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
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
This study indicated that 1D-CNN models could identify
IDH-mut, TERTp-mut and TERTp-only gliomas using 1H-MRS with 94.11%, 76.92%, and
82.05% accuracies, respectively. Deep-learning techniques might be promising
for the mutational classification of 1H-MRS data of gliomas.
Table 2. The performance metrics of the 1D-CNN models on test and validation
set.
Figure 2. Example short TE PRESS 1H-MRS data for (a)
IDH-mut&TERTp-mut, (b) IDH-mut&TERTp-wt, (c) TERTp-only, and (d) IDH-wt&TERTp-wt
gliomas.
