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Predicting 1p/19q Codeletion Status in Glioma Using MRI-Derived Radiomics: A Systematic Review and Meta-Analysis of
Amir Mahmoud Ahmadzadeh1, Nima Broomand Lomer2, Mohammad Amin Ashoobi3
1From the Department of Radiology, School of Medicine (A.M.A.), Mashhad University of Medical Sciences, Mashhad, Iran.
Background:
The 1p/19q codeletion is a key genetic marker in gliomas and plays a crucial role in prognosis and treatment decisions. Traditional methods for detecting this genetic alteration rely on invasive tissue biopsies.
Purpose:
This systematic review and meta-analysis aimed to evaluate the performance of MRI-derived radiomics-based models to predict glioma 1p/19q codeletion status.
Data Sources:
A literature search was conducted in 4 databases: PubMed, Web of Science, EMBASE, and Scopus.
Study Selection:
We selected the studies that assessed the performance of radiomics-based models in determining 1p/19q codeletion status.
Data Analysis:
The Methodological Radiomics Score was used to evaluate study quality. Pooled diagnostic estimates were calculated, and heterogeneity was assessed by using the I 2 statistic. Subgroup and sensitivity analyses were performed to investigate potential sources of heterogeneity. Deek's funnel plot was used to assess publication bias.
Data Synthesis:
Twenty-eight studies met the inclusion criteria for the systematic review. A meta-analysis of 10 studies yielded a pooled sensitivity of 0.82 (95% CI, 0.67-0.91), specificity of 0.80 (95% CI, 0.70-0.88), positive diagnostic likelihood (DLR) of 4.14 (95% CI, 2.62-6.52), negative DLR of 0.23 (95% CI, 0.12-0.43), diagnostic odds ratio of 18.37 (95% CI, 7.36-45.85), and area under the curve of 0.87 (95% CI, 0.84-0.90). Subgroup analysis revealed significant differences based on the country and segmentation method.
Limitations:
Our meta-analysis is limited by small number of studies with external validation cohorts.
Conclusions:
MRI-derived radiomics-based models demonstrated good predictive performance for glioma 1p/19q codeletion status, highlighting their potential as a noninvasive tool for glioma characterization and for aiding in treatment decision-making.
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