Recent Applications of Artificial Intelligence from Histopathologic Image-Based Prediction of Microsatellite Instability in Solid Cancers: A Systematic Review

Simple Summary Although the evaluation of microsatellite instability (MSI) is important for immunotherapy, it is not feasible to test MSI in all cancers due to the additional cost and time. Recently, artificial intelligence (AI)-based MSI prediction models from whole slide images (WSIs) are being developed and have shown promising results. However, these models are still at their elementary level, with limited data for validation. This study aimed to assess the current status of AI applications to WSI-based MSI prediction and to suggest a better study design. The performance of the MSI prediction models were promising, but a small dataset, lack of external validation, and lack of a multiethnic population dataset were the major limitations. Through a combination with high-sensitivity tests such as polymerase chain reaction and immunohistochemical stains, AI-based MSI prediction models with a high performance and appropriate large datasets will reduce the cost and time for MSI testing and will be able to enhance the immunotherapy treatment process in the near future. Cancers with high microsatellite instability (MSI-H) have a better prognosis and respond well to immunotherapy. However, MSI is not tested in all cancers because of the additional costs and time of diagnosis. Therefore, artificial intelligence (AI)-based models have been recently developed to evaluate MSI from whole slide images (WSIs). Here, we aimed to assess the current state of AI application to predict MSI based on WSIs analysis in MSI-related cancers and suggest a better study design for future studies. Studies were searched in online databases and screened by reference type, and only the full texts of eligible studies were reviewed. The included 14 studies were published between 2018 and 2021, and most of the publications were from developed countries. The commonly used dataset is The Cancer Genome Atlas dataset. Colorectal cancer (CRC) was the most common type of cancer studied, followed by endometrial, gastric, and ovarian cancers. The AI models have shown the potential to predict MSI with the highest AUC of 0.93 in the case of CRC. The relatively limited scale of datasets and lack of external validation were the limitations of most studies. Future studies with larger datasets are required to implicate AI models in routine diagnostic practice for MSI prediction.