ABSTRACT
BACKGROUND: The 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. METHODS: Ki-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). RESULTS: The 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. CONCLUSIONS: The 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.
