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...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Ji Hyun Park [verfasserIn] Eun Young Kim [verfasserIn] Claudio Luchini [verfasserIn] Albino Eccher [verfasserIn] Kalthoum Tizaoui [verfasserIn] Jae Il Shin [verfasserIn] Beom Jin Lim [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	microsatellite instability DNA mismatch repair digital pathology deep learning artificial intelligence

Übergeordnetes Werk:	In: International Journal of Molecular Sciences - MDPI AG, 2003, 23(2022), 5, p 2462
Übergeordnetes Werk:	volume:23 ; year:2022 ; number:5, p 2462

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.3390/ijms23052462

Katalog-ID:	DOAJ018353592

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spelling	10.3390/ijms23052462 doi (DE-627)DOAJ018353592 (DE-599)DOAJ45e6555eb14947d9af4951e64fea3712 DE-627 ger DE-627 rakwb eng QH301-705.5 QD1-999 Ji Hyun Park verfasserin aut Artificial Intelligence for Predicting Microsatellite Instability Based on Tumor Histomorphology: A Systematic Review 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. microsatellite instability DNA mismatch repair digital pathology deep learning artificial intelligence Biology (General) Chemistry Eun Young Kim verfasserin aut Claudio Luchini verfasserin aut Albino Eccher verfasserin aut Kalthoum Tizaoui verfasserin aut Jae Il Shin verfasserin aut Beom Jin Lim verfasserin aut In International Journal of Molecular Sciences MDPI AG, 2003 23(2022), 5, p 2462 (DE-627)316340715 (DE-600)2019364-6 14220067 nnns volume:23 year:2022 number:5, p 2462 https://doi.org/10.3390/ijms23052462 kostenfrei https://doaj.org/article/45e6555eb14947d9af4951e64fea3712 kostenfrei https://www.mdpi.com/1422-0067/23/5/2462 kostenfrei https://doaj.org/toc/1661-6596 Journal toc kostenfrei https://doaj.org/toc/1422-0067 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 5, p 2462
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allfieldsGer	10.3390/ijms23052462 doi (DE-627)DOAJ018353592 (DE-599)DOAJ45e6555eb14947d9af4951e64fea3712 DE-627 ger DE-627 rakwb eng QH301-705.5 QD1-999 Ji Hyun Park verfasserin aut Artificial Intelligence for Predicting Microsatellite Instability Based on Tumor Histomorphology: A Systematic Review 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. microsatellite instability DNA mismatch repair digital pathology deep learning artificial intelligence Biology (General) Chemistry Eun Young Kim verfasserin aut Claudio Luchini verfasserin aut Albino Eccher verfasserin aut Kalthoum Tizaoui verfasserin aut Jae Il Shin verfasserin aut Beom Jin Lim verfasserin aut In International Journal of Molecular Sciences MDPI AG, 2003 23(2022), 5, p 2462 (DE-627)316340715 (DE-600)2019364-6 14220067 nnns volume:23 year:2022 number:5, p 2462 https://doi.org/10.3390/ijms23052462 kostenfrei https://doaj.org/article/45e6555eb14947d9af4951e64fea3712 kostenfrei https://www.mdpi.com/1422-0067/23/5/2462 kostenfrei https://doaj.org/toc/1661-6596 Journal toc kostenfrei https://doaj.org/toc/1422-0067 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 5, p 2462
allfieldsSound	10.3390/ijms23052462 doi (DE-627)DOAJ018353592 (DE-599)DOAJ45e6555eb14947d9af4951e64fea3712 DE-627 ger DE-627 rakwb eng QH301-705.5 QD1-999 Ji Hyun Park verfasserin aut Artificial Intelligence for Predicting Microsatellite Instability Based on Tumor Histomorphology: A Systematic Review 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. microsatellite instability DNA mismatch repair digital pathology deep learning artificial intelligence Biology (General) Chemistry Eun Young Kim verfasserin aut Claudio Luchini verfasserin aut Albino Eccher verfasserin aut Kalthoum Tizaoui verfasserin aut Jae Il Shin verfasserin aut Beom Jin Lim verfasserin aut In International Journal of Molecular Sciences MDPI AG, 2003 23(2022), 5, p 2462 (DE-627)316340715 (DE-600)2019364-6 14220067 nnns volume:23 year:2022 number:5, p 2462 https://doi.org/10.3390/ijms23052462 kostenfrei https://doaj.org/article/45e6555eb14947d9af4951e64fea3712 kostenfrei https://www.mdpi.com/1422-0067/23/5/2462 kostenfrei https://doaj.org/toc/1661-6596 Journal toc kostenfrei https://doaj.org/toc/1422-0067 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 5, p 2462
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callnumber-first	Q - Science
author	Ji Hyun Park
spellingShingle	Ji Hyun Park misc QH301-705.5 misc QD1-999 misc microsatellite instability misc DNA mismatch repair misc digital pathology misc deep learning misc artificial intelligence misc Biology (General) misc Chemistry Artificial Intelligence for Predicting Microsatellite Instability Based on Tumor Histomorphology: A Systematic Review
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topic_title	QH301-705.5 QD1-999 Artificial Intelligence for Predicting Microsatellite Instability Based on Tumor Histomorphology: A Systematic Review microsatellite instability DNA mismatch repair digital pathology deep learning artificial intelligence
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title	Artificial Intelligence for Predicting Microsatellite Instability Based on Tumor Histomorphology: A Systematic Review
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title_sort	artificial intelligence for predicting microsatellite instability based on tumor histomorphology: a systematic review
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title_auth	Artificial Intelligence for Predicting Microsatellite Instability Based on Tumor Histomorphology: A Systematic Review
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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Artificial Intelligence for Predicting Microsatellite Instability Based on Tumor Histomorphology: A Systematic Review
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up_date	2024-07-03T17:26:32.769Z
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