ArticleScientific reports2022
Improving MGMT methylation status prediction of glioblastoma through optimizing radiomics features using genetic algorithm-based machine learning approach.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 3 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 3 syntheses or guidelines pooled it, 54 citations in OpenAlex.
- Diagnostic Accuracy of Artificial Intelligence for Predicting MGMT Promoter Methylation in Glioblastoma Using MR Imaging: A Systematic Review.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Pooled it
- Radiomics and Machine Learning in Diagnostics of Glial Brain Tumors: a Systematic Review and Meta-Analysis.Sovremennye tekhnologii v meditsine · 2025Pooled 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
- 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
- Integrated Multi-Omics and Radiogenomic Analysis Identifies S100B and ITGB5 as Complementary Candidate Biomarkers of Glioblastoma Heterogeneity.Chemical biology & drug design · 2026Article
- A data-driven scientific approach to explore the relationship between MGMT methylation and imaging phenotype in glioblastoma.Radiological physics and technology · 2026Article
- Article
- Left Pulvinar Thalamic Tumor with Ventricular Atrial Extension Presenting as Network-Level Cognitive and Gait Dysfunction.Diagnostics (Basel, Switzerland) · 2026Article
- Radiomics in glioblastoma recurrence: advances in prediction, localization, and differentiation from treatment-related effects.Journal of translational medicine · 2026Review
- Metal-based nanomaterials achieve tumor treatment strategies by overcoming the blood-brain barrier of glioma and responding to the tumor microenvironment.Materials today. Bio · 2026Review
- A Comprehensive Analysis of Natural Bioactive Molecules for the Treatment and Control of Glioblastoma Multiforme (GBM) Targeting Underlying Molecular Mechanism.Chemistry & biodiversity · 2026Review
- Artificial intelligence in cancer: applications, challenges, and future perspectives.Molecular cancer · 2025Review
- Article
- Artificial Intelligence-Driven Multi-Omics Approaches in Glioblastoma.International journal of molecular sciences · 2025Review
- Prediction of MGMT methylation status in glioblastoma patients based on radiomics feature extracted from intratumoral and peritumoral MRI imaging.Scientific reports · 2025Article
- Radiomics and AI-Based Prediction of MGMT Methylation Status in Glioblastoma Using Multiparametric MRI: A Hybrid Feature Weighting Approach.Diagnostics (Basel, Switzerland) · 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
- Correlations ofMolecular and clinical oncology · 2025Article
- Apparent diffusion coefficient predicts MGMT status in adult-type diffuse gliomas and is correlated with Ki-67 proliferation index.Frontiers in oncology · 2025Article
- Radiomics for predicting grades, isocitrate dehydrogenase mutation, and oxygen 6-methylguanine-DNA methyltransferase promoter methylation of adult diffuse gliomas: combination of structural MRI, apparent diffusion coefficient, and susceptibility-weighted imaging.Quantitative imaging in medicine and surgery · 2024Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
O6-Methylguanine-DNA-methyltransferase (MGMT) promoter methylation was shown in many studies to be an important predictive biomarker for temozolomide (TMZ) resistance and poor progression-free survival in glioblastoma multiforme (GBM) patients. However, identifying the MGMT methylation status using molecular techniques remains challenging due to technical limitations, such as the inability to obtain tumor specimens, high prices for detection, and the high complexity of intralesional heterogeneity. To overcome these difficulties, we aimed to test the feasibility of using a novel radiomics-based machine learning (ML) model to preoperatively and noninvasively predict the MGMT methylation status. In this study, radiomics features extracted from multimodal images of GBM patients with annotated MGMT methylation status were downloaded from The Cancer Imaging Archive (TCIA) public database for retrospective analysis. The radiomics features extracted from multimodal images from magnetic resonance imaging (MRI) had undergone a two-stage feature selection method, including an eXtreme Gradient Boosting (XGBoost) feature selection model followed by a genetic algorithm (GA)-based wrapper model for extracting the most meaningful radiomics features for predictive purposes. The cross-validation results suggested that the GA-based wrapper model achieved the high performance with a sensitivity of 0.894, specificity of 0.966, and accuracy of 0.925 for predicting the MGMT methylation status in GBM. Application of the extracted GBM radiomics features on a low-grade glioma (LGG) dataset also achieved a sensitivity 0.780, specificity 0.620, and accuracy 0.750, indicating the potential of the selected radiomics features to be applied more widely on both low- and high-grade gliomas. The performance indicated that our model may potentially confer significant improvements in prognosis and treatment responses in GBM patients.
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