Glioblastoma is an aggressive primary brain cancer that is notoriously difficult to treat, in part due to its diffuse infiltration of brain tissue and the limitations of the blood-brain barrier (BBB), which often prevents drugs from reaching the entire tumor. Specifically, rapid tumor proliferation leads to accelerated angiogenesis resulting in a ‘leaky’ BBB, which affects drug distribution. To address the differential impact of this BBB heterogeneity across patients, time series bioluminescence imaging (BLI) data was compiled from experiments treating murine orthotopic glioblastoma patient-derived xenografts (PDXs). The extent of BBB breakdown has been previously quantified for multiple PDX lines, allowing us to examine the heterogeneity in this feature among human patients. BLI data directly quantifies total tumor cell abundance, allowing us to observe how therapy affects tumor cell populations. After adjusting for lead time bias via a nonlinear mixed effects approach, we used the serial BLI data to obtain an overall growth rate for each PDX line across multiple subjects. These different growth kinetics were used to parametrize corresponding therapeutic models of the individual PDX lines. While further work is needed to verify our results across more PDX lines, they suggest that our existing characterization of tumor invasiveness may be able to aid in matching patients to the best therapy for their individual tumors.