MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas

Abstract

Purpose:Imaging biomarker research focuses on discovering relationships between radiological features and histological findings. In glioblastoma patients, methylation of the O6-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.

MRI to MGMT: predicting methylation status in glioblastoma patients using convolutional recurrent neural networks

“…6,27–29 These studies typically require extensive manual feature curation, and may incorporate clinical data along with imaging features for classification. In comparison, our work is primarily focused on using raw MRI frames, which combines feature extraction and classification as one problem.…”

“…4,5 Previous approaches have constructed models to predict MGMT status from imaging and clinical data. 6,7 However, these models typically rely on hand curated features with classifiers such as SVM and random forests, and using neural networks may enable the discovery of novel biological features and increase the ease of implementation of such models.…”

Radiomics signature: A potential biomarker for the prediction of MGMT promoter methylation in glioblastoma

“…Although genetic analysis using surgical specimens is the reference standard for assessing the methylation status of MGMT, testing for MGMT promoter methylation status by methylation‐specific polymerase chain reaction requires a large tissue sample, while immunohistochemical detection of MGMT protein has technical shortcomings . Along with potential complications, the possibility of incomplete biopsy sampling due to the spatially heterogeneous GBM and the high expense, it is not yet widely accepted in hospitals, especially primary hospitals …”

Multi-label Inductive Matrix Completion for Joint MGMT and IDH1 Status Prediction for Glioma Patients

“…Most deadly gliomas are classified by World Health Organization (WHO) as grades III and IV, referred to as high-grade gliomas (HGG). Related studies have shown that O6-methylguanine-DNA methyltransferase promoter methylation (MGMT-m) and isocitrate dehydrogenases mutation (IDH1-m) are the two strong molecular indicators that may associate with better prognosis (i.e., better sensitivity to the treatment and longer survival time), compared to their counterparts, i.e., MGMT promoter unmethylation (MGMT-u) and IDH1 wild (IDH1-w) [1, 2]. To date, the identification of the MGMT and IDH1 statuses is becoming clinical routine, but conducted via invasive biopsy, which has limited their wider clinical implementation.…”

“…For example, Korfiatis et al extracted tumor texture features from a single T2-weighted MRI modality, and trained a support vector machine (SVM) to predict MGMT status [1]. Yamashita et al extracted both the functional information (i.e., tumor blood flow) from perfusion MRI and the structural features from T1-weighted MRI, and employed a nonparametric approach to predict IDH1 status [2].…”