Determination of Ki-67 indices in neuroendocrine tumours of the gastrointestinal tract: the past, the present, and the future.

Abstract

Ki-67 index (Ki-67i) is integral to the grading of many tumours. There remains considerable variability across pathologists in methods used to determine Ki-67i and in their results. Manual counting (or "eyeballing") is widely used, but digital pathology tools such as web-based image analysis and artificial intelligence-assisted cell detection software have become available in daily pathology practice. This study aims to compare the accuracy and efficiency of manual and two digital methods of Ki-67i determination. H&E and Ki-67 immunohistochemical (IHC) slides/images of 12 gastrointestinal neuroendocrine tumours (GI-NETs) were provided to 8 pathologists to evaluate Ki-67i via manual estimation (ME; the "past"), web-based image analysis using cellular segmentation (AI4Path.ca; the "present"), and software-based image analysis with built-in AI algorithms (QuPath; the "future"). Data collected include Ki67i, time expended, total cells counted, and pathologists' confidence level in the reported result. Deviation of Ki-67i from a gold standard result (GS) was analyzed using multiple linear regression, and results were compared via paired t test. Our results found no statistically significant differences in Ki-67i deviation from GS when comparing ME and AI4P methods for all 12 cases. The QP Ki-67i detection accuracy varied significantly. ME was the method with the least time expenditure. Junior pathologists are less confident in ME. Grading consensus was comparable among all three methods. These findings suggest that while digital pathology can confer increased Ki-67i accuracy in some cases of GI-NETs, higher time expenditure and proper hotspot selection may represent barriers to the adoption of digital pathology methods in the future.