Introduction

Adult primary brain tumors such as gliomas are characterized by enormous cellular diversity captured by grading. For gliomas, tumor grade is based on the region with the highest level of aberrant histopathology. Recently, a large number of subtypes have been characterized based on morphological variants and their molecular characterization showing an enormous heterogeneity that is only being discovered. This poses an enormous challenge in interpreting these subtypes and understanding their clinical and molecular associations.This project aims to develop open source advanced image-based modeling algorithms and software and to couple them with a bioinformatics system for the analysis of brain tumors. The net results are: a more robust identification tumor subtypes; hypothesis generation for the molecular basis of each subtype; creation of a publicly available databank where new tissue sections can be compared against an existing database of prognostic and predictive subtypes and their molecular signatures; and potentially enabling new opportunities for personalized therapy.