TERT-Promoter Mutational Status in Glioblastoma – Is There an Association With Amino Acid Uptake on Dynamic 18F-FET PET?

Ruf, Viktoria; Rohr, Katharina von; Suchorska, Bogdana; Mittlmeier, Lena M.; Beyer, Leonie; Brendel, Matthias; Wenter, Vera; Kunz, Wolfgang G.; Bartenstein, Peter; Herms, Jochen; Niyazi, Maximilian; Tonn, Jörg C.; Albert, Nathalie L.

doi:10.3389/fonc.2021.645316

Cited by 4 publications

(6 citation statements)

References 57 publications

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“…), and dynamic [ 18 F]FET PET images to develop the radiomic models. A total of 107 features were extracted from each [24]. Interestingly, radiomics based on the standard TBR 20-40 model showed a low performance for the prediction of the TERTp-mutation status, and even the TBR 5-15 model, generated from nine early summation [ 18 F]FET PET features, had an accuracy of only 66% and an AUC of 0.61 in the testing cohort.…”

Section: Discussionmentioning

confidence: 99%

“…Our TTP model, built from ten dynamic [ 18 F]FET PET features selected by RFE, achieved the highest AUC of 0.82 in the independent testing cohort, indicating that the TERTp-mutation status can be predicted by using [ 18 F]FET PET based radiomics. Notably, our former study did neither find an association between the TERTp-mutation status and traditional static [ 18 F]FET PET parameters (TBR max and TBR mean in static 20–40 min summation images) nor the standard dynamic parameter TTP min [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

“…The retrospective analysis of PET imaging data was approved by the institutional ethics committee (604–16). A total of 61% of the investigated patients (97/159) have been evaluated in a previous study [ 24 ].…”

Section: Methodsmentioning

confidence: 99%

“…Interestingly, an early TTP min was associated with an aggressive disease course in newly diagnosed gliomas and was able to predict an IDH-wildtype status [ 22 , 23 ]. Yet, in our recently published study investigating [ 18 F]FET uptake characteristics in TERTp mutant and TERTp wildtype glioblastomas, neither the standard TBR max as static parameter nor TTP min as dynamic parameter were associated with the TERTp-mutation status [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of TERTp-mutation status in IDH-wildtype high-grade gliomas using pre-treatment dynamic [18F]FET PET radiomics

Kaiser

Holzgreve

et al. 2021

Eur J Nucl Med Mol Imaging

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Purpose To evaluate radiomic features extracted from standard static images (20–40 min p.i.), early summation images (5–15 min p.i.), and dynamic [18F]FET PET images for the prediction of TERTp-mutation status in patients with IDH-wildtype high-grade glioma. Methods A total of 159 patients (median age 60.2 years, range 19–82 years) with newly diagnosed IDH-wildtype diffuse astrocytic glioma (WHO grade III or IV) and dynamic [18F]FET PET prior to surgical intervention were enrolled and divided into a training (n = 112) and a testing cohort (n = 47) randomly. First-order, shape, and texture radiomic features were extracted from standard static (20–40 min summation images; TBR20–40), early static (5–15 min summation images; TBR5–15), and dynamic (time-to-peak; TTP) images, respectively. Recursive feature elimination was used for feature selection by 10-fold cross-validation in the training cohort after normalization, and logistic regression models were generated using the radiomic features extracted from each image to differentiate TERTp-mutation status. The areas under the ROC curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive value were calculated to illustrate diagnostic power in both the training and testing cohort. Results The TTP model comprised nine selected features and achieved highest predictability of TERTp-mutation with an AUC of 0.82 (95% confidence interval 0.71–0.92) and sensitivity of 92.1% in the independent testing cohort. Weak predictive capability was obtained in the TBR5–15 model, with an AUC of 0.61 (95% CI 0.42–0.80) in the testing cohort, while no predictive power was observed in the TBR20–40 model. Conclusions Radiomics based on TTP images extracted from dynamic [18F]FET PET can predict the TERTp-mutation status of IDH-wildtype diffuse astrocytic high-grade gliomas with high accuracy preoperatively.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of TERTp-mutation status in IDH-wildtype high-grade gliomas using pre-treatment dynamic [18F]FET PET radiomics

Kaiser

Holzgreve

et al. 2021

Eur J Nucl Med Mol Imaging

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“…Recent studies have also reported better diagnostic performances for radiomic features obtained from dynamic parametric images as compared to more conventional dynamic parameters extracted from volumes of interest (VOIs) [ 12 , 13 ]. The same authors reported improved diagnostic performances of dynamic parameters extracted from the voxel level coupled with radiomic analysis compared to dynamic parameters extracted from a VOI, with the latter unable to predict the presence of TERT promoter mutation in gliomas at the initial diagnosis [ 12 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Relevance of Dynamic 18F-DOPA PET Radiomics for Differentiation of High-Grade Glioma Progression from Treatment-Related Changes

et al. 2021

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This study evaluates the relevance of 18F-DOPA PET static and dynamic radiomics for differentiation of high-grade glioma (HGG) progression from treatment-related changes (TRC) by comparing diagnostic performances to the current PET imaging standard of care. Eighty-five patients with histologically confirmed HGG and investigated by dynamic 18F-FDOPA PET in two institutions were retrospectively selected. ElasticNet logistic regression, Random Forest and XGBoost machine models were trained with different sets of features—radiomics extracted from static tumor-to-background-ratio (TBR) parametric images, radiomics extracted from time-to-peak (TTP) parametric images, as well as combination of both—in order to discriminate glioma progression from TRC at 6 months from the PET scan. Diagnostic performances of the models were compared to a logistic regression model with TBRmean ± clinical features used as reference. Training was performed on data from the first center, while external validation was performed on data from the second center. Best radiomics models showed only slightly better performances than the reference model (respective AUCs of 0.834 vs. 0.792, p < 0.001). Our current results show similar findings at the multicentric level using different machine learning models and report a marginal additional value for TBR static and TTP dynamic radiomics over the classical analysis based on TBR values.

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Dynamic amino-acid PET in neuro-oncology: a prognostic tool becomes essential

Verger

Imbert

Zaragori

2021

Eur J Nucl Med Mol Imaging

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TERT-Promoter Mutational Status in Glioblastoma – Is There an Association With Amino Acid Uptake on Dynamic 18F-FET PET?

Cited by 4 publications

References 57 publications

Prediction of TERTp-mutation status in IDH-wildtype high-grade gliomas using pre-treatment dynamic [18F]FET PET radiomics

Prediction of TERTp-mutation status in IDH-wildtype high-grade gliomas using pre-treatment dynamic [18F]FET PET radiomics

Relevance of Dynamic 18F-DOPA PET Radiomics for Differentiation of High-Grade Glioma Progression from Treatment-Related Changes

Dynamic amino-acid PET in neuro-oncology: a prognostic tool becomes essential

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