Publication: Digital image analysis of Ki67 hotspot detection and index counting in gastroenteropancreatic neuroendocrine neoplasms
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Issued Date
2024-08-01
Resource Type
ISSN
10929134
eISSN
15328198
Scopus ID
2-s2.0-85188967257
Pubmed ID
38547761
Journal Title
Annals of Diagnostic Pathology
Volume
71
Rights Holder(s)
SCOPUS
Bibliographic Citation
Annals of Diagnostic Pathology Vol.71 (2024)
Suggested Citation
Saetiew K., Angkathunyakul N., Hunnangkul S., Pongpaibul A. Digital image analysis of Ki67 hotspot detection and index counting in gastroenteropancreatic neuroendocrine neoplasms. Annals of Diagnostic Pathology Vol.71 (2024). doi:10.1016/j.anndiagpath.2024.152295 Retrieved from: https://hdl.handle.net/20.500.14740/20397
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Abstract
The Ki-67 proliferative index plays a pivotal role in the subclassification of neuroendocrine neoplasm (NEN) according to the WHO Classification of Digestive System Tumors (5th edition), which designates neuroendocrine tumor (NET) grades 1, 2, and 3 for Ki-67 proliferative index of <3 %, 3–20 %, and >20 %, respectively. Proliferative index calculation must be performed in the hotspot, traditionally selected by visual scanning at low-power magnification. Recently, gradient map visualization has emerged as a tool for various purposes, including hotspot selection. This study includes 97 cases of gastrointestinal neuroendocrine neoplasms, with hotspots selected by bare eye and gradient map visualization (GM). Each hotspot was analyzed using three methods: eye estimation (EE), digital image analysis (DIA), and manual counting. Of the NENs studied, 91 % were NETs (26 % for G1, 55 % for G2, and 10 % for G3). Only 9 cases were neuroendocrine carcinoma (NEC). Between two hotspot selection methods, GM resulted in a higher grade in 14.77 % of cases, primarily upgrading from NET G1 to G2. Among the counting methods, DIA demonstrated substantial agreement with manual counting, both for pathologist and resident. Grading by other methods tended to result in a higher grade than MC (26.99 % with EE and 8.52 % with DIA). Given its clinical and statistical significance, this study advocates for the application of GM in hotspot selection to identify higher-grade tumors. Furthermore, DIA provides accurate grading, offering time efficiency over MC.