European journal of radiology · 2026
Predicting TERT mutation in glioma patients using MRI-derived radiomics; A systematic review and meta-analysis.
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Moradi Zahra, Mohammadzadeh Saeed, Mehrtabar Ehsan, Ashoobi Mohammad Amin, Mohebbi Alisa, Khalaj Fattaneh, Sotoudeh Houman
Аннотация
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.
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.