Abstract #2124

Boosting The Deep Learning Performance in Predicting IDH Mutation in Gliomas Using Multiparametric MRI Including SWI, FLAIR and CE-T1WI

Sena Azamat^1,2, Buse Buz-Yaluğ¹, Alpay Ozcan³, Ayça Ersen Danyeli^4,5,6, Necmettin Pamir^5,7, Alp Dinçer^4,8, Koray Ozduman^4,7, and Esin Ozturk-Isik^1,4

¹Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey, ²Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey, ³Electric and Electronic Engineering Department, Bogazici University, Istanbul, Turkey, ⁴Brain Tumor Research Group, Acibadem University, Istanbul, Turkey, ⁵Center for Neuroradiological Applications and Reseach, Acibadem University, Istanbul, Turkey, ⁶Department of Medical Pathology, Acibadem University, Istanbul, Turkey, ⁷Department of Neurosurgery, Acibadem University, Istanbul, Turkey, ⁸Department of Radiology, Acibadem University, Istanbul, Turkey

Synopsis

Keywords: Tumors, Machine Learning/Artificial IntelligenceGliomas with IDH mutations tend to have a better prognosis regardless of the histopathological grade. The main aim of this study was to identify isocitrate dehydrogenase (IDH) mutations in glioma patients using deep learning models based on SWI, FLAIR and CE-T1W images separately and together. As a result, a 2D CNN model based on multiparametric MRI resulted in an accuracy of 92.8%, while CNN based on just SWI had 72.2% and CNN based on just FLAIR had 61.1% accuracies for predicting IDH mutation in gliomas.

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