Conventional magnetic resonance imaging–based radiomic signature predicts telomerase reverse transcriptase promoter mutation status in grade II and III gliomas

Jiang, Chendan; Kong, Ziren; Zhang, Yiwei; Liu, Sirui; Liu, Zeyu; Chen, Wenlin; Liu, Penghao; Liu, Delin; Wang, Yaning; Lyu, Yuelei; Zhao, Dachun; Wang, Yu; You, Hui; Feng, Feng; Ma, Wenbin

doi:10.1007/s00234-020-02392-1

Cited by 28 publications

(37 citation statements)

References 45 publications

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“…In addition, based on the convolutional neural network features described above, the linear SVM model reached an accuracy of 0.84 ± 0.09 ( 12 ). Further, the prediction of mutations in p TERT in the subgroup of IDH also reached stable performances, where the random forest model achieved an AUC of 0.824 (95% CI, 0.639–1) and 0.750 (95% CI, 0.260–1) in the mutant IDH and wild-type IDH groups, respectively ( 13 ).…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, some studies presenting radiomics analysis focus on p TERT mutations only. A previous study compared three machine-learning methods in predicting p TERT mutations in LGGs, including random forest, SVM, and adaboost methods ( 13 ). The results showed that the random forest method had the best performance after feature selection using LASSO, and the AUC value reached 0.827 (95% CI, 0.667–0.988) in the validation group.…”

Section: Discussionmentioning

confidence: 99%

“…Although the above radiomics-based analysis achieved good performance in the predication of mutations in p TERT , previous studies have focused on LGGs, which are composed of WHO grades II and III gliomas, with limited sample sizes ( 11 – 13 , 29 ). However, gliomas in WHO grade II and HGG showed differences in biological and radiomics features ( 49 ).…”

Section: Discussionmentioning

confidence: 99%

“…However, the predominant focus of many prior studies has been the prediction of the subtype combination of p TERT and IDH , which has demonstrated moderate performance ( 11 , 12 ), rather than the status of p TERT alone. Other studies have exhibited superior performance at predicting p TERT status in patients, including those with higher-grade gliomas (WHO grade III or IV) ( 13 , 14 ). In this regard, p TERT status in WHO grade II gliomas has rarely been predicted directly.…”

Section: Introductionmentioning

confidence: 99%

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Radiomics Features Predict Telomerase Reverse Transcriptase Promoter Mutations in World Health Organization Grade II Gliomas via a Machine-Learning Approach

et al. 2021

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The detection of mutations in telomerase reverse transcriptase promoter (pTERT) is important since preoperative diagnosis of pTERT status helps with evaluating prognosis and determining the surgical strategy. Here, we aimed to establish a radiomics-based machine-learning algorithm and evaluated its performance with regard to the prediction of mutations in pTERT in patients with World Health Organization (WHO) grade II gliomas. In total, 164 patients with WHO grade II gliomas were enrolled in this retrospective study. We extracted a total of 1,293 radiomics features from multi-parametric magnetic resonance imaging scans. Elastic net (used for feature selection) and support vector machine with linear kernel were applied in nested 10-fold cross-validation loops. The predictive model was evaluated by receiver operating characteristic and precision-recall analyses. We performed an unpaired t-test to compare the posterior predictive probabilities among patients with differing pTERT statuses. We selected 12 valuable radiomics features using nested 10-fold cross-validation loops. The area under the curve (AUC) was 0.8446 (95% confidence interval [CI], 0.7735–0.9065) with an optimal summed value of sensitivity of 0.9355 (95% CI, 0.8802–0.9788) and specificity of 0.6197 (95% CI, 0.5071–0.7371). The overall accuracy was 0.7988 (95% CI, 0.7378–0.8598). The F1-score was 0.8406 (95% CI, 0.7684–0.902) with an optimal precision of 0.7632 (95% CI, 0.6818–0.8364) and recall of 0.9355 (95% CI, 0.8802–0.9788). Posterior probabilities of pTERT mutations were significantly different between patients with wild-type and mutant TERT promoters. Our findings suggest that a radiomics analysis with a machine-learning algorithm can be useful for predicting pTERT status in patients with WHO grade II glioma and may aid in glioma management.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Radiomics Features Predict Telomerase Reverse Transcriptase Promoter Mutations in World Health Organization Grade II Gliomas via a Machine-Learning Approach

et al. 2021

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“…Advances in radiomics [9,10] and MRI techniques, including ASL [11,16,35,36], DSC [2,35,37], and diffusion tensor imaging [38,39], have been used in evaluating glioma grade or genotypes. Several studies [14,16,36] have shown that, compared with perfusion parameters, ADC values have a better predictive effect on tumor grade and genotypes.…”

Section: Discussionmentioning

confidence: 99%

Feasibility of Evaluating the Histologic and Genetic Subtypes of WHO Grade II-IV Gliomas by Diffusion-Weighted Imaging

Liu

Zhang

Kong

et al. 2021

Preprint

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Background: To explore the feasibility of diffusion-weighted imaging (DWI) metrics to predict the histologic subtypes and genetic status of gliomas (e.g., IDH, MGMT, and TERT) noninvasively.Methods: One hundred and eleven patients with pathologically confirmed WHO grade II-IV gliomas were recruited retrospectively. Apparent diffusion coefficient (ADC) values were measured in solid parts of gliomas on co-registered T2-weighted images and were compared with each other in terms of WHO grading and genotypes using t-tests. Receiver operating characteristic analysis was performed to assess the diagnostic performances of ADC. Subsequently, multiple linear regression was used to find independent variables, which can directly affect ADC values. Results: The values of overall mean ADC (omADC) and normalized ADC (nADC) of high grade gliomas and IDH wildtype gliomas were lower than low grade gliomas and IDH mutated gliomas (P<0.05). nADC values showed better diagnostic performance than omADC in identifying tumor grade (AUC: 0.787 vs. 0.750) and IDH status (AUC: 0.836 vs. 0.777). ADC values had limited abilities in distinguishing TERT status (AUC=0.607 for nADC and 0.617 for omADC) and MGMT status (AUC=0.651 for nADC). Only tumor grade and IDH status were tightly associated with ADC values.Conclusion: DWI metrics can predict glioma grading and IDH mutation noninvasively, but have limited use in detecting TERT mutation and MGMT methylation.

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A radiomics‐based nomogram may be useful for predicting telomerase reverse transcriptase promoter mutation status in adult glioblastoma

Li,

Chen,

Huang

et al. 2024

Brain and Behavior

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Background and purposeAs a crucial diagnostic and prognostic biomarker, telomerase reverse transcriptase (TERT) promoter mutation holds immense significance for personalized treatment of patients with glioblastoma (GBM). In this study, we developed a radiomics nomogram to determine the TERT promoter mutation status and assessed its prognostic efficacy in GBM patients.MethodsThe study retrospectively included 145 GBM patients. A comprehensive set of 3736 radiomics features was extracted from preoperative magnetic resonance imaging, including T2‐weighted imaging, T1‐weighted imaging (T1WI), contrast‐enhanced T1WI, and fluid‐attenuated inversion recovery. The construction of the radiomics model was based on integrating the radiomics signature (rad‐score)with clinical features. Receiver‐operating characteristic curve analysis was employed to evaluate the discriminative ability of the prediction model, and the risk score was used to stratify patient outcomes.ResultsThe least absolute shrinkage and selection operator classifier identified 10 robust features for constructing the prediction model, and the radiomics nomogram exhibited excellent performance in predicting TERT promoter mutation status, with area under the curve values of.906 (95% confidence interval [CI]:.850–.963) and.899 (95% CI:.708–.966) in the training and validation sets, respectively. The clinical utility of the radiomics nomogram is further supported by calibration curve and decision curve analyses. Additionally, the radiomics nomogram effectively stratified GBM patients with significantly different prognoses (HR = 1.767, p = .019).ConclusionThe radiomics nomogram holds promise as a modality for evaluating TERT promoter mutations and prognostic outcomes in patients with GBM.

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Conventional magnetic resonance imaging–based radiomic signature predicts telomerase reverse transcriptase promoter mutation status in grade II and III gliomas

Cited by 28 publications

References 45 publications

Radiomics Features Predict Telomerase Reverse Transcriptase Promoter Mutations in World Health Organization Grade II Gliomas via a Machine-Learning Approach

Radiomics Features Predict Telomerase Reverse Transcriptase Promoter Mutations in World Health Organization Grade II Gliomas via a Machine-Learning Approach

Feasibility of Evaluating the Histologic and Genetic Subtypes of WHO Grade II-IV Gliomas by Diffusion-Weighted Imaging

A radiomics‐based nomogram may be useful for predicting telomerase reverse transcriptase promoter mutation status in adult glioblastoma

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