MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas
Language English Country United States Media print
Document type Journal Article
Grant support
U01 CA160045
NCI NIH HHS - United States
PubMed
27277032
PubMed Central
PMC4866963
DOI
10.1118/1.4948668
Knihovny.cz E-resources
- MeSH
- DNA Modification Methylases genetics MeSH
- DNA Repair Enzymes genetics MeSH
- Glioblastoma diagnostic imaging genetics surgery MeSH
- Humans MeSH
- Magnetic Resonance Imaging methods MeSH
- DNA Methylation * MeSH
- Brain diagnostic imaging surgery MeSH
- Biomarkers, Tumor genetics MeSH
- Tumor Suppressor Proteins genetics MeSH
- Brain Neoplasms diagnostic imaging genetics surgery MeSH
- Promoter Regions, Genetic MeSH
- Retrospective Studies MeSH
- ROC Curve MeSH
- Support Vector Machine MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- DNA Modification Methylases MeSH
- DNA Repair Enzymes MeSH
- MGMT protein, human MeSH Browser
- Biomarkers, Tumor MeSH
- Tumor Suppressor Proteins MeSH
PURPOSE: Imaging 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. METHODS: A 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. RESULTS: The 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. CONCLUSIONS: Results 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.
Department of Health Sciences Research Mayo Clinic 200 1st Street SW Rochester Minnesota 55905
Department of Medical Oncology Mayo Clinic 200 1st Street SW Rochester Minnesota 55905
Department of Neurologic Surgery Mayo Clinic 200 1st Street SW Rochester Minnesota 55905
Department of Neurology Mayo Clinic 200 1st Street SW Rochester Minnesota 55905
Department of Radiology Mayo Clinic 200 1st Street SW Rochester Minnesota 55905
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