TH-AB-304-09: A XXXX -Specific Heterogeneous Radiobiological Model for Glioblastoma Multiforme.

@entity1 @entity39 @entity5 @entity11 @entity519
PURPOSE: @entity5 proliferate and invade healthy brain tissue rapidly, yielding short life expectancies. Radiation therapy is commonly used, but @entity11 are known to be radio-resistant, and almost always recur following treatment. Current radiotherapy protocols are based on the classic linear-quadratic radiobiological model, assuming a @entity5 . @entity11 are very heterogeneous, consisting of normoxic, @entity519 , and @entity39 tissues, each responding differently to radiation. An enhanced linear-quadratic model which takes into account the different responses of the heterogeneous @entity5 regions to radiation can guide treatment planning to optimize dose distributions to maximize the therapeutic effect. METHODS: We used a set of differential equations to model the growth of @entity5 . Our model expands on the one-component model developed by (Rockne,2010) by including normoxic, @entity519 , and @entity39 components and radiosensitivity values for each component. Proliferation and diffusion parameters are extracted by contouring the @entity5 on two sets of pre-treatment MRIs and modeling it as a volume equivalent sphere. RESULTS: We compared two examples of @entity5 presented in (Rockne,2010) with our three component model and find better predictive capabilities for post-RT @entity5 volumes using the three component model. In both cases, the one-component model over predicts the effects of radiation on the @entity5 core, while the three-component model more accurately fits the data by accounting for radioresistance of the @entity519 core. Additionally, we applied the model to a UCSF @entity1 with a recurring @entity5 and use the model to predict time to fatal @entity5 burden. CONCLUSION: The three-component model accurately matches @entity5 growth dynamics derived from MRIs for two example cases presented in literature, and one case from UCSF. Spatial information about @entity519 , @entity39 , and normoxic cell densities are derived from the model providing information needed to more intelligently prescribe dose distributions tailored to a specific @entity1 's @entity5 .[Rockne,et.al.,PhysMedBio,2010].
