Artificial intelligence can be trained to predict c-KIT-11 mutational status of canine mast cell tumors from hematoxylin and eosin-stained histological slides

Chloé Puget; Jonathan Ganz; Julian Ostermaier; Thomas Conrad; Eda Parlak; Christof A. Bertram; Matti Kiupel; Katharina Breininger; Marc Aubreville; Robert Klopfleisch

doi:10.1177/03009858241286806

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Title (eng)

Artificial intelligence can be trained to predict c-KIT-11 mutational status of canine mast cell tumors from hematoxylin and eosin-stained histological slides

Author

Chloé Puget

Jonathan Ganz

Julian Ostermaier

Thomas Conrad

Eda Parlak

Christof A. Bertram

University of Veterinary Medicine Vienna

Matti Kiupel

Katharina Breininger

Marc Aubreville

Robert Klopfleisch

Abstract (eng)

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.

Keywords (eng)

AnimalsDogsDog Diseases GeneticsDog Diseases PathologyDog Diseases DiagnosisProto-Oncogene Proteins c-kit GeneticsProto-Oncogene Proteins c-kit MetabolismEosine Yellowish-(YS)MutationHematoxylinArtificial IntelligenceDeep LearningStaining and Labeling Veterinary

Type (eng)

journal article

Language

[eng]

Persistent identifier

https://phaidra.vetmeduni.ac.at/o:3990

DOI

10.1177/03009858241286806

Is in series

Title (eng)

Veterinary Pathology

Volume