ArticleBMC cancer2018
Structural and advanced imaging in predicting MGMT promoter methylation of primary glioblastoma: a region of interest based analysis.
Article in BMC cancer, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 4 of them syntheses that pooled it.
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Who cites it
57 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Diagnostic performance of radiomics using machine learning algorithms to predict MGMT promoter methylation status in glioma patients: a meta-analysis.Diagnostic and interventional radiology (Ankara, Turkey) · 2021Pooled it
- Cerebral and tumoral blood flow in adult gliomas: a systematic review of results from magnetic resonance imaging.The British journal of radiology · 2021Pooled it
- The role of imaging for the management of newly diagnosed glioblastoma in adults: a systematic review and evidence-based clinical practice guideline update.Journal of neuro-oncology · 2020Pooled it
- Clinically Relevant Imaging Features forAJNR. American journal of neuroradiology · 2018Pooled it
- Multiparametric static hybrid ¹⁸F-FET PET/MR for preoperative prediction of TERT promoter mutation in glioblastoma.Neuroradiology · 2026Article
- Association of TERT promoter mutation and MGMT methylation with MRI features and overall survival in glioblastoma, IDH-wildtype.Quantitative imaging in medicine and surgery · 2026Article
- Radiomics in glioblastoma recurrence: advances in prediction, localization, and differentiation from treatment-related effects.Journal of translational medicine · 2026Review
- Exploring the Role of Advanced MRI in Understanding Glioblastoma Biology: A Scoping Review.Cancers · 2026Review
- Article
- Magnetic resonance imaging fractal analysis of O(6)-methylguanine-DNA methyltransferase promoter methylation status in isocitrate dehydrogenase wild-type glioblastoma.Quantitative imaging in medicine and surgery · 2025Article
- Artificial Intelligence in the Diagnosis and Treatment of Brain Gliomas.Biomedicines · 2025Review
- Radiomics and AI-Based Prediction of MGMT Methylation Status in Glioblastoma Using Multiparametric MRI: A Hybrid Feature Weighting Approach.Diagnostics (Basel, Switzerland) · 2025Article
- Explainable, modality-adaptive radiomics for MGMT methylation prediction in high-grade glioma: a decision-curve analysis study.Frontiers in oncology · 2025Article
- Article
- Dynamic susceptibility contrast‑enhanced perfusion magnetic resonance imaging parameters for predictingOncology letters · 2024Article
- AI tool for predicting MGMT methylation in glioblastoma for clinical decision support in resource limited settings.Scientific reports · 2024Article
- Evolution of Molecular Biomarkers and Precision Molecular Therapeutic Strategies in Glioblastoma.Cancers · 2024Review
- Association between dichotomized VASARI feature and overall survival in glioblastoma patients: a single-institution propensity score matching analysis.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Adaptive fine-tuning based transfer learning for the identification of MGMT promoter methylation status.Biomedical physics & engineering express · 2024Article
- Preoperative prediction of MGMT promoter methylation in glioblastoma based on multiregional and multi-sequence MRI radiomics analysis.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
backgroundThe methylation status of oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter has been associated with treatment response in glioblastoma(GBM). Using pre-operative MRI techniques to predict MGMT promoter methylation status remains inconclusive. In this study, we investigated the value of features from structural and advanced imagings in predicting the methylation of MGMT promoter in primary glioblastoma patients.
methodsNinety-two pathologically confirmed primary glioblastoma patients underwent preoperative structural MR imagings and the efficacy of structural image features were qualitatively analyzed using Fisher's exact test. In addition, 77 of the 92 patients underwent additional advanced MRI scans including diffusion-weighted (DWI) and 3-diminsional pseudo-continuous arterial spin labeling (3D pCASL) imaging. Apparent diffusion coefficient (ADC) and relative cerebral blood flow (rCBF) values within the manually drawn region-of-interest (ROI) were calculated and compared using independent sample t test for their efficacies in predicting MGMT promoter methylation. Receiver operating characteristic curve (ROC) analysis was used to investigate the predicting efficacy with the area under the curve (AUC) and cross validations. Multiple-variable logistic regression model was employed to evaluate the predicting performance of multiple variables.
resultsMGMT promoter methylation was associated with tumor location and necrosis (P < 0.05). Significantly increased ADC value (P < 0.001) and decreased rCBF (P < 0.001) were associated with MGMT promoter methylation in primary glioblastoma. The ADC achieved the better predicting efficacy than rCBF (ADC: AUC, 0.860; sensitivity, 81.1%; specificity, 82.5%; vs rCBF: AUC, 0.835; sensitivity, 75.0%; specificity, 78.4%; P = 0.032). The combination of tumor location, necrosis, ADC and rCBF resulted in the highest AUC of 0.914.
conclusionADC and rCBF are promising imaging biomarkers in clinical routine to predict the MGMT promoter methylation in primary glioblastoma patients.
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