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Revisiting Ki-67 Assessment in Canine Mast Cell Tumours: From Manual Hotspot to Automated Global Analysis
by
, and
Rebeca Scalco
1,†, 
Elena Wasmer
1,†,
Kathrin Jäger
2,
Sven Rottenberg
1,3,
Heike Aupperle-Lellbach
2
and
Simone de Brot
1,3,* 1
Institute of Animal Pathology, University of Bern, 3012 Bern, Switzerland
2
LABOKLIN GmbH & Co. KG, 97688 Bad Kissingen, Germany
3
Bern Center for Precision Medicine, University of Bern and Inselspital, 3008 Bern, Switzerland
*
Author to whom correspondence should be addressed.
†
R.S. and E.W. contributed equally to this work and share first authorship.
Vet. Sci. 2026, 13(2), 198; https://doi.org/10.3390/vetsci13020198
Submission received: 30 December 2025
/
Revised: 13 February 2026
/
Accepted: 14 February 2026
/
Published: 18 February 2026
(This article belongs to the Special Issue Focus on Tumours in Pet Animals: 2nd Edition)
Simple Summary
Canine mast cell tumours (MCTs) show highly variable behaviour, making it difficult to predict outcomes. Ki-67 is a protein that marks all proliferating cells and is commonly used as a prognostic indicator. Traditionally, Ki-67 is measured manually in small “hotspot” areas of the tumour, but results can vary between pathologists, limiting reproducibility. This study developed a semi-automated workflow to measure Ki-67 across the whole tumour tissue section using digital pathology and deep-learning tools. A total of 309 tumours were analysed, and the results were compared with manual hotspot counts, tumour grade, and clinical outcome. The workflow enabled rapid and reliable Ki-67 measurement across a range of hundreds of thousands to millions of cells, though defining the tumour region often required some manual correction. Global Ki-67 levels strongly correlated with manual hotspot counts and showed promising potential to predict survival, though larger studies are needed. Importantly, this approach can be applied to Ki-67 and other biomarkers, providing a standardized, reproducible, and comprehensive method for assessing tumour behaviour and improving prognostic and treatment decisions in veterinary oncology.
Abstract
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.
Keywords:
digital pathology; Ki-67; mast cell tumour; canine
