Diagnostic performance of radiomics models for preoperative prediction of microsatellite instability status in endometrial cancer: a systematic review and meta-analysis.

Abstract

Purpose: Microsatellite instability (MSI), caused by defects in mismatch repair (MMR) genes, serves as a critical molecular biomarker with therapeutic implications for endometrial cancer (EC). This study aims to assess the diagnostic performance of radiomics as a non-invasive approach for predicting MSI status in EC.

Methods: A systematic search across PubMed, Scopus, Embase, Web of Science, Cochrane library and Clinical Trials was conducted. Quality assessment was performed using QUADAS-2 and METRICS. Pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) were computed using a bivariate model. Separate meta-analyses for radiomics and combined models were conducted. Subgroup analysis and sensitivity analysis were conducted to find potential sources of heterogeneity. Likelihood ratio scattergram was used to evaluate the clinical applicability.

Results: A total of 9 studies (1650 patients) were included in the systematic review, with seven studies contributing to the meta-analysis of radiomics model and five for combined model. The pooled diagnostic performance of the radiomics model was as follows: sensitivity, 0.66; specificity, 0.89; PLR, 5.48; NLR, 0.43; DOR, 18.56; and AUC, 0.87. For combined model, the pooled sensitivity, specificity, PLR, NLR, DOR, and AUC were 0.58, 0.94, 7.37, 0.50, 16.43, and 0.85, respectively. Subgroup analysis of radiomics models revealed that studies employing non-linear classifiers achieved superior performance compared to those utilizing linear classifiers.

Conclusion: Radiomics showed promise as non-invasive tool for MSI prediction in EC, with potential clinical utility in guiding personalized treatments. However, further studies are required to validate these findings.