Systematic review and meta-analysis of deep learning for MSI-H in colorectal cancer whole slide images
Abstract This meta-analysis evaluated diagnostic performance of deep learning (DL) algorithms using whole slide images (WSIs) for detecting microsatellite instability-high (MSI-H) in colorectal cancer (CRC). PubMed, Embase, and Web of Science were searched until January 2025. Nineteen studies compri...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01848-z |
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| _version_ | 1849235651520626688 |
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| author | Huo Li Jing Qin Zhongzhuan Li Rong Ouyang Zhixin Chen Shijiang Huang Shufen Qin Qiliang Huang |
| author_facet | Huo Li Jing Qin Zhongzhuan Li Rong Ouyang Zhixin Chen Shijiang Huang Shufen Qin Qiliang Huang |
| author_sort | Huo Li |
| collection | DOAJ |
| description | Abstract This meta-analysis evaluated diagnostic performance of deep learning (DL) algorithms using whole slide images (WSIs) for detecting microsatellite instability-high (MSI-H) in colorectal cancer (CRC). PubMed, Embase, and Web of Science were searched until January 2025. Nineteen studies comprising 33,383 samples were included. Bivariate random-effects models calculated pooled sensitivity/specificity with 95% CIs. The revised QUADAS-2 tool was used for quality assessment. Pooled patient-based internal validation showed a sensitivity of 0.88 and specificity of 0.86, while external validation revealed higher sensitivity of 0.93 but lower specificity of 0.71. Image-based analysis showed similar accuracy. Meta-regression identified center, reference standard, and tile size as major sources of heterogeneity, with no significant differences observed between internal and external performance. Overall, DL algorithms demonstrate excellent sensitivity in detecting MSI-H; however, their lower specificity in external validation suggests overfitting and highlights the need for algorithm standardization to improve generalizability and clinical utility. |
| format | Article |
| id | doaj-art-d8bad0b039d740ffb3f52fa5ba3e3258 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-d8bad0b039d740ffb3f52fa5ba3e32582025-08-20T04:02:44ZengNature Portfolionpj Digital Medicine2398-63522025-07-018111210.1038/s41746-025-01848-zSystematic review and meta-analysis of deep learning for MSI-H in colorectal cancer whole slide imagesHuo Li0Jing Qin1Zhongzhuan Li2Rong Ouyang3Zhixin Chen4Shijiang Huang5Shufen Qin6Qiliang Huang7Department of Gastroenterology, The Fourth Affiliated Hospital of Guangxi Medical UniversityDepartment of General Medicine, Liuzhou People’s HospitalDepartment of Gastroenterology, The Fourth Affiliated Hospital of Guangxi Medical UniversityDepartment of Gastroenterology, The Fourth Affiliated Hospital of Guangxi Medical UniversityDepartment of Gastroenterology, The Fourth Affiliated Hospital of Guangxi Medical UniversityDepartment of Gastroenterology, The Fourth Affiliated Hospital of Guangxi Medical UniversityDepartment of Gastroenterology, The Fourth Affiliated Hospital of Guangxi Medical UniversityDepartment of Gastroenterology, The Fourth Affiliated Hospital of Guangxi Medical UniversityAbstract This meta-analysis evaluated diagnostic performance of deep learning (DL) algorithms using whole slide images (WSIs) for detecting microsatellite instability-high (MSI-H) in colorectal cancer (CRC). PubMed, Embase, and Web of Science were searched until January 2025. Nineteen studies comprising 33,383 samples were included. Bivariate random-effects models calculated pooled sensitivity/specificity with 95% CIs. The revised QUADAS-2 tool was used for quality assessment. Pooled patient-based internal validation showed a sensitivity of 0.88 and specificity of 0.86, while external validation revealed higher sensitivity of 0.93 but lower specificity of 0.71. Image-based analysis showed similar accuracy. Meta-regression identified center, reference standard, and tile size as major sources of heterogeneity, with no significant differences observed between internal and external performance. Overall, DL algorithms demonstrate excellent sensitivity in detecting MSI-H; however, their lower specificity in external validation suggests overfitting and highlights the need for algorithm standardization to improve generalizability and clinical utility.https://doi.org/10.1038/s41746-025-01848-z |
| spellingShingle | Huo Li Jing Qin Zhongzhuan Li Rong Ouyang Zhixin Chen Shijiang Huang Shufen Qin Qiliang Huang Systematic review and meta-analysis of deep learning for MSI-H in colorectal cancer whole slide images npj Digital Medicine |
| title | Systematic review and meta-analysis of deep learning for MSI-H in colorectal cancer whole slide images |
| title_full | Systematic review and meta-analysis of deep learning for MSI-H in colorectal cancer whole slide images |
| title_fullStr | Systematic review and meta-analysis of deep learning for MSI-H in colorectal cancer whole slide images |
| title_full_unstemmed | Systematic review and meta-analysis of deep learning for MSI-H in colorectal cancer whole slide images |
| title_short | Systematic review and meta-analysis of deep learning for MSI-H in colorectal cancer whole slide images |
| title_sort | systematic review and meta analysis of deep learning for msi h in colorectal cancer whole slide images |
| url | https://doi.org/10.1038/s41746-025-01848-z |
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