: 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.

Artificial Intelligence for Predicting Microsatellite Instability Based on Tumor Histomorphology: A Systematic Review / Park, Ji Hyun; Kim, Eun Young; Luchini, Claudio; Eccher, Albino; Tizaoui, Kalthoum; Shin, Jae Il; Lim, Beom Jin. - In: INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES. - ISSN 1422-0067. - 23:5(2022), pp. 1-15. [10.3390/ijms23052462]

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

Eccher, Albino;
2022

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.
2022
23
5
1
15
Artificial Intelligence for Predicting Microsatellite Instability Based on Tumor Histomorphology: A Systematic Review / Park, Ji Hyun; Kim, Eun Young; Luchini, Claudio; Eccher, Albino; Tizaoui, Kalthoum; Shin, Jae Il; Lim, Beom Jin. - In: INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES. - ISSN 1422-0067. - 23:5(2022), pp. 1-15. [10.3390/ijms23052462]
Park, Ji Hyun; Kim, Eun Young; Luchini, Claudio; Eccher, Albino; Tizaoui, Kalthoum; Shin, Jae Il; Lim, Beom Jin
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1317489
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