Diagnostic and Interventional Radiology
Head and Neck Radiology - Review

Diagnostic performance of radiomics using machine learning algorithms to predict MGMT promoter methylation status in glioma patients: a meta-analysis

1.

Department of Radiology, Affiliated Hospital of Southwest Medical University, Sichuan, China

Diagn Interv Radiol 2021; 27: 716-724
DOI: 10.5152/dir.2021.21153
Read: 266 Downloads: 53 Published: 15 October 2021

PURPOSE: We aimed to assess the diagnostic performance of radiomics using machine learning algorithms to predict the methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter in glioma patients.

METHODS: A comprehensive literature search of PubMed, EMBASE, and Web of Science until 27 July 2021 was performed to identify eligible studies. Stata SE 15.0 and Meta-Disc 1.4 were used for data analysis.

RESULTS: A total of fifteen studies with 1663 patients were included: five studies with training and validation cohorts and ten with only training cohorts. The pooled sensitivity and specificity of machine learning for predicting MGMT promoter methylation in gliomas were 85% (95% CI 79%–90%) and 84% (95% CI 78%–88%) in the training cohort (n=15) and 84% (95% CI 70%–92%) and 78% (95% CI 63%–88%) in the validation cohort (n=5). The AUC was 0.91 (95% CI 0.88–0.93) in the training cohort and 0.88 (95% CI 0.85–0.91) in the validation cohort. The meta-regression demonstrated that magnetic resonance imaging sequences were related to heterogeneity. The sensitivity analysis showed that heterogeneity was reduced by excluding one study with the lowest diagnostic performance.

CONCLUSION: This meta-analysis demonstrated that machine learning is a promising, reliable and repeatable candidate method for predicting MGMT promoter methylation status in glioma and showed a higher performance than non-machine learning methods.

You may cite this article as: Huang H, Wang FF, Luo S, Chen G, Tang G. Diagnostic performance of radiomics using machine learning algorithms to predict MGMT promoter methylation status in glioma patients: a meta-analysis. Diagn Interv Radiol 2021; 27:716–724.

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