Artificial intelligence predicts c-KIT exon 11 genotype by phenotype in canine cutaneous mast cell tumors: Can human observers learn it?

Puget C, Ganz J, Bertram CA, Conrad T, Baeblich M, Voss A, Landmann K, Haake AF, Spree A, Hartung S, Aeschlimann L, Soto S, de Brot S, Dettwiler M, Aupperle-Lellbach H, Bolfa P, Bartel A, Kiupel M, Breininger K, Aubreville M, Klopfleisch R (2025)

Publication Type: Journal article

Publication year: 2025

Journal

Veterinary Pathology American College of Veterinary Pathologists

Article Number: 03009858251380284

DOI: 10.1177/03009858251380284

Abstract

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.

Authors with CRIS profile

Katharina Breininger Department Artificial Intelligence in Biomedical Engineering (AIBE)

Involved external institutions

Universität Bern CH Switzerland (CH) Vetscope Pathologie Dettwiler CH Switzerland (CH) LABOKLIN GmbH & Co.KG DE Germany (DE) Ross University School of Veterinary Medicine KN Saint Kitts And Nevis (KN) Freie Universität Berlin DE Germany (DE) Michigan State University US United States (USA) (US) Technische Hochschule Ingolstadt DE Germany (DE) Veterinärmedizinische Universität Wien (Vetmeduni Vienna) / University of Veterinary Medicine, Vienna AT Austria (AT) Fachhochschule Flensburg DE Germany (DE)

How to cite

APA:

Puget, C., Ganz, J., Bertram, C.A., Conrad, T., Baeblich, M., Voss, A.,... Klopfleisch, R. (2025). Artificial intelligence predicts c-KIT exon 11 genotype by phenotype in canine cutaneous mast cell tumors: Can human observers learn it? Veterinary Pathology. https://doi.org/10.1177/03009858251380284

MLA:

Puget, Chloé, et al. "Artificial intelligence predicts c-KIT exon 11 genotype by phenotype in canine cutaneous mast cell tumors: Can human observers learn it?" Veterinary Pathology (2025).

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