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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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 2398-6352 |