ArticleEuropean radiology2018
Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study.
Article in European radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers, 6 of them syntheses that pooled it.
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Who cites it
92 citing papers in PubMed, 6 syntheses or guidelines pooled it, 160 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
- 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
- Pooled it
- Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.European journal of nuclear medicine and molecular imaging · 2019Pooled it
- Radiomics in glioblastoma recurrence: advances in prediction, localization, and differentiation from treatment-related effects.Journal of translational medicine · 2026Review
- Integrating Genomics, Radiomics, and Pathomics in Oncology: A Scoping Review and a Framework for AI-Enabled Surgomics.Bioengineering (Basel, Switzerland) · 2026Review
- Fusion of habitat analysis and deep learning on contrast-enhanced T1-weighted imaging for predicting Ki-67 status in pediatric brain tumors.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2025Article
- 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
- From images to clinical insights: an educational review on radiomics in lung diseases.Breathe (Sheffield, England) · 2025Review
- Dynamic susceptibility contrast‑enhanced perfusion magnetic resonance imaging parameters for predictingOncology letters · 2024Article
- The prognostic importance of glioblastoma size and shape.Acta neurochirurgica · 2024Article
- Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements.The Lancet. Oncology · 2024Review
- Review
- Evolution of Molecular Biomarkers and Precision Molecular Therapeutic Strategies in Glioblastoma.Cancers · 2024Review
- 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
- Clinical development of MRI-based multi-sequence multi-regional radiomics model to predict lymph node metastasis in rectal cancer.Abdominal radiology (New York) · 2024Article
32 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
objectivesTo build a reliable radiomics model from multiregional and multiparametric magnetic resonance imaging (MRI) for pretreatment prediction of O
methodsIn this retrospective multicentre study, 1,705 multiregional radiomics features were automatically extracted from multiparametric MRI. A radiomics model with a minimal set of all-relevant features and a radiomics model with univariately-predictive and non-redundant features were built for MGMT methylation prediction from a primary cohort (133 patients) and tested on an independent validation cohort (60 patients). Predictive models combing clinical factors were built and evaluated. Both radiomics models were assessed on subgroups stratified by clinical factors.
resultsThe radiomics model with six all-relevant features allowed pretreatment prediction of MGMT methylation (AUC=0.88, accuracy=80 %), which significantly outperformed the model with eight univariately-predictive and non-redundant features (AUC=0.76, accuracy=70 %). Combing clinical factors with radiomics features did not benefit the prediction performance. The all-relevant model achieved significantly better performance in stratified analysis.
conclusionsRadiomics model built from multiregional and multiparameter MRI may serve as a potential imaging biomarker for pretreatment prediction of MGMT methylation in GBM. The all-relevant features have the potential of offering better predictive power than the univariately-predictive and non-redundant features. KEY POINTS: • Multiregional and multiparametric MRI features reliably predicted MGMT methylation in multicentre cohorts. • All-relevant imaging features predicted MGMT methylation better than univariately-predictive and non-redundant features. • Combing clinical factors with radiomics features did not benefit the prediction performance.
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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.