Abstract #2134

Identification of NF2 loss in meningiomas using T2-weighted MRI and Deep Learning

Abdullah Bas¹, Ayca Ersen Danyeli^2,3, Ozge Can^2,4, Koray Ozduman^2,5, Alp Dincer^2,6, and Esin Ozturk-Isik^1,2

¹Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey, ²Brain Tumor Research Group, Acibadem University, Istanbul, Turkey, ³Department of Medical Pathology, Acibadem University, Istanbul, Turkey, ⁴Department of Biomedical Engineering, Acibadem University, Istanbul, Turkey, ⁵Department of Neurosurgery, Acibadem University, Istanbul, Turkey, ⁶Department of Radiology, Acıbadem University, Istanbul, Turkey

Synopsis

Keywords: Tumors, Machine Learning/Artificial Intelligence, Deep LearningNF2-L in meningiomas is a relevant indicator of prognosis. NF2-L is one of the most common genetic mutations in meningiomas. As a result of that situation, developing a non-invasive approach may assist the current clinical procedures. To our knowledge, some studies try to predict NF2-L using MRI modalities but either they use registration or performed using the modalities of MRI that are not in default MRI scanning procedures. Hence, the solutions that they provide are not clinically feasible. In this study, we aim to develop new approaches that are capable to implement directly into clinical procedures.[1]

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