Comparing non-machine learning vs. machine learning methods for Ki67 scoring in gastrointestinal neuroendocrine tumors

Comparing non-machine learning vs. machine learning methods for Ki67 scoring in gastrointestinal neuroendocrine tumors

Abstract The Ki67 score is a crucial prognostic biomarker for neuroendocrine tumors, but its manual assessment is labor-intensive, requiring the counting of 500-2,000 cells in hotspots. Digital image analysis could streamline this process, yet few comprehensive comparisons exist between different to...

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Bibliographic Details
Main Authors: Nazanin Mola, Hrafn Weishaupt, Valentin Krasontovitsch, Erlend Hodneland, Sabine Leh
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Ki67
Neuroendocrine tumor (NET)
Image analysis
Immunohistochemistry
Intra-observer agreement
Machine learning
Online Access:https://doi.org/10.1038/s41598-025-08778-6
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