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Updated: Jun 11, 2026

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy

Published on: October 4, 2019

Pre-operative MRI-Based Radiomics for Predicting Telomerase Reverse Transcriptase Promoter Mutation Status in Glioma

Tina Foodeh¹, Mohammad Amir Korani², Mohammad Teymourzadeh³

¹Department of Pathology, Isfahan University of Medical Sciences, Isfahan, Iran.

Neurosurgical Review

|June 9, 2026

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Summary

This summary is machine-generated.

Pre-operative MRI radiomics shows moderate accuracy for predicting TERT promoter mutations in glioma. Combined radiomics-clinical models offer improved performance but require further validation for clinical use.

Area of Science:

Neuro-oncology
Medical imaging
Genetics

Background:

TERT promoter (TERTp) mutations are crucial for glioma prognosis and treatment.
Tissue testing for TERTp status faces limitations like sampling errors and surgical inaccessibility.
Magnetic Resonance Imaging (MRI)-based radiomics presents a non-invasive alternative for TERTp status prediction.

Purpose of the Study:

To assess the diagnostic accuracy of pre-operative MRI radiomics for predicting TERTp mutations in glioma.
To compare the performance of radiomics-only, clinical-only, and combined radiomics-clinical models.
To identify factors influencing model performance and heterogeneity.

Main Methods:

A systematic review and meta-analysis following PRISMA-DTA guidelines.

Keywords:

Glioma Machine learning Magnetic resonance imaging (MRI)Radiogenomics Radiomics Telomerase reverse transcriptase promoter

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Last Updated: Jun 11, 2026

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy

Published on: October 4, 2019

Searched PubMed, Embase, Web of Science, and Scopus up to October 13, 2025.

Included 14 retrospective studies (2,863 patients) evaluating MRI radiomics models against molecular reference standards.

Utilized bivariate random-effects models to pool sensitivity, specificity, and AUC, assessing risk of bias with QUADAS-AI.

Main Results:

MRI-only radiomics models showed moderate discriminative performance (AUC 0.79) with substantial heterogeneity.
Clinical-only models had lower pooled performance (AUC 0.73).
Combined radiomics-clinical models demonstrated numerically higher performance (AUC 0.82), though not definitively superior.
Subgroup analyses indicated classifier type, validation strategy, and software influence performance.

Conclusions:

Pre-operative MRI radiomics offers moderate accuracy for predicting TERTp mutation status in glioma.
Combined models show potential but require further validation due to study limitations (retrospective design, heterogeneity).
Current models should be adjunctive; prospective, multicenter validation with standardized workflows is essential for clinical implementation.