| Abstract | Gliomas are highly invasive brain tumors that account for nearly half of all primary brain tumors. Since current medical imaging techniques only detect a portion of the cancerous cells comprising these lesions, a reaction-diffusion model was developed to explore the extent of the tumor invasion below the threshold of detection of imaging and to predict glioma growth that can be tailored to an individual patient\\\\\\\'s tumor (Harpold et al, 2007). This computational model is based on two key elements: net rates of cell proliferation and cell diffusion. The diffusion coefficient is a function of the spatial variable that differentiates regions of grey and white matter to reflect the fact that glioma cells migrate faster in white matter than in grey matter (Swanson et al, 2000). In previous model implementations, cell diffusion was assumed to be constant and isotropic. However, it is commonly accepted that glioma cells migrate preferentially along the direction of white matter tracts, which are organized in myelinated axonal fibres that cause diffusion to be fastest parallel to, and slowest perpendicular to, the fibre tract (Sadlbauer et al, 2009). Additionally, the neuronal cell density in grey matter is higher than that in white matter, providing a faster route for glioma cell motility in white matter. To model anisotropic glioma cell migration in a 3D virtual brain, we use diffusion tensor imaging (DTI), a type of magnetic resonance image (MRI) that quantifies the directional orientation of white matter tracts. Thus, the DTI provides a tensor at each spatial location in the brain indicating the magnitude and direction that glioma cells tend to migrate. Patient-specific tissue classification maps are used in coordination with the DTI atlas to show how well the DTI based model predicts observed tumor growth. We also illustrate that anatomy and spatial heterogeneity can dictate the benefit of anisotropic simulations over isotropic simulations. I will compare the results of our isotropic and anisotropic simulations to quantify the overall differences in growth patterns between the two approaches and further, compare with the observed growth in individual patients to determine which technique more accurately predicts the growth of gliomas in vivo. A. Stadlbauer, et al. Detection of tumour invasion into the pyramidal tract in glioma patients with sensorimotor deficits by correlation of F-fluoroethyl-L-tyrosine PET and magnetic resonance diffusion tensor imaging. Acta Nueroch, 2009 H. L. P. Harpold, et al. The evolution of mathematical modeling of glioma growth and invasion. J Neuropath and Exp Neurol, 66(1):1-9, 2007 K. R. Swanson, et al. A Quantitative Model for Differential Motility of Gliomas in Grey and White Matter. Cell Prolif, 33: 317-329, 2000 S. Jbabdi, et al. Simulation of anisotropic growth of low-grade gliomas using diffusion tensor imaging. Mag Res in Med, 54:616–624, 2005 |
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