ArticleEndocrine pathology2019
Comparison of Monitor-Image and Printout-Image Methods in Ki-67 Scoring of Gastroenteropancreatic Neuroendocrine Tumors.
Article in Endocrine pathology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Digitally derived Ki-67 proliferation index for GastroEnteroPancreatic neuroendocrine neoplasms.Pathology oncology research : POR · 2025Article
- Rare Head and Neck Cancers and Pathological Diagnosis Challenges: A Comprehensive Literature Review.Diagnostics (Basel, Switzerland) · 2024Review
- Digital Image Comparison of Cellular Yield in Bronchial Brushing: Pre- and Post-Biopsy Lavage Cytology.Acta cytologica · 2024Article
- Use of Novel Open-Source Deep Learning Platform for Quantification of Ki-67 in Neuroendocrine Tumors - Analytical Validation.International journal of general medicine · 2023Article
- Update from the 5th Edition of the World Health Organization Classification of Head and Neck Tumors: Overview of the 2022 WHO Classification of Head and Neck Neuroendocrine Neoplasms.Head and neck pathology · 2022Review
- From Counting Mitoses to Ki67 Assessment: Technical Pitfalls in the New WHO Classification of Endocrine and Neuroendocrine Tumors.Endocrine pathology · 2022Article
- Automated assessment of Ki-67 proliferation index in neuroendocrine tumors by deep learning.APMIS : acta pathologica, microbiologica, et immunologica Scandinavica · 2022Article
- Ki-67 Proliferation Index Assessment in Gastroenteropancreatic Neuroendocrine Tumors by Digital Image Analysis With Stringent Case and Hotspot Level Concordance Requirements.American journal of clinical pathology · 2021Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gastroenteropancreatic neuroendocrine tumors (GEP-NET) are classified according to tumor grade. Ki-67 and mitotic count are the two determinants of this classification. Therefore, Ki-67 scoring becomes very important in classifying the patients accurately. Eye-balling, counting of cells through the microscope, automated image analysis systems, and manual counting of printed image are the four major scoring methods in use. The aim of this study is to show the agreement between monitor-image method (MIM) and printout-image method (PIM) of Ki-67 scoring. In our study, 120 GEP-NETs from 85 patients diagnosed between January 2005 and July 2017 were evaluated. Thirty-seven cases with either polypectomy or resection material were selected. Seven different scoring methods using either a monitor-image or a printout-image were applied for Ki-67 scoring. They are as follows: whole-PIM, 1/9-PIM, whole-MIM, 1/4-MIM, 1/6-MIM, 1/9-MIM, and 1/12-MIM. In the comparison of Ki-67 scoring methods, intraclass correlation coefficients ranging from 0.951 to 0.999 were found. The Bland-Altman analysis showed near-perfect agreement between whole-MIM and whole-PIM as well as 1/9-MIM and 1/9-PIM. The level of agreements among the other methods were sufficient too, but there was a relative decrease in the level of agreement as the area of counting becomes smaller. The average application time decreased from 373.7 to 41.7 s gradually as the scoring area becomes smaller. Our study shows that there is a remarkable agreement between the MIM and PIM used in Ki-67 scoring.
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