  Glioblastoma Multiforme is a high grade, very aggressive, brain tumor, with
patients having a poor prognosis. Lower grade gliomas are less aggressive, but
they can evolve into higher grade tumors over time. Patient management and
treatment can vary considerably with tumor grade, ranging from tumor resection
followed by a combined radio- and chemotherapy to a "wait and see" approach.
Hence, tumor grading is important for adequate treatment planning and
monitoring. The gold standard for tumor grading relies on histopathological
diagnosis of biopsy specimens. However, this procedure is invasive, time
consuming, and prone to sampling error. Given these disadvantages, automatic
tumor grading from widely used MRI protocols would be clinically important, as
a way to expedite treatment planning and assessment of tumor evolution. In this
paper, we propose to use Convolutional Neural Networks for predicting tumor
grade directly from imaging data. In this way, we overcome the need for expert
annotations of regions of interest. We evaluate two prediction approaches: from
the whole brain, and from an automatically defined tumor region. Finally, we
employ interpretability methodologies as a quality assurance stage to check if
the method is using image regions indicative of tumor grade for classification.
