Evidence map›Paper›PMID 41745992›Full record

ArticleVeterinary sciences2026

Revisiting Ki-67 Assessment in Canine Mast Cell Tumours: From Manual Hotspot to Automated Global Analysis.

Rebeca Scalco, Elena Wasmer, Kathrin Jäger, Sven Rottenberg, Heike Aupperle-Lellbach, Simone de Brot

Abstract read
In one paragraph

Article in Veterinary sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Rebeca ScalcoInstitute of Animal Pathology, University of Bern, 3012 Bern, Switzerland.
Elena WasmerInstitute of Animal Pathology, University of Bern, 3012 Bern, Switzerland.
Kathrin JägerLABOKLIN GmbH & Co. KG, 97688 Bad Kissingen, Germany.
Sven RottenbergInstitute of Animal Pathology, University of Bern, 3012 Bern, Switzerland.ORCID 0000-0003-2044-9844
Heike Aupperle-LellbachLABOKLIN GmbH & Co. KG, 97688 Bad Kissingen, Germany.ORCID 0000-0002-4601-1480
Simone de BrotInstitute of Animal Pathology, University of Bern, 3012 Bern, Switzerland.ORCID 0000-0003-3049-0103

Funding

This research was funded by the Specialization Commission of the University of Bern NA
6 · The paper itself

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.

Indexed as

caninedigital pathologyKi-67mast cell tumour

Identifiers

PMID41745992
PMCPMC12945039

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.