  Microsatellite instability (MSI) is associated with several tumor types and
its status has become increasingly vital in guiding patient treatment
decisions. However, in clinical practice, distinguishing MSI from its
counterpart is challenging since the diagnosis of MSI requires additional
genetic or immunohistochemical tests. In this study, interpretable pathological
image analysis strategies are established to help medical experts to
automatically identify MSI. The strategies only require ubiquitous Haematoxylin
and eosin-stained whole-slide images and can achieve decent performance in the
three cohorts collected from The Cancer Genome Atlas. The strategies provide
interpretability in two aspects. On the one hand, the image-level
interpretability is achieved by generating localization heat maps of important
regions based on the deep learning network; on the other hand, the
feature-level interpretability is attained through feature importance and
pathological feature interaction analysis. More interestingly, both from the
image-level and feature-level interpretability, color features and texture
characteristics are shown to contribute the most to the MSI predictions.
Therefore, the classification models under the proposed strategies can not only
serve as an efficient tool for predicting the MSI status of patients, but also
provide more insights to pathologists with clinical understanding.
