Deep learning for the detection of microsatellite instability from histology images in colorectal cancer: A systematic literature review
Microsatellite instability (MSI) or deficient mismatch repair (dMMR) is a clinically important genetic feature affecting 10–15% of colorectal cancer (CRC) patients. Patients with metastatic MSI/dMMR CRC are eligible for therapy with immune checkpoint inhibitors, making MSI/dMMR the most important im...
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Elsevier
2021-12-01
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| Series: | ImmunoInformatics |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667119021000082 |
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| author | Amelie Echle Narmin Ghaffari Laleh Peter L. Schrammen Nicholas P. West Christian Trautwein Titus J. Brinker Stephen B. Gruber Roman D. Buelow Peter Boor Heike I. Grabsch Philip Quirke Jakob N. Kather |
| author_facet | Amelie Echle Narmin Ghaffari Laleh Peter L. Schrammen Nicholas P. West Christian Trautwein Titus J. Brinker Stephen B. Gruber Roman D. Buelow Peter Boor Heike I. Grabsch Philip Quirke Jakob N. Kather |
| author_sort | Amelie Echle |
| collection | DOAJ |
| description | Microsatellite instability (MSI) or deficient mismatch repair (dMMR) is a clinically important genetic feature affecting 10–15% of colorectal cancer (CRC) patients. Patients with metastatic MSI/dMMR CRC are eligible for therapy with immune checkpoint inhibitors, making MSI/dMMR the most important immuno-oncological biomarker in CRC. Gold standard tests for detection of MSI/dMMR in CRC are based on wet laboratory tests such as immunohistochemistry (IHC) or DNA extraction with subsequent polymerase chain reaction (PCR). However, since 2019, advances in Deep Learning (DL), an Artificial Intelligence (AI) technology, have enabled the prediction of MSI/dMMR directly from digitized routine haematoxylin and eosin (H&E) histopathology slides with high accuracy. In addition to the initial proof-of-concept publication in 2019, twelve subsequent studies have refined, improved, and further validated this approach. At this moment, MSI/dMMR prediction using Deep Learning has become a widely used benchmark task for academic studies in the field of computational pathology. Beyond academic use, this assay has attracted commercial interest from companies with the possibility of approval as a diagnostic device in the near future. In this review, we summarize and quantitatively compare the existing evidence on Deep-Learning-based detection of MSI/dMMR in CRC and discuss the need for further improvement and potential for integration into routine pathological workflows. Ultimately, this DL-based method could facilitate the identification of patients eligible for treatment with immune checkpoint inhibitors by pre-screening or replacement of current methods. |
| format | Article |
| id | doaj-art-ee9727f317444a1ca55efe1dcf4304e9 |
| institution | DOAJ |
| issn | 2667-1190 |
| language | English |
| publishDate | 2021-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | ImmunoInformatics |
| spelling | doaj-art-ee9727f317444a1ca55efe1dcf4304e92025-08-20T02:46:47ZengElsevierImmunoInformatics2667-11902021-12-01310000810.1016/j.immuno.2021.100008Deep learning for the detection of microsatellite instability from histology images in colorectal cancer: A systematic literature reviewAmelie Echle0Narmin Ghaffari Laleh1Peter L. Schrammen2Nicholas P. West3Christian Trautwein4Titus J. Brinker5Stephen B. Gruber6Roman D. Buelow7Peter Boor8Heike I. Grabsch9Philip Quirke10Jakob N. Kather11Department of Medicine III, University Hospital RWTH Aachen, Pauwelsstr. 30, Aachen 52074, GermanyDepartment of Medicine III, University Hospital RWTH Aachen, Pauwelsstr. 30, Aachen 52074, GermanyDepartment of Medicine III, University Hospital RWTH Aachen, Pauwelsstr. 30, Aachen 52074, GermanyPathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United KingdomDepartment of Medicine III, University Hospital RWTH Aachen, Pauwelsstr. 30, Aachen 52074, GermanyDigital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, GermanyCenter for Precision Medicine and Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, United StatesInstitute of Pathology, University Hospital RWTH Aachen, Aachen, GermanyInstitute of Pathology, University Hospital RWTH Aachen, Aachen, GermanyPathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom; Department of Pathology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, NetherlandsPathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United KingdomDepartment of Medicine III, University Hospital RWTH Aachen, Pauwelsstr. 30, Aachen 52074, Germany; Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom; Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany; Corresponding author.Microsatellite instability (MSI) or deficient mismatch repair (dMMR) is a clinically important genetic feature affecting 10–15% of colorectal cancer (CRC) patients. Patients with metastatic MSI/dMMR CRC are eligible for therapy with immune checkpoint inhibitors, making MSI/dMMR the most important immuno-oncological biomarker in CRC. Gold standard tests for detection of MSI/dMMR in CRC are based on wet laboratory tests such as immunohistochemistry (IHC) or DNA extraction with subsequent polymerase chain reaction (PCR). However, since 2019, advances in Deep Learning (DL), an Artificial Intelligence (AI) technology, have enabled the prediction of MSI/dMMR directly from digitized routine haematoxylin and eosin (H&E) histopathology slides with high accuracy. In addition to the initial proof-of-concept publication in 2019, twelve subsequent studies have refined, improved, and further validated this approach. At this moment, MSI/dMMR prediction using Deep Learning has become a widely used benchmark task for academic studies in the field of computational pathology. Beyond academic use, this assay has attracted commercial interest from companies with the possibility of approval as a diagnostic device in the near future. In this review, we summarize and quantitatively compare the existing evidence on Deep-Learning-based detection of MSI/dMMR in CRC and discuss the need for further improvement and potential for integration into routine pathological workflows. Ultimately, this DL-based method could facilitate the identification of patients eligible for treatment with immune checkpoint inhibitors by pre-screening or replacement of current methods.http://www.sciencedirect.com/science/article/pii/S2667119021000082 |
| spellingShingle | Amelie Echle Narmin Ghaffari Laleh Peter L. Schrammen Nicholas P. West Christian Trautwein Titus J. Brinker Stephen B. Gruber Roman D. Buelow Peter Boor Heike I. Grabsch Philip Quirke Jakob N. Kather Deep learning for the detection of microsatellite instability from histology images in colorectal cancer: A systematic literature review ImmunoInformatics |
| title | Deep learning for the detection of microsatellite instability from histology images in colorectal cancer: A systematic literature review |
| title_full | Deep learning for the detection of microsatellite instability from histology images in colorectal cancer: A systematic literature review |
| title_fullStr | Deep learning for the detection of microsatellite instability from histology images in colorectal cancer: A systematic literature review |
| title_full_unstemmed | Deep learning for the detection of microsatellite instability from histology images in colorectal cancer: A systematic literature review |
| title_short | Deep learning for the detection of microsatellite instability from histology images in colorectal cancer: A systematic literature review |
| title_sort | deep learning for the detection of microsatellite instability from histology images in colorectal cancer a systematic literature review |
| url | http://www.sciencedirect.com/science/article/pii/S2667119021000082 |
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