Publikationsdatenbank

Artificial intelligence predicts the c-Kit-11 mutational status of canine cutaneous mast cell tumours through their phenotype in HE-stained histological slides (2024)

Art

Poster

Autoren

Puget, C. (WE 12)

Ganz, J.

Ostermeier, J.

Konrad, T. (WE 12)

Parlak, E.

Bertram, C. A.

Breininger, K.

Kiupel, M.

Aubreville, M.

Klopfleisch, R. (WE 12)

Kongress

European Congress of Veterinary Pathology and Clinical Pathology (ESVP/ECVP/ESVCP/ECVCP)

Lisbon, 30.08. – 02.09.2023

Quelle

Journal of comparative pathology

Bandzählung: 210

Seiten: 65

ISSN: 0021-9975

Sprache

Englisch

Verweise

URL (Volltext): https://www.sciencedirect.com/science/article/pii/S0021997524000835?via%3Dihub

Kontakt

Institut für Tierpathologie

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

Abstract / Zusammenfassung

Introduction: Cutaneous mast cell tumours (MCTs) account for 7–21% of skin tumours in dogs. Numerous prognostic factors are assessed through the histopathological examination of biopsy samples. PCR analysis of the c-Kit exon 11 mutational status is often performed to evaluate the prospect of success of a tyrosine kinase inhibitor therapy. This project aimed to train a deep learning algorithm (DLA) to identify the c-Kit-11 mutational status of MCTs solely based on histological morphology.

Materials and methods: HE-stained slides of 196 c-Kit-11 mutated and 189 non-mutated cutaneous and subcutaneous MCTs were scanned with an Aperio scanner and used as a training database. The sample was then scanned with a 3DHistech Pannoramic scanner to assess the DLA performance with a domain shift.

Results: The DLA correctly classified the HE-stained slides after their c-Kit 11 mutational status in 83% of the cases. Chi2 tests excluded an association between the DLA classification and the tumour grades (Patnaik and Kiupel) as well as the location of the MCTs in the skin. A strong association existed between c-Kit-11 mutated classification and ulcerated epidermis. The DLA reached a classification accuracy of 0.80 on the 3DHistech database.

Conclusions: DLA assisted morphological examination of MCTs can rapidly predict the c-Kit-11 mutational status of MCTs with good precision, potentially saving the time and costs of a PCR analysis. Increasing the number of images as well as including scans originating from different scanners in the training dataset might improve the classification accuracy and robustness of this DLA.