High-grade brain gliomas have a terrible prognosis, with mean survival just over one year even though combinations of surgical removal, radiotherapy, and chemotherapy are used in clinical practice. Our overarching hypothesis is that advanced modeling and analysis methods, which combine diverse information from patient multiparametric images, tumor histologic and molecular profile, biophysical models of glioma growth, and population-based statistics, will improve our ability to estimate the spatial extent and spatial heterogeneity of tumor infiltration, and to predict tumor recurrence and patient survival. If our results turn out to support this hypothesis, treatment of these aggressive tumors can be tailored to better balance the trade-off between targeting tumor death and preserving normal function. Moreover, having estimates of patient survival not only influences treatment decisions but is also important for personal and social reasons.

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