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

Abstract

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.

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

“…Several studies have analyzed the value of MRI based radiomics to predict the TERTp-mutation status in brain tumour patients [49][50][51]. Although these studies reported to achieve high accuracy values in the range of 79.88-93.80%, only WHO grade II or/and III gliomas have been considered and a limited number of patients has been investigated [49][50][51].…”

“…Several studies have analyzed the value of MRI based radiomics to predict the TERTp-mutation status in brain tumour patients [49][50][51]. Although these studies reported to achieve high accuracy values in the range of 79.88-93.80%, only WHO grade II or/and III gliomas have been considered and a limited number of patients has been investigated [49][50][51]. Besides, Tian et al established a multiparameter MRI based radiomics model for the prediction of the TERTp-mutation status in patients with high-grade glioma [52], but ignored that TERTp-mutations play different roles in different IDH phenotypes [48].…”

Tumor Progression and Treatment-Related Changes: Radiological Diagnosis Challenges for the Evaluation of Post Treated Glioma

“…Clinicians attempt to evaluate histopathological features and glioma genomics from non-invasive imaging using AI. Radiomics based on multimodal MRI can precisely differentiate among glioma molecular, including IDH [ 109 ], MGMT [ 110 ], TERT [ 111 ] and H3 K27M [ 112 ]. A study established SVM models for detecting IDH and TP53 mutations, and the detecting accuracies for IDH and TP53 mutations on the development cohort were 84.9% and 92.0%, while that on the validation cohort were 80.0% and 85.0%, respectively [ 113 ].…”

A Survey of Radiomics in Precision Diagnosis and Treatment of Adult Gliomas

“…Fang et al [67] extracted a total of 1293 radiomics features from preoperative MRI images (T1WI, CE-T1WI, and T2WI sequences) of 164 patients with WHO grade II gliomas and built a model for predicting TERT promoter mutation status based on the 12 most valuable radiomics features selected by nested 10-fold cross-validation cycle. The results showed that the overall accuracy was 79.88%, and the AUC was 0.8446.…”