Publication Database

Artificial intelligence predicts c-KIT exon 11 genotype by phenotype in canine cutaneous mast cell tumors:
can human observers learn it? (2025)

Art

Zeitschriftenartikel / wissenschaftlicher Beitrag

Autoren

Puget, Chloé (WE 12)

Ganz, Jonathan

Bertram, Christof A.

Conrad, Thomas (WE 12)

Baeblich, Malte (WE 12)

Voss, Anne (WE 12)

Landmann, Katharina (WE 12)

Haake, Alexander F. H. (WE 12)

Spree, Andreas (WE 12)

Hartung, Svenja

Aeschlimann, Leonore

Soto, Sara

de Brot, Simone

Dettwiler, Martina

Aupperle-Lellbach, Heike

Bolfa, Pompei

Bartel, Alexander

Kiupel, Matti

Breininger, Katharina

Aubreville, Marc

Klopfleisch, Robert (WE 12)

Quelle

Veterinary pathology : an internat. journal of natural and experimental disease in animals

Bandzählung: AOP

Seiten: 3009858251380284

ISSN: 0300-9858

Sprache

Englisch

Verweise

URL (Volltext): https://journals.sagepub.com/doi/10.1177/03009858251380284

DOI: 10.1177/03009858251380284

Pubmed: 41059708

Kontakt

Institut für Tierpathologie

Robert-von-Ostertag-Str. 15
14163 Berlin
+49 30 838 62450
pathologie@vetmed.fu-berlin.de

Abstract / Zusammenfassung

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.