Canine mast cell tumours (MCTs) show highly variable behaviour, and Ki-67 is an established prognostic indicator. Conventional Ki-67 assessment is manual and restricted to small hotspot areas, limiting reliability. This study presents a semi-automated whole-tumour tissue section (global) Ki-67 analysis workflow, outlines its limitations, and examines correlations with hotspot counts and clinical outcome. A total of 309 canine MCTs were assessed using a deep-learning-assisted quantification with commercial software. Global Ki-67 metrics were correlated with hotspot Ki-67 counts and histomorphologic tumour grades, as supported by clinical follow-up data from 68 dogs. The defined analytic workflow enabled an overall feasible global Ki-67 assessment in canine MCTs. The region-of-interest (ROI) definition required frequent manual adjustments, whereas Ki-67 quantification was fully automated and rapid. Global Ki-67 metrics correlated with manual hotspot counts, with Ki-67-positive cell density on average twice as high in tumour hotspots compared with whole tumour sections, with differences ranging up to 38-fold. Exploratory survival analyses suggested promising predictive power, warranting validation in a robust survival study. With established digital pathology tools, global whole-tumour assessment of Ki-67 and other biomarkers is feasible. It should become the new standard for defining robust prognostic and predictive markers in canine mast cell and other tumours.
Scalco et al. (Thu,) studied this question.