ArticleVirchows Archiv : an international journal of pathology2015
A methodology for comprehensive breast cancer Ki67 labeling index with intra-tumor heterogeneity appraisal based on hexagonal tiling of digital image analysis data.
Article in Virchows Archiv : an international journal of pathology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 44 citations in OpenAlex.
- Distinct Tumor-Immune Ecologies in Patients with Lung Cancer Predict Progression and Define a Clinical Biomarker of Therapy Response.Cancer research · 2026Article
- Reproducibility of Ki67 Haralick entropy as a prognostic marker in estrogen receptor-positive HER2-negative breast cancer.American journal of clinical pathology · 2025Article
- Characterization of Breast Cancer Intra-Tumor Heterogeneity Using Artificial Intelligence.Cancers · 2024Article
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- Improving HCC Prognostic Models after Liver Resection by AI-Extracted Tissue Fiber Framework Analytics.Cancers · 2023Article
- Systematically higher Ki67 scores on core biopsy samples compared to corresponding resection specimen in breast cancer: a multi-operator and multi-institutional study.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022Article
- Artificial intelligence applied to breast pathology.Virchows Archiv : an international journal of pathology · 2022Review
- Impact of automated methods for quantitative evaluation of immunostaining: Towards digital pathology.Frontiers in oncology · 2022Article
- Automated Detection of Portal Fields and Central Veins in Whole-Slide Images of Liver Tissue.Journal of pathology informatics · 2022Article
- Machine-Learning-Based Evaluation of Intratumoral Heterogeneity and Tumor-Stroma Interface for Clinical Guidance.The American journal of pathology · 2021Review
- Intratumoral Heterogeneity and Immune Response Indicators to Predict Overall Survival in a Retrospective Study of HER2-Borderline (IHC 2+) Breast Cancer Patients.Frontiers in oncology · 2021Article
- Europe Unites for the Digital Transformation of Pathology: The Role of the New ESDIP.Journal of pathology informatics · 2021Article
- Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer.Scientific reports · 2020Article
- Immuno-Interface Score to Predict Outcome in Colorectal Cancer Independent of Microsatellite Instability Status.Cancers · 2020Article
- Factors influencing agreement of breast cancer luminal molecular subtype by Ki67 labeling index between core needle biopsy and surgical resection specimens.Virchows Archiv : an international journal of pathology · 2020Article
- Independent Prognostic Value of Intratumoral Heterogeneity and Immune Response Features by Automated Digital Immunohistochemistry Analysis in Early Hormone Receptor-Positive Breast Carcinoma.Frontiers in oncology · 2020Article
- Hormonal Receptor Immunochemistry Heterogeneity andFrontiers in oncology · 2020Article
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- Focused scores enable reliable discrimination of small differences in steatosis.Diagnostic pathology · 2018Article
- Digital image analysis of Ki67 proliferation index in breast cancer using virtual dual staining on whole tissue sections: clinical validation and inter-platform agreement.Breast cancer research and treatment · 2018Article
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital image analysis (DIA) enables higher accuracy, reproducibility, and capacity to enumerate cell populations by immunohistochemistry; however, the most unique benefits may be obtained by evaluating the spatial distribution and intra-tissue variance of markers. The proliferative activity of breast cancer tissue, estimated by the Ki67 labeling index (Ki67 LI), is a prognostic and predictive biomarker requiring robust measurement methodologies. We performed DIA on whole-slide images (WSI) of 302 surgically removed Ki67-stained breast cancer specimens; the tumour classifier algorithm was used to automatically detect tumour tissue but was not trained to distinguish between invasive and non-invasive carcinoma cells. The WSI DIA-generated data were subsampled by hexagonal tiling (HexT). Distribution and texture parameters were compared to conventional WSI DIA and pathology report data. Factor analysis of the data set, including total numbers of tumor cells, the Ki67 LI and Ki67 distribution, and texture indicators, extracted 4 factors, identified as entropy, proliferation, bimodality, and cellularity. The factor scores were further utilized in cluster analysis, outlining subcategories of heterogeneous tumors with predominant entropy, bimodality, or both at different levels of proliferative activity. The methodology also allowed the visualization of Ki67 LI heterogeneity in tumors and the automated detection and quantitative evaluation of Ki67 hotspots, based on the upper quintile of the HexT data, conceptualized as the "Pareto hotspot". We conclude that systematic subsampling of DIA-generated data into HexT enables comprehensive Ki67 LI analysis that reflects aspects of intra-tumor heterogeneity and may serve as a methodology to improve digital immunohistochemistry in general.
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