| Abstract | Gliomas, the most common primary brain tumors, are extremely aggressive and uniformly fatal, recurring inevitably despite treatment by surgical resection, radiation therapy, and chemotherapy. This is especially true of high-grade, rapidly growing glioblastoma multiforme (GBM), which account for nearly half of all gliomas. Current medical imaging techniques are unable to assess the full extent of diffuse glioma invasion, and its dynamics in vivo remain unclear. Mathematical modeling is therefore an ideal approach to enhancing the diagnosis and treatment of GBM. Using a model for glioma growth that has been shown to have prognostic significance as well as spatial accuracy in predicting disease distribution, progression, and recurrence, GBM aggressiveness can be described in terms of the diffuse invasion (net dispersal rate D) and proliferation (net rate ρ) of malignant cells. We investigate the influence of anatomical location on GBM growth kinetics as quantified by the model parameters and the radial velocity of tumor expansion. Routine magnetic resonance imaging (MRI) data from 140 newly diagnosed GBM patients was reviewed to categorically assess each tumor’s spatial relationship to central brain structures. Patient-specific model parameters and velocity of tumor expansion were calculated from tumor volumes and radii obtained via image analysis. Statistical results across all spatio-anatomic classifications showed no significant differences in range or variability for the distributions of D, ρ, D/ρ, or velocity, implying that the biological aggressiveness of GBM is independent of anatomic location. This is a direct contradiction to previous studies that have proposed a link between anatomical location and aggressive glioma behavior associated with poor patient survival. The observation that heterogeneity in aggressiveness does not appear to be biased as much as expected across spatial locations encourages future consideration of the effects of anatomic barriers and relative white matter location on differential growth patterns. |
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