My research project is focused on mathematically modeling the spatial and temporal distribution of therapeutic radionuclides for recurrent glioblastoma and predicting the tumor’s eventual response to this therapy. Glioblastoma is a devastating disease with remarkably low survival rates which have improved very little in many years; thus, we desperately need new approaches to treating this tumor type. My project is focused on the radionuclide Rhenium-186, which is encapsulated within nanoliposome to serve as a theranostic (186RNL) administered via convection enhanced delivery. Treatment with 186RNL has the potential to provide significantly larger effective doses to a tumor than external beam radiation, while decreasing toxicity to the surrounding, healthy tissues. We propose that a tumor’s response to therapy can be modeled by parameters such as cellularity and perfusion (which can be estimated from patient-specific MRI data), as well as the distribution and properties of the administered radionuclide (which can be estimated from patient-specific SPECT data) during treatment. By solving this problem, we can provide tools to help oncologists design optimized treatment and, therefore optimize outcomes on an individual patient basis.