ArticlePloS one2019
Practical approaches to automated digital image analysis of Ki-67 labeling index in 997 breast carcinomas and causes of discordance with visual assessment.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 27 citations in OpenAlex.
- Optimization of Ki67 digital image analysis in breast cancer by automated tumor area identification.Virchows Archiv : an international journal of pathology · 2026Article
- How artificial intelligence applied to digital pathology could guide treatment personalization in breast cancer.ESMO real world data and digital oncology · 2026Review
- Advancing Ki67 hotspot detection in breast cancer: a comparative analysis of automated digital image analysis algorithms.Histopathology · 2025Article
- The Ki67 dilemma: investigating prognostic cut-offs and reproducibility for automated Ki67 scoring in breast cancer.Breast cancer research and treatment · 2024Article
- Quantitative immunohistochemistry analysis of breast Ki67 based on artificial intelligence.Open life sciences · 2024Article
- Comparative Study of Ki-67 Labeling Index Quantification by Eye-rolling, Manual Count, and Digital Image Analysis; An Approach with Caution.Iranian journal of pathology · 2024Article
- Article
- The Evolution of Ki-67 and Breast Carcinoma: Past Observations, Present Directions, and Future Considerations.Cancers · 2023Review
- Tissue microarrey: a potential cost-effective approach for mismatch repair testing in colorectal cancer.BMC gastroenterology · 2022Article
- Visual and digital assessment of Ki-67 in breast cancer tissue - a comparison of methods.Diagnostic pathology · 2022Article
- Automated Analysis of Proliferating Cells Spatial Organisation Predicts Prognosis in Lung Neuroendocrine Neoplasms.Cancers · 2021Article
- Quality of Histopathological Reporting in Breast Cancer: Results From Four South African Breast Units.JCO global oncology · 2021Article
- Accuracy of Grading in Pancreatic Neuroendocrine Neoplasms and Effect on Survival Estimates: An Institutional Experience.Annals of surgical oncology · 2020Article
- Histopathologist-level quantification of Ki-67 immunoexpression in gastroenteropancreatic neuroendocrine tumors using semiautomated method.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- Neoadjuvant Metformin Added to Systemic Therapy Decreases the Proliferative Capacity of Residual Breast Cancer.Journal of clinical medicine · 2019Article
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Ki-67 labeling index (LI) is an important prognostic factor in breast carcinoma. The Ki-67 LI is traditionally calculated via unaided microscopic estimation; however, inter-observer and intra-observer variability and low reproducibility are problems with this visual assessment (VA) method. For more accurate assessment and better reproducibility with Ki-67 LI, digital image analysis was introduced recently. We used both VA and automated digital image analysis (ADIA) (Ventana Virtuoso image management software) to estimate Ki-67 LI for 997 cases of breast carcinoma, and compared VA and ADIA results. VA and ADIA were highly correlated (intraclass correlation coefficient 0.982, and Spearman's correlation coefficient 0.966, p<0.05). We retrospectively analyzed cases with a greater than 5% difference between VA and ADIA results. The cause of these differences was: (1) tumor heterogeneity (98 cases, 56.0%), (2) VA interpretation error (32 cases, 18.3%), (3) misidentification of tumor cells (26 cases, 14.9%), (4) poor immunostaining or slide quality (16 cases, 9.1%), and (5) Estimation of non-tumor cells (3 cases, 1.7%). There were more discrepancies between VA and ADIA results in the group with a VA value of 10-20% compared to groups with <10% and ≥20%. Although ADIA is more accurate than VA, there are some limitations. Therefore, ADIA findings require confirmation by a pathologist.
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