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

Burak Han Akkurt; Dorothee Cäcilia Spille; Susanne Peetz-Dienhart; Nora Maren Kiolbassa; Christian Mawrin; Manfred Musigmann; Walter Leonhard Heindel; Werner Paulus; Walter Stummer; Manoj Mannil; Benjamin Brokinkel

doi:10.3390/cancers15174415

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

Cancers (Basel). 2023 Sep 4;15(17):4415. doi: 10.3390/cancers15174415.

Affiliations

¹ Department of Radiology, University Hospital Muenster, DE-48149 Muenster, Germany.
² Department of Neurosurgery, University Hospital Muenster, DE-48149 Muenster, Germany.
³ Institute of Neuropathology, University Hospital Muenster, DE-48149 Muenster, Germany.
⁴ Department of Neuropathology, University Hospital Magdeburg, 39120 Magdeburg, Germany.
⁵ Institute for Diagnostic and Interventional Radiology, Caritas-Hospital, DE-97980 Bad Mergentheim, Germany.

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.

Keywords: TERT; human telomerase reverse transcriptase; magnetic resonance imaging; meningiomas; radiomics.

Grants and funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. We acknowledge support from the Open Access Publication Fund of the University of Muenster.