A glioma is a primary brain tumor in adults that is characterized by a highly variable, but overall poor survival. The optimal timing of treatment is in part determined by the expected biological behavior of the tumor. At present the expected biological behavior, determined by the tumor genotype, can only be determined by tissue analysis, which requires brain surgery. Non-invasive and improved diagnostic methods are sought to obtain insight into the molecular profile of the tumor and the expected biological behavior to avoid surgery performed solely for diagnostic purposes. Vascularization is an important aspect of the biological behavior of a primary brain tumor. Tumor vascularization characteristics can be assessed by Magnetic Resonance Imaging (MRI), but with the currently available technology this can only be achieved with unacceptably long scan times. In this proposal, the investigators will develop and optimize a novel MRI protocol to gather a large set of quantitative vascularization parameters within an acceptable scan time. The hypothesis is that from such a 'vascular signature' the tumor genotype can be inferred by means of machine learning.