Computational analysis of pathological image enables interpretable prediction for microsatellite instability
This provides an automated tool for predicting MSI status in clinical practice, offering interpretability to aid pathologists, though it is incremental as it builds on existing deep learning methods for medical imaging.
The study tackled the challenge of distinguishing microsatellite instability (MSI) in tumors by developing interpretable pathological image analysis strategies using Haematoxylin and eosin-stained whole-slide images, achieving decent performance across three cohorts from The Cancer Genome Atlas.
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.