TY - JOUR
T1 - Predicting TERT promoter mutation using MR images in patients with wild-type IDH1 glioblastoma
AU - Yamashita, K.
AU - Hatae, R.
AU - Hiwatashi, A.
AU - Togao, O.
AU - Kikuchi, K.
AU - Momosaka, D.
AU - Yamashita, Y.
AU - Kuga, D.
AU - Hata, N.
AU - Yoshimoto, K.
AU - Suzuki, S. O.
AU - Iwaki, T.
AU - Iihara, K.
AU - Honda, H.
N1 - Funding Information:
This work was supported by the Japan Society for the Promotion of Science (Grant Number 17K10365).
Publisher Copyright:
© 2019 Société française de radiologie
PY - 2019/7/1
Y1 - 2019/7/1
N2 - 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.
AB - 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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U2 - 10.1016/j.diii.2019.02.010
DO - 10.1016/j.diii.2019.02.010
M3 - Article
C2 - 30948344
AN - SCOPUS:85063651717
SN - 2211-5684
VL - 100
SP - 411
EP - 419
JO - Diagnostic and Interventional Imaging
JF - Diagnostic and Interventional Imaging
IS - 7-8
ER -