Radiomics and MGMT promoter methylation for prognostication of newly diagnosed glioblastoma

Abstract

We attempted to establish a magnetic resonance imaging (MRI)-based radiomic model for stratifying prognostic subgroups of newly diagnosed glioblastoma (GBM) patients and predicting O (6)-methylguanine-DNA methyltransferase promotor methylation (pMGMT-met) status of the tumor. Preoperative MRI scans from 201 newly diagnosed GBM patients were included in this study. A total of 489 texture features including the first-order feature, second-order features from 162 datasets, and location data from 182 datasets were collected. Supervised principal component analysis was used for prognostication and predictive modeling for pMGMT-met status was performed based on least absolute shrinkage and selection operator regression. 22 radiomic features that were correlated with prognosis were used to successfully stratify patients into high-risk and low-risk groups (p = 0.004, Log-rank test). The radiomic high- and low-risk stratification and pMGMT status were independent prognostic factors. As a matter of fact, predictive accuracy of the pMGMT methylation status was 67% when modeled by two significant radiomic features. A significant survival difference was observed among the combined high-risk group, combined intermediate-risk group (this group consists of radiomic low risk and pMGMT-unmet or radiomic high risk and pMGMT-met), and combined low-risk group (p = 0.0003, Log-rank test). Radiomics can be used to build a prognostic score for stratifying high- and low-risk GBM, which was an independent prognostic factor from pMGMT methylation status. On the other hand, predictive accuracy of the pMGMT methylation status by radiomic analysis was insufficient for practical use.

Figure 1

Overview of the analyzed cohort with landscape of genetic information. Genetic status is shown by color as indicated.

Figure 2

Illustration showing the workflow for image analysis. Two types of VOIs were created based on Gd enhancement of the tumor and edema lesion identification on T2-weighted images. Both VOIs were co-registered, and VOI_core and VOI_edema were generated. Subsequently, intensity normalization of all images was performed, and first-order and second-order texture analysis, VOI shape analysis, and location analysis were performed.

Figure 3

Lesion location mapping on the MNI152 standard brain atlas. Note that both enhancing and edema lesions were distributed symmetrically on both sides of the brain. Also note that enhancing lesions tended to occur in proximity to the ventricles compared with edema lesions.

Figure 4

Kaplan-Meier curve of the cohort stratified by radiomic risk score (A) and pMGMT methylation status (C). Both stratifications identified the poor-risk subgroup within the cohort. P values were calculated with the Log-rank test. LASSO was further used to predict long-term survivors using various cut-off in overall survival (B). In this analysis, predictive modeling was possible only when the cut-off was set within 10 to 17 months.

Figure 5

(A) Significant radiomic features corresponding to the prognostic outcome within the cohort using supervised principal component analysis. Twenty-two features were identified by supervised principal component analysis. (B) Significant radiomic features corresponding to the prognostic outcome within the cohort using LASSO. 36 features were identified by supervised principal component analysis. Items colored in “red” are those both identified by supervised principal component analysis and LASSO. (C) Two radiomic features were identified to be predictive of pMGMT methylation status. Of note, higher T1Gd_core_GLRLMLrge_SD was indicative of pMGMT unmethylated glioblastomas.

Figure 6

Kaplan-Meier curve of three different risk groups: low risk is composed of patients of radiomic low risk with pMGMT methylated status; medium risk is composed of patients of radiomic low risk with pMGMT unmethylated status or radiomic high risk with pMGMT methylated status; high risk is composed of patients of radiomic high risk with pMGMT unmethylated status. The P value was calculated with the Log-rank test.