This is the challenge design document for the "Computational Precision Medicine Radiology-Pathology challenge on Brain Tumor Classification 2020", accepted for MICCAI 2020. The goal of CPM-RadPath 2020 is to assess automated brain tumor (glioma) classification algorithms, when data from both radiology (MRI) and histopathology (digital pathology) imaging are used. The algorithmic performance will be evaluated based on a retrospective cohort of three types of gliomas, i.e., glioblastoma, oligodendroglioma, and astrocytoma. The significance of CPM-RadPath 2020 challenge is the integrated use of two different types of imaging, at different spatial resolutions, both of which are key in routine clinical diagnosis and management of brain tumor patients. The selection and number of features from each imaging type will be left to the participants. However, they will be required to use at least one feature from each imaging data type in their algorithms, but the decision about how information from the two imaging types is integrated is left to the participants. This challenge will make use of 388 cases, multiparametric (mpMRI) and histopathology, collected from the same patients. The challenge will be conducted in three phases: i. Training (70% of cases) – labels revealed, ii. Validation (20%) – labels hidden, best of 3 submissions placed on the leaderboard, iii. Test (10%) – labels hidden, single submission of a docker or a singularity container for the final ranking. Products of CPM-RadPath 2020 challenge include: (1) publication of short papers from all participants who complete the validation phase, (2) submission of a journal manuscript reporting a summarized meta-analysis of the challenge outcomes and findings, co-authored by the CPM-RadPath 2020 organizers and members of participating teams, (3) public dissemination of dockerized solutions from top teams through the CPM-RadPath DockerHub site (per participants' prior agreement), and (4) designation of CPM-RadPath challenge dataset in The Cancer Imaging Archive and the Imaging Data Commons of the National Cancer Institute.