Predicting microsatellite instability from whole slide images using texture features

Identifying MSI in whole slide images (WSIs), one of the most widely used diagnostic imaging formats, is of great importance and in demand. In this study we employed color-based texture features to predict MSI on both a tile and sample based level. We found that within cohorts of hematoxylin and eosin (H&E) stained WSIs, texture morphology is able to predict MSI on a tile level with an AUC of up to 0.95 and on a sample level with an AUC of up to 0.98. This runs in contrast to other methods for predicting MSI in H&E WSIs which either utilized artificial intelligence based models, or achieved lower accuracy scores. Our results demonstrate that texture morphology is a significantly notable factor when it comes to identifying MSI in H&E WSIs, and should be used when constructing future models for MSI identification in a clinical setting.