Radiomics Features Predict Promoter Mutations in World Health Organization Grade II Gliomas a Machine-Learning Approach.

The detection of mutations in telomerase reverse transcriptase promoter (p) is important since preoperative diagnosis of p 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 p 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 p 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 p mutations were significantly different between patients with wild-type and mutant promoters. Our findings suggest that a radiomics analysis with a machine-learning algorithm can be useful for predicting p status in patients with WHO grade II glioma and may aid in glioma management.