Predicting TERT mutation in glioma patients using MRI-derived radiomics; A systematic review and meta-analysis.

PURPOSE: Telomerase reverse transcriptase (TERT) promoter mutations are associated with malignant progression and poor survival in glioma patients. Non-invasive prediction of TERT mutation status using radiomics may provide valuable molecular insights to guide clinical decision-making. This systematic review and meta-analysis aimed to evaluate the performance of MRI-derived radiomics-based models to predict TERT mutation in glioma patients.

METHODS

A literature search was conducted in four databases: PubMed, Web of Science, Embase, and Scopus. Pooled diagnostic estimates were calculated using a bivariate random-effects model. Heterogeneity was assessed using generalized I2 statistics, and subgroup analyses were performed to investigate the source of heterogeneity. Deeks' funnel plot was used to assess publication bias.

RESULTS

17 studies were included in the analysis. Meta-analysis yielded a pooled sensitivity of 83 % (95 % CI: 76 %-87 %), specificity of 79 % (95 % CI: 71 %-85 %), positive diagnostic likelihood (DLR) of 3.89 (95 % CI: 2.78-5.43), negative DLR of 0.22 (95 % CI: 0.16-0.30), diagnostic odds ratio of 17.56 (95 % CI: 10.57-29.16), and the area under curve of 0.88. Subgroup analysis demonstrated significant differences based on segmentation approach.

CONCLUSION

MRI-derived radiomics models demonstrate good diagnostic performance for the non-invasive prediction of TERT promoter mutations in gliomas. These models may serve as an adjunctive imaging biomarker for preoperative molecular characterization and risk stratification.