TY - JOUR A1 - Puget, Chloé A1 - Ganz, Jonathan A1 - Bertram, Christof A1 - Conrad, Thomas A1 - Baeblich, Malte A1 - Voss, Anne A1 - Landmann, Katharina A1 - Haake, Alexander F. H. A1 - Spree, Andreas A1 - Hartung, Svenja A1 - Aeschlimann, Leonore A1 - Soto, Sara A1 - de Brot, Simone A1 - Dettwiler, Martina A1 - Aupperle-Lellbach, Heike A1 - Bolfa, Pompei A1 - Bartel, Alexander A1 - Kiupel, Matti A1 - Breininger, Katharina A1 - Aubreville, Marc A1 - Klopfleisch, Robert T1 - Artificial intelligence predicts c-KIT exon 11 genotype by phenotype in canine cutaneous mast cell tumors: Can human observers learn it? JF - Veterinary Pathology N2 - 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. UR - https://doi.org/10.1177/03009858251380284 Y1 - 2025 UR - https://doi.org/10.1177/03009858251380284 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-63841 SN - 1544-2217 VL - 63 IS - 2 SP - 369 EP - 379 PB - Sage CY - London ER -