ArticleMedical physics2016
MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas.
Article in Medical physics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 96 papers, 6 of them syntheses that pooled it.
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Who cites it
96 citing papers in PubMed, 6 syntheses or guidelines pooled it, 163 citations in OpenAlex.
- Quality assessment of the MRI-radiomics studies for MGMT promoter methylation prediction in glioma: a systematic review and meta-analysis.European radiology · 2024Pooled it
- AI-driven estimation of O6 methylguanine-DNA-methyltransferase (MGMT) promoter methylation in glioblastoma patients: a systematic review with bias analysis.Journal of cancer research and clinical oncology · 2024Pooled 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
- 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
- Quantitative magnetic resonance imaging and radiogenomic biomarkers for glioma characterisation: a systematic review.The British journal of radiology · 2018Pooled it
- Clinically Relevant Imaging Features forAJNR. American journal of neuroradiology · 2018Pooled it
- MRI features can predict EGFR expression in lower grade gliomas: A voxel-based radiomic analysis.European radiology · 2018Trial
- Artificial Intelligence for the Preoperative Molecular Characterization of Adult-Type Diffuse High-Grade Gliomas: A Systematic Review and Meta-Analysis.Journal of imaging informatics in medicine · 2026Review
- Radiomics in glioblastoma recurrence: advances in prediction, localization, and differentiation from treatment-related effects.Journal of translational medicine · 2026Review
- Glioblastoma at the crossroads: current understanding and future therapeutic horizons.Signal transduction and targeted therapy · 2025Review
- Machine learning-based risk prediction model for pertussis in children: a multicenter retrospective study.BMC infectious diseases · 2025Article
- 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
- Explainable, modality-adaptive radiomics for MGMT methylation prediction in high-grade glioma: a decision-curve analysis study.Frontiers in oncology · 2025Article
- Evolution of Molecular Biomarkers and Precision Molecular Therapeutic Strategies in Glioblastoma.Cancers · 2024Review
- -New frontiers in domain-inspired radiomics and radiogenomics: increasing role of molecular diagnostics in CNS tumor classification and grading following WHO CNS-5 updates.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Radiomics for predicting MGMT status in cerebral glioblastoma: comparison of different MRI sequences.Journal of radiation research · 2024Article
- Development of A Radiomic Model forInternational journal of molecular sciences · 2023Article
- Biological underpinnings of radiomic magnetic resonance imaging phenotypes for risk stratification in IDH wild-type glioblastoma.Journal of translational medicine · 2023Article
- MRI-Based Deep Learning Method for Classification of IDH Mutation Status.Bioengineering (Basel, Switzerland) · 2023Article
- A Comparison of Three Different Deep Learning-Based Models to Predict the MGMT Promoter Methylation Status in Glioblastoma Using Brain MRI.Journal of digital imaging · 2023Article
36 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
purposeImaging biomarker research focuses on discovering relationships between radiological features and histological findings. In glioblastoma patients, methylation of the O(6)-methylguanine methyltransferase (MGMT) gene promoter is positively correlated with an increased effectiveness of current standard of care. In this paper, the authors investigate texture features as potential imaging biomarkers for capturing the MGMT methylation status of glioblastoma multiforme (GBM) tumors when combined with supervised classification schemes.
methodsA retrospective study of 155 GBM patients with known MGMT methylation status was conducted. Co-occurrence and run length texture features were calculated, and both support vector machines (SVMs) and random forest classifiers were used to predict MGMT methylation status.
resultsThe best classification system (an SVM-based classifier) had a maximum area under the receiver-operating characteristic (ROC) curve of 0.85 (95% CI: 0.78-0.91) using four texture features (correlation, energy, entropy, and local intensity) originating from the T2-weighted images, yielding at the optimal threshold of the ROC curve, a sensitivity of 0.803 and a specificity of 0.813.
conclusionsResults show that supervised machine learning of MRI texture features can predict MGMT methylation status in preoperative GBM tumors, thus providing a new noninvasive imaging biomarker.
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Registered trials
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.