Radiomics-Based Prediction of TERT Promotor Mutations in Intracranial High-Grade Meningiomas

SIMPLE SUMMARY: Mutations in the telomerase reverse transcriptase (TERT) gene promoter are rare in meningiomas, but are associated with more aggressive forms of meningiomas as well as recurrence and progression. In this study, we analyzed data of patients who underwent meningioma surgery with neuropathologically assessed TERT promoter mutation status. Semiautomatic segmentation of the meningiomas was performed on preoperative magnetic resonance imaging (MRI). Radiomics features were extracted from T1-weighted contrast-enhanced MRI data. TERT promoter mutations were predicted using the random forest algorithm with an increasing number of radiomic features. 117 image sets were included and divided into a training group with 94 image sets and a test group with 23 image sets. Based on the test group, established five- and eight-feature models demonstrated high sensitivity, specificity, accuracy and AUC values for prediction of TERT promoter mutation status. ABSTRACT: Purpose: In meningiomas, TERT promotor mutations are rare but qualify the diagnosis of anaplasia, directly impacting adjuvant therapy. Effective screening for patients at risk for promotor mutations could enable more targeted molecular analyses and improve diagnosis and treatment. Methods: Semiautomatic segmentation of intracranial grade 2/3 meningiomas was performed on preoperative magnetic resonance imaging. Discriminatory power to predict TERT promoter mutations was analyzed using a random forest algorithm with an increasing number of radiomic features. Two final models with five and eight features with both fixed and differing radiomics features were developed and adjusted to eliminate random effects and to avoid overfitting. Results: A total of 117 image sets including training (N = 94) and test data (N = 23) were analyzed. To eliminate random effects and demonstrate the robustness of our approach, data partitioning and subsequent model development and testing were repeated a total of 100 times (each time with repartitioned training and independent test data). The established five- and eight-feature models with both fixed and different radiomics features enabled the prediction of TERT with similar but excellent performance. The five-feature (different/fixed) model predicted TERT promotor mutation status with a mean AUC of 91.8%/94.3%, mean accuracy of 85.5%/88.9%, mean sensitivity of 88.6%/91.4%, mean specificity of 83.2%/87.0%, and a mean Cohen’s Kappa of 71.0%/77.7%. The eight-feature (different/fixed) model predicted TERT promotor mutation status with a mean AUC of 92.7%/94.6%, mean accuracy of 87.3%/88.9%, mean sensitivity of 89.6%/90.6%, mean specificity of 85.5%/87.5%, and a mean Cohen’s Kappa of 74.4%/77.6%. Of note, the addition of further features of up to N = 8 only slightly increased the performance. Conclusions: Radiomics-based machine learning enables prediction of TERT promotor mutation status in meningiomas with excellent discriminatory performance. Future analyses in larger cohorts should include grade 1 lesions as well as additional molecular alterations.