Predicting TERT promoter mutation using MR images in patients with wild-type IDH1 glioblastoma - 02/04/19

Doi : 10.1016/j.diii.2019.02.010

K. Yamashita ^a,^⁎ , R. Hatae ^b, A. Hiwatashi ^a, O. Togao ^a, K. Kikuchi ^a, D. Momosaka ^a, Y. Yamashita ^c, D. Kuga ^b, N. Hata ^b, K. Yoshimoto ^b, S.O. Suzuki ^d, T. Iwaki ^d, K. Iihara ^b, H. Honda ^a
^a Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 812-8582 Fukuoka, Japan
^b Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, 812-8582 Fukuoka, Japan
^c Department of Medical Technology, Kyushu University Hospital, 812-8582 Fukuoka, Japan
^d Department of Neuropathology, Graduate School of Medical Sciences, Kyushu University, 812-8582 Fukuoka, Japan

^⁎Corresponding author.

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Abstract

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.

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Keywords : Magnetic resonance imaging (MRI), Glioblastoma, Telomerase reverse transcriptase (TERT), Isocitrate dehydrogenase, Support vector machine