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Title (eng)
Artificial intelligence predicts c-KIT exon 11 genotype by phenotype in canine cutaneous mast cell tumors: Can human observers learn it?
Author
Chloe Puget
Author
Jonathan Ganz
Author
Christof A. Bertram
Author
Thomas Conrad
Author
Malte Baeblich
Author
Anne Voss
Author
Katharina Landmann
Author
Alexander F. H. Haake
Author
Andreas Spree
Author
Svenja Hartung
Author
Leonore Aeschlimann
Author
Sara Soto
Author
Simone de Brot
Author
Martina Dettwiler
Author
Heike Aupperle-Lellbach
Author
Pompei Bolfa
Author
Alexander Bartel
Author
Matti Kiupel
Author
Katharina Breininger
Author
Marc Aubreville
Author
Robert Klopfleisch
Abstract (eng)
Canine cutaneous mast cell tumors (ccMCTs) are frequent neoplasms with variable biological behaviors. Internal tandem duplication mutations in c-KIT exon 11 (c-KIT-11-ITD) are associated with poor prognosis but predict therapeutic response to tyrosine kinase inhibitors. In a previous work, deep learning algorithms managed to predict the presence of c-KIT-11-ITD on digitalized hematoxylin and eosin-stained histological slides (whole-slide images, WSIs) in up to 87% of cases, suggesting the existence of morphological features characterizing ccMCTs carrying c-KIT-11-ITD. This 3-stage blinded study aimed to identify morphological features indicative of c-KIT-11-ITD and to evaluate the ability of human observers to learn this task. 17 untrained pathologists first classified 8 WSIs and 200 image patches (highly relevant for algorithmic classification) of ccMCTs as either positive or negative for c-KIT-11-ITD. Second, they self-trained to recognize c-KIT-11-ITD by looking at the same WSIs and patches correctly sorted. Third, pathologists classified 15 new WSIs and 200 new patches according to c-KIT-11-ITD status. In addition, participants reported microscopic features they considered relevant for their decision. Without training, participants correctly classified the c-KIT-11-ITD status of 63%–88% of WSIs and 43%–55% of patches. With self-training, 25%–38% of WSIs and 55%–56% of patches were correctly classified. High cellular pleomorphism, anisokaryosis, and sparse cytoplasmic granulation were commonly suggested as features associated with c-KIT-11-ITD-positive ccMCTs, none of which showed reliable predictivity in a follow-up study. The results indicate that transfer of algorithmic skills to the human observer is difficult. A c-KIT-11-ITD-specific morphological feature remains to be extracted from the artificial intelligence model.
Remark (eng)
Online Version of Record before inclusion in an issue
Keywords (eng)
c-KITDigital PathologyDeep LearningDogGenotype PredictionMast Cell TumorMorphological FeaturePerformance Study
Type (eng)
journal article
Language
[eng]
Persistent identifier
https://phaidra.vetmeduni.ac.at/o:4633
DOI
10.1177/03009858251380284
Is in series
Title (eng)
Veterinary Pathology
ISSN
1544-2217
Issued
2025
Number of pages
11
Publication
Sage Publications Inc
Version type (eng)
version of record (published version)
Date issued
2025
Access rights (eng)
open access
License
CC BY-NC 4.0 International
Rights statement (eng)
© The Author(s) 2025