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Objective <p>Microsatellite instability (MSI) is a novel predictive biomarker for chemotherapy and immunotherapy response, as well as prognostic indicator in colorectal cancer (CRC). The current standard for MSI identification is polymerase chain reaction (PCR) testing or the immunohistochemical analysis of tumor biopsy samples. However, tumor heterogeneity and procedure complications pose challenges to these techniques. CT and MRI-based radiomics models offer a promising non-invasive approach for this purpose.</p> Materials and methods <p>A systematic search of PubMed, Embase, Cochrane Library and Scopus was conducted to identify studies evaluating the diagnostic performance of CT and MRI-based radiomics models for detecting MSI status in CRC. Pooled area under the curve (AUC), sensitivity, and specificity were calculated in RStudio using a random-effects model. Forest plots and a summary ROC curve were generated. Heterogeneity was assessed using I² statistics and explored through sensitivity analyses, threshold effect assessment, subgroup analyses and meta-regression.</p> Results <p>17 studies with a total of 6,045 subjects were included in the analysis. All studies extracted radiomic features from CT or MRI images of CRC patients with confirmed MSI status to train machine learning models. The pooled AUC was 0.815 (95% CI: 0.784–0.840) for CT-based studies and 0.900 (95% CI: 0.819–0.943) for MRI-based studies. Significant heterogeneity was identified and addressed through extensive analysis.</p> Conclusion <p>Radiomics models represent a novel and promising tool for predicting MSI status in CRC patients. These findings may serve as a foundation for future studies aimed at developing and validating improved models, ultimately enhancing the diagnosis, treatment, and prognosis of colorectal cancer.</p> Graphical Abstract <p></p>

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Preoperative radiomics models using CT and MRI for microsatellite instability in colorectal cancer: a systematic review and meta-analysis

  • 期刊论文

  • 发表日期：

    2025年5月10日

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  • Gianluca Capello Ingold,
  • João Martins da Fonseca,
  • Sanda Kolenda Zloić,
  • Sarah Verdan Moreira,
  • Karabo Kago Marole,
  • Emma Finnegan,
  • Marcia Harumy Yoshikawa,
  • Silvija Daugėlaitė,
  • Tábata Xavit Souza e Silva,
  • Marco Aurélio Soato Ratti

摘要

Objective

Microsatellite instability (MSI) is a novel predictive biomarker for chemotherapy and immunotherapy response, as well as prognostic indicator in colorectal cancer (CRC). The current standard for MSI identification is polymerase chain reaction (PCR) testing or the immunohistochemical analysis of tumor biopsy samples. However, tumor heterogeneity and procedure complications pose challenges to these techniques. CT and MRI-based radiomics models offer a promising non-invasive approach for this purpose.

Materials and methods

A systematic search of PubMed, Embase, Cochrane Library and Scopus was conducted to identify studies evaluating the diagnostic performance of CT and MRI-based radiomics models for detecting MSI status in CRC. Pooled area under the curve (AUC), sensitivity, and specificity were calculated in RStudio using a random-effects model. Forest plots and a summary ROC curve were generated. Heterogeneity was assessed using I² statistics and explored through sensitivity analyses, threshold effect assessment, subgroup analyses and meta-regression.

Results

17 studies with a total of 6,045 subjects were included in the analysis. All studies extracted radiomic features from CT or MRI images of CRC patients with confirmed MSI status to train machine learning models. The pooled AUC was 0.815 (95% CI: 0.784–0.840) for CT-based studies and 0.900 (95% CI: 0.819–0.943) for MRI-based studies. Significant heterogeneity was identified and addressed through extensive analysis.

Conclusion

Radiomics models represent a novel and promising tool for predicting MSI status in CRC patients. These findings may serve as a foundation for future studies aimed at developing and validating improved models, ultimately enhancing the diagnosis, treatment, and prognosis of colorectal cancer.

Graphical Abstract