Abstract #1075

Identification of IDH and TERT Mutation Status in Glioma Patients using Dynamic Susceptibility Contrast MRI

Buse Buz Yalug¹, Ayca Ersen Danyeli^2,3, Cengiz Yakicier^3,4, M. Necmettin Pamir^3,5, Koray Ozduman^3,5, Alp Dincer^3,6, and Esin Ozturk-Isik^1,3

¹Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey, ²Department of Medical Pathology, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey, ³Center for Neuroradiological Applications and Reseach, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey, ⁴Department of Molecular Biology and Genetics, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey, ⁵Department of Neurosurgery, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey, ⁶Department of Radiology, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey

The main purpose of this study was to identify isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase (TERT) promoter mutations in glioma patients using classical machine learning and convolutional neural networks (CNN) on dynamic susceptibility contrast MRI (DSC-MRI). Relative cerebral blood volume (rCBV) maps of glioma patients with different genotypes including IDH-mutant, IDH-wildtype, TERT-mutant, and TERT-wildtype were compared in tumor areas. Classical machine learning classification results were over 85% for both IDH and TERT mutations. On the other hand, CNNs were able to classify IDH mutation status with 83% and TERT mutation status with 72% accuracies.

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