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Abstract #0957

1D-CNN for the Detection of IDH and TERTp Mutations in Diffuse Gliomas using Proton Magnetic Resonance Spectroscopy

Abdullah BAS1, Banu Sacli-Bilmez1, Ayca Ersen Danyeli2,3, Cengiz Yakicier3,4, M.Necmettin Pamir3,5, Koray Ozduman3,5, Alp Dincer3,6, and Esin Ozturk-Isik1,3
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, Acıbadem Mehmet Ali Aydinlar University, Istanbul, Turkey

Isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase promoter (TERTp) mutations affect the clinical behavior and survival rate of diffuse gliomas. The detection of these mutations preoperatively is very critical for treatment planning. In this study, three different one dimensional convolutional neural network (1D-CNN) models were designed to identify IDH mutant (IDH-mut), TERTp mutant (TERTp-mut), and TERTp-only (IDH-wild type and TERTp-mut) gliomas based on proton magnetic-resonance spectroscopy (1H-MRS). The 1D-CNN models could identify IDH-mut, TERTp-mut, and TERTp-only gliomas with 94.11%, 76.92%, and 82.05% accuracies, respectively. This study showed the potential of deep-learning in predicting especially IDH-mutations in gliomas using 1H-MRS data.

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