phaidra.vetmeduni.ac.at o:3990 DOI 10.1177/03009858241286806 Artificial intelligence can be trained to predict c-KIT-11 mutational status of canine mast cell tumors from hematoxylin and eosin-stained histological slides Numerous prognostic factors are currently assessed histologically and immunohistochemically in canine mast cell tumors (MCTs) to evaluate clinical behavior. In addition, polymerase chain reaction (PCR) is often performed to detect internal tandem duplication (ITD) mutations in exon 11 of the c-KIT gene (c-KIT-11-ITD) to predict the therapeutic response to tyrosine kinase inhibitors. This project aimed at training deep learning models (DLMs) to identify MCTs with c-KIT-11-ITD solely based on morphology. Hematoxylin and eosin (HE) stained slides of 368 cutaneous, subcutaneous, and mucocutaneous MCTs (195 with ITD and 173 without) were stained consecutively in 2 different laboratories and scanned with 3 different slide scanners. This resulted in 6 data sets (stain-scanner variations representing diagnostic institutions) of whole-slide images. DLMs were trained with single and mixed data sets and their performances were assessed under stain-scanner variations (domain shifts). The DLM correctly classified HE slides according to their c-KIT-11-ITD status in up to 87% of cases with a 0.90 sensitivity and a 0.83 specificity. A relevant performance drop could be observed when the stain-scanner combination of training and test data set differed. Multi-institutional data sets improved the average accuracy but did not reach the maximum accuracy of algorithms trained and tested on the same stain-scanner variant (ie, intra-institutional). In summary, DLM-based morphological examination can predict c-KIT-11-ITD with high accuracy in canine MCTs in HE slides. However, staining protocol and scanner type influence accuracy. Larger data sets of scans from different laboratories and scanners may lead to more robust DLMs to identify c-KIT mutations in HE slides. eng Animals Dogs Dog Diseases Genetics Dog Diseases Pathology Dog Diseases Diagnosis Proto-Oncogene Proteins c-kit Genetics Proto-Oncogene Proteins c-kit Metabolism Eosine Yellowish-(YS) Mutation Hematoxylin Artificial Intelligence Deep Learning Staining and Labeling Veterinary 2025-03-31T09:05:19.913Z LOMv1.0 Author BEGIN:VCARD VERSION:3.0 N:Puget;Chloé; FN:Chloé Puget END:VCARD BEGIN:VCARD VERSION:3.0 N:Ganz;Jonathan; FN:Jonathan Ganz X-ORCID:https://orcid.org/0009-0008-1299-8716 END:VCARD BEGIN:VCARD VERSION:3.0 N:Ostermaier;Julian; FN:Julian Ostermaier END:VCARD BEGIN:VCARD VERSION:3.0 N:Conrad;Thomas; FN:Thomas Conrad END:VCARD BEGIN:VCARD VERSION:3.0 N:Parlak;Eda; FN:Eda Parlak END:VCARD BEGIN:VCARD VERSION:3.0 N:Bertram;Christof A.; FN:Christof A. Bertram X-ORCID:https://orcid.org/0000-0002-2402-9997 END:VCARD BEGIN:VCARD VERSION:3.0 N:Kiupel;Matti; FN:Matti Kiupel END:VCARD BEGIN:VCARD VERSION:3.0 N:Breininger;Katharina; FN:Katharina Breininger END:VCARD BEGIN:VCARD VERSION:3.0 N:Aubreville;Marc; FN:Marc Aubreville X-ORCID:https://orcid.org/0000-0002-5294-5247 END:VCARD BEGIN:VCARD VERSION:3.0 N:Klopfleisch;Robert; FN:Robert Klopfleisch X-ORCID:https://orcid.org/0000-0002-6308-0568 END:VCARD https://w3id.org/kim/hcrt/scheme https://w3id.org/kim/hcrt/text Textdokument Text LOMv1.0 yes https://creativecommons.org/licenses/by-nc/4.0 application/pdf 1184825 https://phaidra.vetmeduni.ac.at/o:3990 https://phaidra.vetmeduni.ac.at/api/object/o:3990/thumbnail LOMv1.0 discipline