Intratumoral distribution of anisokaryosis in canine cutaneous mast cell tumors
11 affiliations
- 1University of Veterinary Medicine Vienna, Vienna, Austria
- 2Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- 3Mira Vision Microscopy GmbH, Göppingen, Germany
- 4The Schwarzman Animal Medical Center, New York, NY
- 5University of Applied Sciences Upper Austria, Hagenberg, Austria
- 6Johannes Kepler University Linz, Linz, Austria
- 7Ross University School of Veterinary Medicine, Basseterre, Saint Kitts and Nevis
- 8University of Florida, Gainesville, FL
- 9Freie Universität Berlin, Berlin, Germany
- 10Hochschule Flensburg, Flensburg, Germany
- 11Michigan State University, Lansing, MI
Abstract
Automated measurements of anisokaryosis in canine cutaneous mast cell tumors (ccMCTs) have been shown to be predictive of survival, but questions remain regarding the intratumoral distribution of anisokaryosis. Whole-slide images of 96 ccMCTs were analyzed with a deep learning-based segmentation algorithm to quantify anisokaryosis using the standard deviation (SD) of the nuclear area. In 35/96 cases, >5% of the non-overlapping 256 × 256 µm2 regions were hotspots (SD ≥11.5 µm2). Regions selected by 7 pathologists within these 35 cases matched hotspots in 32% of the instances. Outcome analysis (tumor-related death) based on single tumor regions yielded an area under the curve (AUC) of 0.901 for pathologist-selected hotspots, falling between random region selection (AUC: 0.862) and 90th-percentile targeted selection (AUC: 0.956). Whole-slide analysis of the hotspot proportion predicted survival with an AUC of 0.956, with 20% of hotspots as a prognostically meaningful threshold. Whereas pathologists-selected tumor regions are prognostically meaningful for nuclear morphometry, whole-slide analysis may provide additional prognostic information.
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BibTeX
@article{bertram2026intratumoral,
title = {{Intratumoral distribution of anisokaryosis in canine cutaneous mast cell tumors}},
author = {Christof A. Bertram and Frauke Wilm and Eda Parlak and Taryn A. Donovan and Hannah Janout and Pompei Bolfa and Michael J. Dark and Andrea Fuchs-Baumgartinger and Andrea Klang and Robert Klopfleisch and Barbara Richter and Marc Aubreville and Stephan M. Winkler and Matti Kiupel and Alexander Bartel},
journal = {Veterinary Pathology},
year = {2026},
month = jul,
doi = {10.1177/03009858261465447},
url = {https://doi.org/10.1177/03009858261465447},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Intratumoral distribution of anisokaryosis in canine cutaneous mast cell tumors
AU - Christof A. Bertram
AU - Frauke Wilm
AU - Eda Parlak
AU - Taryn A. Donovan
AU - Hannah Janout
AU - Pompei Bolfa
AU - Michael J. Dark
AU - Andrea Fuchs-Baumgartinger
AU - Andrea Klang
AU - Robert Klopfleisch
AU - Barbara Richter
AU - Marc Aubreville
AU - Stephan M. Winkler
AU - Matti Kiupel
AU - Alexander Bartel
PY - 2026
DA - 2026/07/12
JO - Veterinary Pathology
DO - 10.1177/03009858261465447
UR - https://doi.org/10.1177/03009858261465447
AB - Automated measurements of anisokaryosis in canine cutaneous mast cell tumors (ccMCTs) have been shown to be predictive of survival, but questions remain regarding the intratumoral distribution of anisokaryosis. Whole-slide images of 96 ccMCTs were analyzed with a deep learning-based segmentation algorithm to quantify anisokaryosis using the standard deviation (SD) of the nuclear area. In 35/96 cases, >5% of the non-overlapping 256 × 256 µm2 regions were hotspots (SD ≥11.5 µm2). Regions selected by 7 pathologists within these 35 cases matched hotspots in 32% of the instances. Outcome analysis (tumor-related death) based on single tumor regions yielded an area under the curve (AUC) of 0.901 for pathologist-selected hotspots, falling between random region selection (AUC: 0.862) and 90th-percentile targeted selection (AUC: 0.956). Whole-slide analysis of the hotspot proportion predicted survival with an AUC of 0.956, with 20% of hotspots as a prognostically meaningful threshold. Whereas pathologists-selected tumor regions are prognostically meaningful for nuclear morphometry, whole-slide analysis may provide additional prognostic information.
SN - 0300-9858
SN - 1544-2217
ER -