protocols is needed to enhance results.
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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.