ArticleScientific reports2022
Assessment of Ki-67 proliferation index with deep learning in DCIS (ductal carcinoma in situ).
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- The Ki-67 proliferation index in tertiary lymphoid structures as a predictor of survival outcomes in patients with resectable non-small cell lung cancer.BMC cancer · 2026Article
- Assessing the influence of two deep-learning assistance modes on pathologists in cancer identification.Diagnostic pathology · 2026Article
- Multi scale deep learning quantifies Ki67 index in breast cancer histopathology images.Scientific reports · 2025Article
- Lossy DICOM conversion may affect AI performance.Scientific reports · 2025Article
- High Ki67 expression, HER2 overexpression, and low progesterone receptor levels in high-grade DCIS: significant associations with clinical practice implications.Frontiers in oncology · 2025Article
- Deep learning based analysis of G3BP1 protein expression to predict the prognosis of nasopharyngeal carcinoma.PloS one · 2025Article
- Deep learning-based risk stratification of ductal carcinomaFrontiers in oncology · 2025Article
- Deep learning applications in breast cancer histopathological imaging: diagnosis, treatment, and prognosis.Breast cancer research : BCR · 2024Review
- Transformation from hematoxylin-and-eosin staining to Ki-67 immunohistochemistry digital staining images using deep learning: experimental validation on the labeling index.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Automated AI-based grading of neuroendocrine tumors using Ki-67 proliferation index: comparative evaluation and performance analysis.Medical & biological engineering & computing · 2024Article
- Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence.Cancers · 2024Review
- [The model transferability of AI in digital pathology : Potential and reality].Pathologie (Heidelberg, Germany) · 2024Article
- Low progesterone receptor levels in high-grade DCIS correlate with HER2 upregulation and the presence of invasive components.Frontiers in oncology · 2024Article
- Association Between Ki-67 Proliferative Index and Oncotype-Dx Recurrence Score in Hormone Receptor-Positive, HER2-Negative Early Breast Cancers. A Systematic Review of the Literature.Breast cancer : basic and clinical research · 2024Review
- Breaking Barriers: AI's Influence on Pathology and Oncology in Resource-Scarce Medical Systems.Cancers · 2023Review
- Ki67 proliferation index in medullary thyroid carcinoma: a comparative study of multiple counting methods and validation of image analysis and deep learning platforms.Histopathology · 2023Article
- The slow-paced digital evolution of pathology: lights and shadows from a multifaceted board.Pathologica · 2023Review
- Methods to Assess Proliferation of Stimulated Human Lymphocytes In Vitro: A Narrative Review.Cells · 2023Review
- Clinicopathologic characteristics of neuroendocrine tumors with assessment by digital image analysis for Ki-67 index with a focus on the gastroenteropancreatic tract: a single-center study.International journal of clinical and experimental pathology · 2023Article
- Use of Novel Open-Source Deep Learning Platform for Quantification of Ki-67 in Neuroendocrine Tumors - Analytical Validation.International journal of general medicine · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
The proliferation index (PI) is crucial in histopathologic diagnostics, in particular tumors. It is calculated based on Ki-67 protein expression by immunohistochemistry. PI is routinely evaluated by a visual assessment of the sample by a pathologist. However, this approach is far from ideal due to its poor intra- and interobserver variability and time-consuming. These factors force the community to seek out more precise solutions. Virtual pathology as being increasingly popular in diagnostics, armed with artificial intelligence, may potentially address this issue. The proposed solution calculates the Ki-67 proliferation index by utilizing a deep learning model and fuzzy-set interpretations for hot-spots detection. The obtained region-of-interest is then used to segment relevant cells via classical methods of image processing. The index value is approximated by relating the total surface area occupied by immunopositive cells to the total surface area of relevant cells. The achieved results are compared to the manual calculation of the Ki-67 index made by a domain expert. To increase results reliability, we trained several models in a threefold manner and compared the impact of different hyper-parameters. Our best-proposed method estimates PI with 0.024 mean absolute error, which gives a significant advantage over the current state-of-the-art solution.
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