Küchler, Leonore; Posthaus, Caroline; Jäger, Kathrin; Guscetti, Franco; van der Weyden, Louise; von Bomhard, Wolf; Schmidt, Jarno M; Farra, Dima; Aupperle-Lellbach, Heike; Kehl, Alexandra; Rottenberg, Sven; de Brot, Simone (2023). Artificial Intelligence to Predict the BRAF V595E Mutation in Canine Urinary Bladder Urothelial Carcinomas. Animals, 13(15) MDPI 10.3390/ani13152404
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In dogs, the BRAF mutation (V595E) is common in bladder and prostate cancer and represents a specific diagnostic marker. Recent advantages in artificial intelligence (AI) offer new opportunities in the field of tumour marker detection. While AI histology studies have been conducted in humans to detect BRAF mutation in cancer, comparable studies in animals are lacking. In this study, we used commercially available AI histology software to predict BRAF mutation in whole slide images (WSI) of bladder urothelial carcinomas (UC) stained with haematoxylin and eosin (HE), based on a training (n = 81) and a validation set (n = 96). Among 96 WSI, 57 showed identical PCR and AI-based BRAF predictions, resulting in a sensitivity of 58% and a specificity of 63%. The sensitivity increased substantially to 89% when excluding small or poor-quality tissue sections. Test reliability depended on tumour differentiation (p < 0.01), presence of inflammation (p < 0.01), slide quality (p < 0.02) and sample size (p < 0.02). Based on a small subset of cases with available adjacent non-neoplastic urothelium, AI was able to distinguish malignant from benign epithelium. This is the first study to demonstrate the use of AI histology to predict BRAF mutation status in canine UC. Despite certain limitations, the results highlight the potential of AI in predicting molecular alterations in routine tissue sections.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
05 Veterinary Medicine > Department of Infectious Diseases and Pathobiology (DIP) > Institute of Animal Pathology 05 Veterinary Medicine > Department of Infectious Diseases and Pathobiology (DIP) |
UniBE Contributor: |
Aeschlimann, Leonore, Posthaus, Caroline, Rottenberg, Sven, De Brot, Simone Danielle |
Subjects: |
600 Technology > 630 Agriculture 500 Science > 570 Life sciences; biology 600 Technology > 610 Medicine & health |
ISSN: |
2076-2615 |
Publisher: |
MDPI |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
15 Aug 2023 13:18 |
Last Modified: |
15 Aug 2023 13:27 |
Publisher DOI: |
10.3390/ani13152404 |
PubMed ID: |
37570213 |
Uncontrolled Keywords: |
BRAF PCR artificial intelligence (AI) canine histology urothelial carcinoma (UC) |
BORIS DOI: |
10.48350/185420 |
URI: |
https://boris.unibe.ch/id/eprint/185420 |
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