Program Information
A Patient Specific Heterogeneous Tumor Model for Glioblastoma Multiforme
C McGuinness1*, R Noble2 , (1) ,,,(2) SLAC National Accelerator Laboratory, Menlo Park, CA
Presentations
PO-BPC-Exhibit Hall-11 (Saturday, March 7, 2015) Room: Exhibit Hall
Purpose:
Gliomas tumors proliferate and invade healthy brain tissue rapidly, yielding short life expectancies. Radiation therapy is commonly used against the disease, but gliomas are known to be radio-resistant, and almost always recur following treatment. Current radiotherapy protocols are based on the classic linear-quadratic radiobiological model describing tumor response to radiation, assuming a homogeneous (one-component) tumor. Gliomas are very heterogeneous, consisting of normoxic, hypoxic, and necrotic tissues, each responding differently to radiation. An enhanced linear-quadratic model which takes into account the different responses of the heterogeneous tumor 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 glioma tumors. Our model expands on the one component model developed by (Rockne,2010) by including normoxic, hypoxic, and necrotic components and radiosensitivity values for each component separately. Proliferation and diffusion parameters are extracted by contouring the tumor on two sets of pre-treatment MRI images and modeling it as a volume equivalent sphere.
Results:
We compared two examples of glioma tumors presented in (Rockne,2010) with our three component model and find better predictive capabilities for post-RT tumor volumes using the three component model. For the first case the one-component model predicts over predicts the effects of radiation resulting in a tumor diameter of 2.2cm while the three-component model predicts a diameter of 2.6cm in agreement with measured values. The second case similarly overpredicts the effects of radiation and shows poor agreement with the measured post-RT tumor volumes.
Conclusion:
The three component model accurately matches tumor growth dynamics derived from MRI images for two example cases. Spatial information about hypoxic, necrotic, and normoxic cell densities are derived from the model providing information needed to more intelligently prescribe dose distributions tailored to a specific patient’s tumor.
[Rockne,et.al.,PhysMedBio,2010]
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