Abstract #5035

Prediction of NF2 Loss in Meningiomas Using T1-Weighted Contrast Enhanced MRI Generated by Deep Convolutional Generative Adversarial Networks

Sukru Samet Dindar¹, Buse Buz-Yalug², Kubra Tan³, Ayca Ersen Danyeli^4,5, Ozge Can^5,6, Necmettin Pamir^5,7, Alp Dincer^5,8, Koray Ozduman^5,7, Yasemin P. Kahya¹, and Esin Ozturk-Isik^2,5

¹Electrical and Electronics Engineering, Bogazici University, Istanbul, Turkey, ²Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey, ³Health Institutes of Turkey, Istanbul, Turkey, ⁴Department of Medical Pathology, Acibadem University, Istanbul, Turkey, ⁵Brain Tumor Research Group, Acibadem University, Istanbul, Turkey, ⁶Department of Medical Engineering, Acibadem University, Istanbul, Turkey, ⁷Department of Neurosurgery, Acibadem University, Istanbul, Turkey, ⁸Department of Radiology, Acibadem University, Istanbul, Turkey

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial IntelligenceNeurofibromatosis type 2 (NF2) gene mutations have been linked to tumorigenesis in meningiomas. This study aims to improve the prediction of NF2 loss in meningiomas using T1-weighted contrast-enhanced MRI augmented by a deep convolutional generative adversarial network (DCGAN). Synthetically generated MRI increased the training accuracy from 78.9% to 93% and test accuracy from 69.4% to 79.5% in this study.

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