<?xml version="1.0" encoding="UTF-8"?>
<xml><records><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christof Bertram</style></author><author><style face="normal" font="default" size="100%">Frauke Wilm</style></author><author><style face="normal" font="default" size="100%">Eda Parlak</style></author><author><style face="normal" font="default" size="100%">Taryn Donovan</style></author><author><style face="normal" font="default" size="100%">Hannah Janout</style></author><author><style face="normal" font="default" size="100%">Pompei Bolfa</style></author><author><style face="normal" font="default" size="100%">Michael Dark</style></author><author><style face="normal" font="default" size="100%">Andrea Fuchs-Baumgartinger</style></author><author><style face="normal" font="default" size="100%">Andrea Klang</style></author><author><style face="normal" font="default" size="100%">Robert Klopfleisch</style></author><author><style face="normal" font="default" size="100%">Barbara Richter</style></author><author><style face="normal" font="default" size="100%">Marc Aubreville</style></author><author><style face="normal" font="default" size="100%">Stephan Winkler</style></author><author><style face="normal" font="default" size="100%">Matti Kiupel</style></author><author><style face="normal" font="default" size="100%">Alexander Bartel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Intratumoral distribution of anisokaryosis in canine cutaneous mast cell tumors</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2026</style></year><pub-dates><date><style face="normal" font="default" size="100%">07/2026</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">Veterinary Pathology</style></secondary-title><urls><style face="normal" font="default" size="100%">https://journals.sagepub.com/doi/10.1177/03009858261465447</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.1177/03009858261465447</style></electronic-resource-num><pages><style face="normal" font="default" size="100%">03009858261465447</style></pages><abstract><style face="normal" font="default" size="100%">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.</style></abstract><language><style face="normal" font="default" size="100%">en</style></language></record></records></xml>
