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Artificial Intelligence for Predicting Microsatellite Instability Based on Tumor Histomorphology: A Systematic Review

Authors
 Ji Hyun Park  ;  Eun Young Kim  ;  Claudio Luchini  ;  Albino Eccher  ;  Kalthoum Tizaoui  ;  Jae Il Shin  ;  Beom Jin Lim 
Citation
 INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, Vol.23(5) : 2462, 2022-02 
Journal Title
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
ISSN
 1661-6596 
Issue Date
2022-02
MeSH
Artificial Intelligence ; Colorectal Neoplasms* / genetics ; DNA Mismatch Repair / genetics ; Endometrial Neoplasms* / genetics ; Female ; Humans ; Microsatellite Instability
Keywords
DNA mismatch repair ; artificial intelligence ; deep learning ; digital pathology ; microsatellite instability
Abstract
Microsatellite instability (MSI)/defective DNA mismatch repair (dMMR) is receiving more attention as a biomarker for eligibility for immune checkpoint inhibitors in advanced diseases. However, due to high costs and resource limitations, MSI/dMMR testing is not widely performed. Some attempts are in progress to predict MSI/dMMR status through histomorphological features on H&E slides using artificial intelligence (AI) technology. In this study, the potential predictive role of this new methodology was reviewed through a systematic review. Studies up to September 2021 were searched through PubMed and Embase database searches. The design and results of each study were summarized, and the risk of bias for each study was evaluated. For colorectal cancer, AI-based systems showed excellent performance with the highest standard of 0.972; for gastric and endometrial cancers they showed a relatively low but satisfactory performance, with the highest standard of 0.81 and 0.82, respectively. However, analyzing the risk of bias, most studies were evaluated at high-risk. AI-based systems showed a high potential in predicting the MSI/dMMR status of different cancer types, and particularly of colorectal cancers. Therefore, a confirmation test should be required only for the results that are positive in the AI test.
Files in This Item:
T202202337.pdf Download
DOI
10.3390/ijms23052462
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Pediatrics (소아과학교실) > 1. Journal Papers
Yonsei Authors
Shin, Jae Il(신재일) ORCID logo https://orcid.org/0000-0003-2326-1820
Lim, Beom Jin(임범진) ORCID logo https://orcid.org/0000-0003-2856-0133
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/189373
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