TY - JOUR
AU - Christof Bertram
AU - Frauke Wilm
AU - Eda Parlak
AU - Taryn Donovan
AU - Hannah Janout
AU - Pompei Bolfa
AU - Michael Dark
AU - Andrea Fuchs-Baumgartinger
AU - Andrea Klang
AU - Robert Klopfleisch
AU - Barbara Richter
AU - Marc Aubreville
AU - Stephan Winkler
AU - Matti Kiupel
AU - Alexander Bartel
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, \textgreater5\% of the non-overlapping 256 × 256 µm 2 regions were hotspots (SD ≥11.5 µm 2 ). 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.
BT - Veterinary Pathology
DA - 07/2026
DO - 10.1177/03009858261465447
LA - en
N2 - 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, \textgreater5\% of the non-overlapping 256 × 256 µm 2 regions were hotspots (SD ≥11.5 µm 2 ). 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.
PY - 2026
EP - 03009858261465447
T2 - Veterinary Pathology
TI - Intratumoral distribution of anisokaryosis in canine cutaneous mast cell tumors
UR - https://journals.sagepub.com/doi/10.1177/03009858261465447
SN - 0300-9858, 1544-2217
ER -