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Canine Cutaneous Mast Cell Tumors (CCMCTs) present one of the most common cancer subtypes in dogs. The highly variable behavior of CCMCTs makes it hard for pathologists to diagnose and treat the patients. One important prognostic factor that has been identified are mutations in the c-Kit gene, a gene which encodes the receptor tyrosine kinase and can influence cell proliferation. Recent developments in the area of computer vision have shown, that genetic mutations are reflected in the histopathological phenotype and can be accurately classified and detected from hematoxylin and eosin-stained (H&E) whole slide images (WSIs) using deep learning.
In this thesis, we apply multiple instance learning, a type of weakly-supervised learning approach, to a dataset of 457 WSIs of MCTs and show that c-Kit mutations can be detected from the image alone. We also compare different self-supervised pre-training strategies in order to learn better feature representations and improve the accuracy of our mutation prediction. Finally, we use the image regions of high diagnostic importance provided by our deep learning model, and let pathologists examine the difference in histopathological features for each c-Kit mutation.
Numerous prognostic factors are currently assessed histopathologically in biopsies of canine mast cell tumors to evaluate clinical behavior. In addition, PCR analysis of the c-Kit exon 11 mutational status is often performed to evaluate the potential success of a tyrosine kinase inhibitor therapy. This project aimed at training deep learning models (DLMs) to identify the c-Kit-11 mutational status of MCTs solely based on morphology without additional molecular analysis. HE slides of 195 mutated and 173 non-mutated tumors were stained consecutively in two different laboratories and scanned with three different slide scanners. This resulted in six different datasets (stain-scanner variations) of whole slide images. DLMs were trained with single and mixed datasets and their performances was assessed under scanner and staining domain shifts. The DLMs correctly classified HE slides according to their c-Kit 11 mutation status in, on average, 87% of cases for the best-suited stain-scanner variant. A relevant performance drop could be observed when the stain-scanner combination of the training and test dataset differed. Multi-variant datasets improved the average accuracy but did not reach the maximum accuracy of algorithms trained and tested on the same stain-scanner variant. In summary, DLM-assisted morphological examination of MCTs can predict c-Kit-exon 11 mutational status of MCTs with high accuracy. However, the recognition performance is impeded by a change of scanner or staining protocol. Larger data sets with higher numbers of scans originating from different laboratories and scanners may lead to more robust DLMs to identify c-Kit mutations in HE slides.