SynthesisMagnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine2026
Diagnostic Accuracy of Artificial Intelligence for Predicting MGMT Promoter Methylation in Glioblastoma Using MR Imaging: A Systematic Review.
Synthesis in Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeGlioblastoma (GBM) is an aggressive brain tumor with poor prognosis. O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation is a critical biomarker for guiding chemotherapy decisions, yet current testing requires invasive tissue sampling. This study aimed to systematically evaluate the diagnostic accuracy of artificial intelligence (AI) models using MRI for non-invasive prediction of MGMT promoter methylation status in GBM.
methodsWe conducted a systematic search of PubMed, ScienceDirect, Scopus, Google Scholar, Cochrane, Web of Science and EMBASE, identifying 480 records. After duplicate removal and screening, 14 studies met inclusion criteria. Data extracted included AI model architecture, MRI sequences, segmentation methods, and diagnostic metrics. A bivariate random-effects model was used to pool sensitivity and specificity. Meta-regression analyses assessed the effect of AI model type on diagnostic performance. Study quality was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool.
resultsThe bivariate random-effects model yielded a pooled sensitivity of 0.536 (95% confidence interval [95% CI]: 0.509-0.563) and a pooled specificity of 0.514 (95% CI: 0.454-0.574), indicating moderate between-study heterogeneity, with an area under the curve of 0.56. The best-performing models included MGMT-net and transformer-based architectures, particularly when using multimodal MRI inputs. Studies employing automated segmentation and single-sequence input (e.g., T2-weighted only) generally demonstrated lower performance. QUADAS-2 assessment indicated a low risk of bias in most domains, with concerns regarding index test thresholds and external validation in some studies.
conclusionAI-based MRI models show moderate-to-high potential for non-invasive MGMT methylation prediction in GBM. However, heterogeneity in study design, imaging protocols, and validation approaches highlights the need for standardized methodologies and robust external validation before clinical adoption.
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