Tumor mutation burden (TMB) is an important biomarker for the prediction of response to anti-PD-1 immunotherapies. Studies have shown that higher level of TMB (TMB-H) is associated with higher response rate to immunotherapies in patients with various types of advanced solid tumors. However, the measurement of TMB depends on whole exome sequencing (WES) which is an expensive assay and not always available in standard clinical oncology settings. In this work, we assess the feasibility of predicting TMB-H based upon hematoxylin and eosin (H&E)-stained histopathology images, which is a routinely conducted assay in clinical oncology. Using an Inception-V3 convolutional neural network (CNN) as a baseline feature extractor, we compare adding a multi-layer perceptron (MLP) and a squeeze-and-excitation (SE) network on top of the baseline CNN. Training from random initialization and tuning with pretrained weights are also compared. Experiments are conducted on the H&E whole-slide images (WSI) of the melanoma dataset of The Cancer Genome Atlas (TCGA). Results from a 4-fold cross-validation show that the highest average area under the receiver operating characteristic curve (AUC) is 0.589, which implies that the prediction of TMB based on H&E WSI for melanoma remains a challenging problem that will warrant further investigations.
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