ArticleAnalytical cellular pathology (Amsterdam)2015
Comparison of the manual, semiautomatic, and automatic selection and leveling of hot spots in whole slide images for Ki-67 quantification in meningiomas.
Article in Analytical cellular pathology (Amsterdam), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Time-dependent prognostic value of automated Ki67 assessment and its integration with molecular risk profiling in WHO grade 2 meningioma.Acta neuropathologica communications · 2026Article
- Comparing non-machine learning vs. machine learning methods for Ki67 scoring in gastrointestinal neuroendocrine tumors.Scientific reports · 2025Article
- Comparison of vascular endothelial growth factor/vascular endothelial growth factor receptor 2 expression and its relationship to tumor cell proliferation in canine epithelial and mesenchymal tumors.The Journal of veterinary medical science · 2022Article
- piNET-An Automated Proliferation Index Calculator Framework for Ki67 Breast Cancer Images.Cancers · 2020Article
- Article
- Molecular profiling predicts meningioma recurrence and reveals loss of DREAM complex repression in aggressive tumors.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Evaluation of the proliferation marker Ki-67 in gliomas: Interobserver variability and digital quantification.Diagnostic pathology · 2018Article
- Impact of Her-2 Overexpression on Survival of Patients with Metastatic Breast CancerAsian Pacific journal of cancer prevention : APJCP · 2017Article
- An Advanced Deep Learning Approach for Ki-67 Stained Hotspot Detection and Proliferation Rate Scoring for Prognostic Evaluation of Breast Cancer.Scientific reports · 2017Article
- Content-based analysis of Ki-67 stained meningioma specimens for automatic hot-spot selection.Diagnostic pathology · 2016Article
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Authors and funding
9 authors.
Funding
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Abstract
Background. This paper presents the study concerning hot-spot selection in the assessment of whole slide images of tissue sections collected from meningioma patients. The samples were immunohistochemically stained to determine the Ki-67/MIB-1 proliferation index used for prognosis and treatment planning. Objective. The observer performance was examined by comparing results of the proposed method of automatic hot-spot selection in whole slide images, results of traditional scoring under a microscope, and results of a pathologist's manual hot-spot selection. Methods. The results of scoring the Ki-67 index using optical scoring under a microscope, software for Ki-67 index quantification based on hot spots selected by two pathologists (resp., once and three times), and the same software but on hot spots selected by proposed automatic methods were compared using Kendall's tau-b statistics. Results. Results show intra- and interobserver agreement. The agreement between Ki-67 scoring with manual and automatic hot-spot selection is high, while agreement between Ki-67 index scoring results in whole slide images and traditional microscopic examination is lower. Conclusions. The agreement observed for the three scoring methods shows that automation of area selection is an effective tool in supporting physicians and in increasing the reliability of Ki-67 scoring in meningioma.
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