Abstract #1437
Differentiation of NF2 Loss and S100 Positivity in Meningioma Using Dynamic Susceptibility Contrast MRI with Machine Learning at 3T
Buse Buz-Yalug1, Ayca Ersen Danyeli2,3, Kubra Tan4, Ozge Can5, Necmettin Pamir3,6, Alp Dincer3,7, Koray Ozduman3,8, and Esin Ozturk-Isik1
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, 4Health Institutes of Turkey, Istanbul, Turkey, 5Department of Medical Engineering, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey, 6Department of Neurosurgery, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey, 7Department of Radiology, Acıbadem Mehmet Ali Aydinlar University, Istanbul, Turkey, 8Department of Neurosurgery, Acıbadem Mehmet Ali Aydinlar University, Istanbul, Turkey
Synopsis
Meningiomas are the most frequent primary intracranial and spinal tumors. In this work, we studied the relative cerebral blood volume (rCBV) changes in the meningiomas with NF2 loss and S100 positivity. Meningiomas with NF2-L had lower rCBV than NF2-NL, and S100 positive group had also lower rCBV than S100 negative group. The highest classification accuracies obtained using machine learning applications were 75.6% for NF2 molecular subsets and 75.2% for S100 molecular subsets.
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