  The optimal treatment strategy of newly diagnosed glioma is strongly
influenced by tumour malignancy. Manual non-invasive grading based on MRI is
not always accurate and biopsies to verify diagnosis negatively impact overall
survival. In this paper, we propose a fully automatic 3D computer-aided
diagnosis (CAD) system to non-invasively differentiate high-grade glioblastoma
from lower-grade glioma. The approach consists of an automatic segmentation
step to extract the tumour ROI followed by classification using a 3D
convolutional neural network. Segmentation was performed using a 3D U-Net
achieving a dice score of 88.53% which matches top performing algorithms in the
BraTS 2018 challenge. The classification network was trained and evaluated on a
large heterogeneous dataset of 549 patients reaching an accuracy of 91%.
Additionally, the CAD system was evaluated on data from the Ghent University
Hospital and achieved an accuracy of 92% which shows that the algorithm is
robust to data from different centres.
