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 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.

FIG. 1.

Diagram capturing dataset formation for this study.

FIG. 2.

Glioblastoma cases with methylated MGMT GBM tumor. The T1 postcontrast [(a), (d), and (g)], the T2 [(b), (e), and (h)], and the tumor ROI overlaid (blue) on the T1 postcontrast are depicted [(c), (f), and (i)].

FIG. 3.

Glioblastoma cases with unmethylated MGMT GBM tumor. The T1 postcontrast [(a), (d), and (g)], the T2 [(b), (e), and (h)], and the tumor ROI overlaid (blue) on the T1 postcontrast are depicted [(c), (f), and (i)].

FIG. 4.

Correlations of the selected features for the best-performing classification schemes.

FIG. 5.

Glioblastoma cases with methylated MGMT GBM tumor. Texture features corresponding to the best-performing classification scheme (A_z = 0.850), cluster prominence [(b), (g), and (l)], correlation [(c), (h), and (m)], Haralick correlation [(d), (i), and (n)], inertia [(e), (j), and (o)], and corresponding tumor ROI [(a), (f), and (k)].

FIG. 6.

Glioblastoma cases with unmethylated MGMT GBM tumor. Texture features corresponding to the best-performing classification scheme (A_z = 0.850), cluster prominence [(b), (g), and (l)], correlation [(c), (h), and (m)], Haralick correlation [(d), (i), and (n)], inertia [(e), (j), and (o)], and corresponding tumor ROI [(a), (f), and (k)].