Identifying patch-level MSI from histological images of Colorectal Cancer by a Knowledge Distillation Model

Microsatellite instability (MSI) is the result of a defective DNA mismatch repair (MMR) system, and its presence occurs in a variety of cancers. The determination of MSI in colorectal cancer (CRC) will have a better prognosis and management of cancer patients. As the routine MSI identification via molecular testing is expensive, time-consuming, and region-restricted, novel methods to detect MSI are of great interest. In this work, we propose a multi-stage convolutional neural network (CNN) based framework to identify MSI status in colorectal cancer patients from histopathological images. A mislabel-aware module is designed to deal with the uncertainty problem in global-local labelling. An auto-grading model is proposed to discriminate patches by the degree of their histopathological correlation with recognizable MSI status, and subsequently aggregate the weights to make slide-level predictions. Our proposed methodology outperforms the existing models in the classification accuracy, and explicitly sorts out patches with representative features. The research outcome has the potential to assist in the interpretation of histopathology as a surrogate for MSI testing and also in the study of recognizable morphology of MSI-H/MSS tumors. Furthermore, this approach can be extended and applied to other cancer types.