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

K. Yamashita, R. Hatae, Hiwatashi Akio, Osamu Togao, kazufumi kikuchi, D. Momosaka, Y. Yamashita, Daisuke Kuga, Nobuhiro Hata, K. Yoshimoto, Satoshi Suzuki, Toru Iwaki, Koji Iihara, Hiroshi Honda

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)411-419
Number of pages9
JournalDiagnostic and Interventional Imaging
Volume100
Issue number7-8
DOIs
Publication statusPublished - Jul 1 2019

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Glioblastoma
Telomerase
Mutation
Confidence Intervals
Necrosis
Magnetic Resonance Imaging
Logistic Models
Regression Analysis
Sensitivity and Specificity
Multivariate Analysis

All Science Journal Classification (ASJC) codes

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging

Cite this

Predicting TERT promoter mutation using MR images in patients with wild-type IDH1 glioblastoma. / Yamashita, K.; Hatae, R.; Akio, Hiwatashi; Togao, Osamu; kikuchi, kazufumi; Momosaka, D.; Yamashita, Y.; Kuga, Daisuke; Hata, Nobuhiro; Yoshimoto, K.; Suzuki, Satoshi; Iwaki, Toru; Iihara, Koji; Honda, Hiroshi.

In: Diagnostic and Interventional Imaging, Vol. 100, No. 7-8, 01.07.2019, p. 411-419.

Research output: Contribution to journalArticle

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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.",
author = "K. Yamashita and R. Hatae and Hiwatashi Akio and Osamu Togao and kazufumi kikuchi and D. Momosaka and Y. Yamashita and Daisuke Kuga and Nobuhiro Hata and K. Yoshimoto and Satoshi Suzuki and Toru Iwaki and Koji Iihara and Hiroshi Honda",
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T1 - Predicting TERT promoter mutation using MR images in patients with wild-type IDH1 glioblastoma

AU - Yamashita, K.

AU - Hatae, R.

AU - Akio, Hiwatashi

AU - Togao, Osamu

AU - kikuchi, kazufumi

AU - Momosaka, D.

AU - Yamashita, Y.

AU - Kuga, Daisuke

AU - Hata, Nobuhiro

AU - Yoshimoto, K.

AU - Suzuki, Satoshi

AU - Iwaki, Toru

AU - Iihara, Koji

AU - Honda, Hiroshi

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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