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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."}]},{"@type":"phaidra:Remark","skos:prefLabel":[{"@language":"eng","@value":"Online Version of Record before inclusion in an issue"}]}],"bf:provisionActivity":[{"@type":"bf:Publication","bf:agent":[{"@type":"schema:Organization","schema:name":[{"@value":"Sage Publications Inc"}]}]}],"dce:rights":[{"@language":"eng","@value":"© The Author(s) 2025"}],"dce:subject":[{"@type":"skos:Concept","skos:prefLabel":[{"@language":"eng","@value":"c-KIT"}]},{"@type":"skos:Concept","skos:prefLabel":[{"@language":"eng","@value":"Digital Pathology"}]},{"@type":"skos:Concept","skos:prefLabel":[{"@language":"eng","@value":"Deep Learning"}]},{"@type":"skos:Concept","skos:prefLabel":[{"@language":"eng","@value":"Dog"}]},{"@type":"skos:Concept","skos:prefLabel":[{"@language":"eng","@value":"Genotype Prediction"}]},{"@type":"skos:Concept","skos:prefLabel":[{"@language":"eng","@value":"Mast Cell Tumor"}]},{"@type":"skos:Concept","skos:prefLabel":[{"@language":"eng","@value":"Morphological Feature"}]},{"@type":"skos:Concept","skos:prefLabel":[{"@language":"eng","@value":"Performance Study"}]}],"dce:title":[{"@type":"bf:Title","bf:mainTitle":[{"@language":"eng","@value":"Artificial intelligence predicts c-KIT exon 11 genotype by phenotype in canine cutaneous mast cell tumors: Can human observers learn it?"}]}],"dcterms:accessRights":[{"@type":"skos:Concept","skos:exactMatch":["https:\/\/pid.phaidra.org\/vocabulary\/QW5R-NG4J"],"skos:prefLabel":[{"@language":"eng","@value":"open access"}]}],"dcterms:issued":["2025"],"dcterms:language":["eng"],"dcterms:type":[{"@type":"skos:Concept","skos:exactMatch":["https:\/\/pid.phaidra.org\/vocabulary\/69ZZ-2KGX"],"skos:prefLabel":[{"@language":"eng","@value":"Text"},{"@language":"deu","@value":"Text"},{"@language":"ita","@value":"Testo"}]}],"ebucore:filename":["puget-et-al-2025-artificial-intelligence-predicts-c-kit-exon-11-genotype-by-phenotype-in-canine-cutaneous-mast-cell.pdf"],"ebucore:hasMimeType":["application\/pdf"],"edm:hasType":[{"@type":"skos:Concept","skos:exactMatch":["https:\/\/pid.phaidra.org\/vocabulary\/VKA6-9XTY"],"skos:prefLabel":[{"@language":"eng","@value":"journal article"},{"@language":"deu","@value":"Wissenschaftlicher Artikel"},{"@language":"ita","@value":"Articolo di rivista"}]}],"edm:rights":["http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"],"oaire:version":[{"@type":"skos:Concept","skos:exactMatch":["https:\/\/pid.phaidra.org\/vocabulary\/PMR8-3C8D"],"skos:prefLabel":[{"@language":"eng","@value":"version of record (published version)"}]}],"rdam:P30004":[{"@type":"ids:doi","@value":"10.1177\/03009858251380284"}],"rdau:P60193":[{"@type":"schema:CreativeWork","dce:title":[{"@type":"bf:Title","bf:mainTitle":[{"@language":"eng","@value":"Veterinary Pathology"}]}],"dcterms:issued":["2025"],"ids:issn":["1544-2217"]}],"role:aut":[{"@type":"schema:Person","schema:familyName":[{"@value":"Puget"}],"schema:givenName":[{"@value":"Chloe"}]},{"@type":"schema:Person","schema:familyName":[{"@value":"Ganz"}],"schema:givenName":[{"@value":"Jonathan"}]},{"@type":"schema:Person","schema:affiliation":[{"@type":"schema:Organization","schema:name":[{"@value":"University of Veterinary Medicine Vienna"}],"skos:exactMatch":["https:\/\/ror.org\/01w6qp003"]}],"schema:familyName":[{"@value":"Bertram"}],"schema:givenName":[{"@value":"Christof A."}],"skos:exactMatch":[{"@type":"ids:orcid","@value":"0000-0002-2402-9997"}]},{"@type":"schema:Person","schema:familyName":[{"@value":"Conrad"}],"schema:givenName":[{"@value":"Thomas"}]},{"@type":"schema:Person","schema:familyName":[{"@value":"Baeblich"}],"schema:givenName":[{"@value":"Malte"}]},{"@type":"schema:Person","schema:familyName":[{"@value":"Voss"}],"schema:givenName":[{"@value":"Anne"}]},{"@type":"schema:Person","schema:familyName":[{"@value":"Landmann"}],"schema:givenName":[{"@value":"Katharina"}]},{"@type":"schema:Person","schema:familyName":[{"@value":"Haake"}],"schema:givenName":[{"@value":"Alexander F. 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