@article{1310,
  author = {Christof Bertram and Frauke Wilm and Eda Parlak and Taryn Donovan and Hannah Janout and Pompei Bolfa and Michael Dark and Andrea Fuchs-Baumgartinger and Andrea Klang and Robert Klopfleisch and Barbara Richter and Marc Aubreville and Stephan Winkler and Matti Kiupel and Alexander Bartel},
  title = {Intratumoral distribution of anisokaryosis in canine cutaneous mast cell tumors},
  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, \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.},
  year = {2026},
  booktitle = {Veterinary Pathology},
  journal = {Veterinary Pathology},
  pages = {03009858261465447},
  month = {07/2026},
  issn = {0300-9858, 1544-2217},
  url = {https://journals.sagepub.com/doi/10.1177/03009858261465447},
  doi = {10.1177/03009858261465447},
  language = {en},
}