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 Danyeli^2,3, Kubra Tan⁴, Ozge Can⁵, Necmettin Pamir^3,6, Alp Dincer^3,7, Koray Ozduman^3,8, 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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