부산대학교 도서관

Simple Summary: Defects in a DNA repair pathway called mismatch repair (MMR) can lead to cancer, including colorectal cancer (CRC). The detection of mismatch repair deficiency (dMMR) is based on molecular tests, one of which is microsatellite instability (MSI) testing. Detecting tumors with dMMR/MSI is important for the identification of patients with Lynch Syndrome and determining if patients may benefit from immunotherapy. Recently, artificial intelligence has been evaluated as a method to predict MSI/dMMR directly from tissue slides that are available for most cancer patients. We review the data regarding the utility of machine learning for dMMR/MSI classification, including its accuracy and limitations, focusing on CRC. We also provide an overview of previous efforts to predict MSI from tissue slides and background regarding the use of artificial intelligence for image analyses. We summarize recent efforts to use artificial intelligence for the prediction of MSI and discuss the implications for predicting response to immunotherapy. Microsatellite instability (MSI) is a molecular marker of deficient DNA mismatch repair (dMMR) that is found in approximately 15% of colorectal cancer (CRC) patients. Testing all CRC patients for MSI/dMMR is recommended as screening for Lynch Syndrome and, more recently, to determine eligibility for immune checkpoint inhibitors in advanced disease. However, universal testing for MSI/dMMR has not been uniformly implemented because of cost and resource limitations. Artificial intelligence has been used to predict MSI/dMMR directly from hematoxylin and eosin (H&E) stained tissue slides. We review the emerging data regarding the utility of machine learning for MSI classification, focusing on CRC. We also provide the clinician with an introduction to image analysis with machine learning and convolutional neural networks. Machine learning can predict MSI/dMMR with high accuracy in high quality, curated datasets. Accuracy can be significantly decreased when applied to cohorts with different ethnic and/or clinical characteristics, or different tissue preparation protocols. Research is ongoing to determine the optimal machine learning methods for predicting MSI, which will need to be compared to current clinical practices, including next-generation sequencing. Predicting response to immunotherapy remains an unmet need. [ABSTRACT FROM AUTHOR]