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Artificial Intelligence for Predicting Microsatellite Instability Based on Tumor Histomorphology: A Systematic Review
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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8910565/ https://www.ncbi.nlm.nih.gov/pubmed/35269607 http://dx.doi.org/10.3390/ijms23052462 |
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author | Park, Ji Hyun Kim, Eun Young Luchini, Claudio Eccher, Albino Tizaoui, Kalthoum Shin, Jae Il Lim, Beom Jin |
author_facet | Park, Ji Hyun Kim, Eun Young Luchini, Claudio Eccher, Albino Tizaoui, Kalthoum Shin, Jae Il Lim, Beom Jin |
author_sort | Park, Ji Hyun |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8910565 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89105652022-03-11 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 Int J Mol Sci Review 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. MDPI 2022-02-23 /pmc/articles/PMC8910565/ /pubmed/35269607 http://dx.doi.org/10.3390/ijms23052462 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Park, Ji Hyun Kim, Eun Young Luchini, Claudio Eccher, Albino Tizaoui, Kalthoum Shin, Jae Il Lim, Beom Jin Artificial Intelligence for Predicting Microsatellite Instability Based on Tumor Histomorphology: A Systematic Review |
title | Artificial Intelligence for Predicting Microsatellite Instability Based on Tumor Histomorphology: A Systematic Review |
title_full | Artificial Intelligence for Predicting Microsatellite Instability Based on Tumor Histomorphology: A Systematic Review |
title_fullStr | Artificial Intelligence for Predicting Microsatellite Instability Based on Tumor Histomorphology: A Systematic Review |
title_full_unstemmed | Artificial Intelligence for Predicting Microsatellite Instability Based on Tumor Histomorphology: A Systematic Review |
title_short | Artificial Intelligence for Predicting Microsatellite Instability Based on Tumor Histomorphology: A Systematic Review |
title_sort | artificial intelligence for predicting microsatellite instability based on tumor histomorphology: a systematic review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8910565/ https://www.ncbi.nlm.nih.gov/pubmed/35269607 http://dx.doi.org/10.3390/ijms23052462 |
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