The region-of-interest size impacts on Ki67 quantification by computer-assisted image analysis in breast cancer.

Abstract

Therapeutic decision-making in breast cancer depends on histopathologic biomarkers and is influenced by the Ki67 proliferation index. Computer-assisted image analysis (CAIA) promises to improve Ki67 quantification. Several commercial applications have been developed for semiautomated CAIA-based Ki67 quantification, many of which rely on measurements in user-defined regions of interest (ROIs). Because of intratumoral proliferative heterogeneity, definition of the ROI is an important step in the analytical procedure. This study explores the ROI size impacts on Ki67 quantification. Whole-slide sections of 100 breast cancers were immunostained with the anti-Ki67 antibody 30-9 and were analyzed on the iScan Coreo digital pathology platform using a Food and Drug Administration-cleared Ki67 quantification software version v5.3 (Virtuoso; Ventana, Tucson, TX). For each case, the Ki67 labeling index (LI) was determined in multiple ROIs of gradually increasing size centered around a high-proliferation area. The spatial Ki67 decline was modeled with nonlinear regression. Depending on the ROI size, the median Ki67 LI varied between 55% and 15%. The proportion of tumors classified as Ki67 low according to the St Gallen 2013/2015 cutoff increased from 2% to 56%, as the ROI size increased from 50 to 10,000 cells captured. The interrater reliability of conventional Ki67 assessment versus CAIA-based Ki67 quantification was also dependent on the ROI size and varied between slight and almost perfect agreement (Cohen κ = 0.06-0.85). In conclusion, the ROI size is a critically important parameter for semiautomated Ki67 quantification by CAIA. Ki67 LIs determined on platforms like iScan Coreo/Virtuoso require an ROI size adjustment, for which we offer a downloadable data transformation tool.