Andreas Haghofer Andreas Haghofer Eda Parlak Eda Parlak Alexander Bartel Alexander Bartel Taryn A. Donovan Taryn A. Donovan Charles-Antoine Assenmacher Charles-Antoine Assenmacher Pompei Bolfa Pompei Bolfa Michael J. Dark Michael J. Dark Andrea Fuchs-Baumgartinger Andrea Fuchs-Baumgartinger Andrea Klang Andrea Klang Kathrin Jäger Kathrin Jäger Robert Klopfleisch Robert Klopfleisch Sophie Merz Sophie Merz Barbara Richter Barbara Richter F. Yvonne Schulman F. Yvonne Schulman Hannah Janout Hannah Janout Jonathan Ganz Jonathan Ganz Josef Scharinger Josef Scharinger Marc Aubreville Marc Aubreville Stephan M. Winkler Stephan M. Winkler Matti Kiupel Matti Kiupel Christof A. Bertram Christof A. Bertram Sage 2024 Variation in nuclear size and shape is an important criterion of malignancy for many tumor types; however, categorical estimates by pathologists have poor reproducibility. Measurements of nuclear characteristics can improve reproducibility, but current manual methods are time-consuming. The aim of this study was to explore the limitations of estimates and develop alternative morphometric solutions for canine cutaneous mast cell tumors (ccMCTs). We assessed the following nuclear evaluation methods for accuracy, reproducibility, and prognostic utility: (1) anisokaryosis estimates by 11 pathologists; (2) gold standard manual morphometry of at least 100 nuclei; (3) practicable manual morphometry with stratified sampling of 12 nuclei by 9 pathologists; and (4) automated morphometry using deep learning-based segmentation. The study included 96 ccMCTs with available outcome information. Inter-rater reproducibility of anisokaryosis estimates was low (k = 0.226), whereas it was good (intraclass correlation = 0.654) for practicable morphometry of the standard deviation (SD) of nuclear size. As compared with gold standard manual morphometry (area under the ROC curve [AUC] = 0.839, 95% confidence interval [CI] = 0.701-0.977), the prognostic value (tumor-specific survival) of SDs of nuclear area for practicable manual morphometry and automated morphometry were high with an AUC of 0.868 (95% CI = 0.737-0.991) and 0.943 (95% CI = 0.889-0.996), respectively. This study supports the use of manual morphometry with stratified sampling of 12 nuclei and algorithmic morphometry to overcome the poor reproducibility of estimates. Further studies are needed to validate our findings, determine inter-algorithmic reproducibility and algorithmic robustness, and explore tumor heterogeneity of nuclear features in entire tumor sections. PDFDocument eng 2025-03-31T08:19:56.324975Z 2024 Animals Dogs Dog Diseases Pathology Dog Diseases Diagnosis Prognosis Reproducibility of Results Cell Nucleus Pathology Algorithms Skin Neoplasms Veterinary Skin Neoplasms Pathology Skin Neoplasms Diagnosis Male Female Mastocytosis, Cutaneous Veterinary Mastocytosis, Cutaneous Pathology Mastocytosis, Cutaneous Diagnosis 2890275 b application/pdf http://creativecommons.org/licenses/by/4.0/