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The prediction of microsatellite instability (MSI) and microsatellitestability (MSS) is essential in predicting both the treatment response andprognosis of gastrointestinal cancer. In clinical practice, a universal MSItesting is recommended, but the accessibility of such a test is limited. Thus,a more cost-efficient and broadly accessible tool is desired to cover thetraditionally untested patients. In the past few years, deep-learning-basedalgorithms have been proposed to predict MSI directly from haematoxylin andeosin (H&E)-stained whole-slide images (WSIs). Such algorithms can besummarized as (1) patch-level MSI/MSS prediction, and (2) patient-levelaggregation. Compared with the advanced deep learning approaches that have beenemployed for the first stage, only the na\"ive first-order statistics (e.g.,averaging and counting) were employed in the second stage. In this paper, wepropose a simple yet broadly generalizable patient-level MSI aggregation (MAg)method to effectively integrate the precious patch-level information. Briefly,the entire probabilistic distribution in the first stage is modeled ashistogram-based features to be fused as the final outcome with machine learning(e.g., SVM). The proposed MAg method can be easily used in a plug-and-playmanner, which has been evaluated upon five broadly used deep neural networks:ResNet, MobileNetV2, EfficientNet, Dpn and ResNext. From the results, theproposed MAg method consistently improves the accuracy of patient-levelaggregation for two publicly available datasets. It is our hope that theproposed method could potentially leverage the low-cost H&E based MSI detectionmethod. The code of our work has been made publicly available athttps://github.com/Calvin-Pang/MAg.

MAg: a simple learning-based patient-level aggregation method for detecting microsatellite instability from whole-slide images