Ki‐67 proliferation index in neuroendocrine tumors: Can augmented reality microscopy with image analysis improve scoring?

BackgroundThe Ki‐67 index is important for grading neuroendocrine tumors (NETs) in cytology. However, different counting methods exist. Recently, augmented reality microscopy (ARM) has enabled real‐time image analysis using glass slides. The objective of the current study was to compare different traditional Ki‐67 scoring methods in cell block material with newer methods such as ARM.MethodsKi‐67 immunostained slides from 50 NETs of varying grades were retrieved (39 from the pancreas and 11 metastases). Methods with which to quantify the Ki‐67 index in up to 3 hot spots included: 1) “eyeball” estimation (EE); 2) printed image manual counting (PIMC); 3) ARM with live image analysis; and 4) image analysis using whole‐slide images (WSI) (field of view [FOV] and the entire slide).ResultsThe Ki‐67 index obtained using the different methods varied. The pairwise kappa results varied from no agreement for image analysis using digital image analysis WSI (FOV) and histology to near‐perfect agreement for ARM and PIMC. Using surgical pathology as the gold standard, the EE method was found to have the highest concordance rate (84.2%), followed by WSI analysis of the entire slide (73.7%) and then both the ARM and PIMC methods (63.2% for both). The PIMC method was the most time‐consuming whereas image analysis using WSI (FOV) was the fastest method followed by ARM.ConclusionsThe Ki‐67 index for NETs in cell block material varied by the method used for scoring, which may affect grade. PIMC was the most time‐consuming method, and EE had the highest concordance rate. Although real‐time automated counting using image analysis demonstrated inaccuracies, ARM streamlined and hastened the task of Ki‐67 quantification in NETs.