Predicting TERT promoter mutation using MR images in patients with wild-type IDH1 glioblastoma.

PURPOSE: The purpose of this study was to identify magnetic resonance imaging (MRI) features that are associated with telomerase reverse transcriptase promoter mutation (TERTm) in glioblastoma. MATERIALS AND METHODS: A total of 112 patients with glioblastoma who had MRI at 1.5- or 3.0-T were retrospectively included. There were 43 patients with glioblastoma with wild-type TERT (TERTw) (22 men, 21 women; mean age, 47±25 [SD] years; age range: 3-84 years) and 69 patients with glioblastoma with TERTm (34 men, 35 women; mean age 64±11 [SD] years; age range, 41--85 years). The feature vectors consist of 11 input units for two clinical parameters (age and gender) and nine MRI characteristics (tumor location, subventricular extension, cortical extension, multiplicity, enhancing volume, necrosis volume, the percentage of necrosis volume, minimum apparent diffusion coefficient [ADC] and normalized ADC). First, the diagnostic performance using univariate and multivariate logistic regression analyses was evaluated. Second, the cross-validation of the support vector machine (SVM) was performed by using leave-one-out method with 43 TERTw and 69 TERTm to evaluate the diagnostic performance. In addition, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for the differentiation between TERTw and TERTm were compared between logistic regression analysis and SVM. RESULTS: With multivariate analysis, the percentage of necrosis volume and age were significantly greater in TERTm glioblastoma than in TERTw glioblastoma. SVM allowed discriminating between TERTw glioblastoma and TERTm glioblastoma with sensitivity, specificity, PPV, NPV, and accuracy of 85.7% [60/70; 95% confidence interval (CI): 75.3-92.9%], 54.8% (23/42; 95% CI: 38.7-70.2%), 75.9% (60/79; 95% CI: 69.1-81.7%), 69.7% (23/33; 95% CI: 54.9-81.3%) and 74.1% (83/112; 95% CI: 65.0-81.9%), respectively. CONCLUSION: The percentage of necrosis volume and age may surrogate for predicting TERT mutation status in glioblastoma.

MR Imaging-Based Analysis of Glioblastoma Multiforme: Estimation of IDH1 Mutation Status.

BACKGROUND AND PURPOSE: Glioblastoma multiforme is highly aggressive and the most common type of primary malignant brain tumor in adults. Imaging biomarkers may provide prognostic information for patients with this condition. Patients with glioma with isocitrate dehydrogenase 1 (IDH1) mutations have a better clinical outcome than those without such mutations. Our purpose was to investigate whether the IDH1 mutation status in glioblastoma multiforme can be predicted by using MR imaging. MATERIALS AND METHODS: We retrospectively studied 55 patients with glioblastoma multiforme with wild type IDH1 and 11 patients with mutant IDH1. Absolute tumor blood flow and relative tumor blood flow within the enhancing portion of each tumor were measured by using arterial spin-labeling data. In addition, the maximum necrosis area, the percentage of cross-sectional necrosis area inside the enhancing lesions, and the minimum and mean apparent diffusion coefficients were obtained from contrast-enhanced T1-weighted images and diffusion-weighted imaging data. Each of the 6 parameters was compared between patients with wild type IDH1 and mutant IDH1 by using the Mann-Whitney U test. The performance in discriminating between the 2 entities was evaluated by using receiver operating characteristic analysis. RESULTS: Absolute tumor blood flow, relative tumor blood flow, necrosis area, and percentage of cross-sectional necrosis area inside the enhancing lesion were significantly higher in patients with wild type IDH1 than in those with mutant IDH1 (P < .05 each). In contrast, no significant difference was found in the ADC(minimum) and ADC(mean). The area under the curve for absolute tumor blood flow, relative tumor blood flow, percentage of cross-sectional necrosis area inside the enhancing lesion, and necrosis area were 0.850, 0.873, 0.739, and 0.772, respectively. CONCLUSIONS: Tumor blood flow and necrosis area calculated from MR imaging are useful for predicting the IDH1 mutation status.