SynthesisDiagnostic and interventional radiology (Ankara, Turkey)2021
Diagnostic performance of radiomics using machine learning algorithms to predict MGMT promoter methylation status in glioma patients: a meta-analysis.
Synthesis in Diagnostic and interventional radiology (Ankara, Turkey), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- The potential of MRI radiomics based on extrapulmonary metastases in predicting EGFR mutations: a systematic review and meta-analysis.Biomedical engineering online · 2025Pooled it
- Exposure-aware multi-omics and artificial intelligence for biomarker discovery and precision prevention in diffuse glioma.Frontiers in immunology · 2026Review
- Review
- Diagnostic Accuracy of Deep Learning Models in Predicting Glioma Molecular Markers: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2025Review
- Virtual Biopsy for the Prediction of MGMT Promoter Methylation in Gliomas: A Comprehensive Review of Radiomics and Deep Learning Approaches Applied to MRI.Diagnostics (Basel, Switzerland) · 2025Review
- Radiomics-Based Prediction of TERT Promotor Mutations in Intracranial High-Grade Meningiomas.Cancers · 2023Article
- Identification of Radiomic Signatures in Brain MRI Sequences T1 and T2 That Differentiate Tumor Regions of Midline Gliomas with H3.3K27M Mutation.Diagnostics (Basel, Switzerland) · 2023Article
- An overview of meta-analyses on radiomics: more evidence is needed to support clinical translation.Insights into imaging · 2023Article
- MGMT gene promoter methylation by pyrosequencing method correlates volumetric response and neurological status in IDH wild-type glioblastomas.Journal of neuro-oncology · 2022Article
- AI-Driven Image Analysis in Central Nervous System Tumors-Traditional Machine Learning, Deep Learning and Hybrid Models.Journal of biotechnology and biomedicine · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeWe 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.
methodsA 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.
resultsA 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.
conclusionThis 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.