title: Mathematical modelling of glioma growth: the use of Diffusion Tensor Imaging (DTI) data to predict the anisotropic pathways of cancer invasion.
abstract: The nonuniform growth of certain forms of cancer can present significant complications for their treatment, a particularly acute problem in gliomas. A number of experimental results have suggested that invasion is facilitated by the directed movement of cells along the aligned neural fibre tracts that form a large component of the white matter. Diffusion tensor imaging (DTI) provides a window for visualising this anisotropy and gaining insight on the potential invasive pathways. In this paper we develop a mesoscopic model for glioma invasion based on the individual migration pathways of invading cells along the fibre tracts. Via scaling we obtain a macroscopic model that allows us to explore the overall growth of a tumour. To connect DTI data to parameters in the macroscopic model we assume that directional guidance along fibre tracts is described by a bimodal von Mises-Fisher distribution (a normal distribution on a unit sphere) and parametrised according to the directionality and degree of anisotropy in the diffusion tensors. We demonstrate the results in a simple model for glioma growth, exploiting both synthetic and genuine DTI datasets to reveal the potentially crucial role of anisotropic structure on invasion.
authors: Department of Mathematics and Maxwell Institute for Mathematical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK. K.Painter@hw.ac.uk
authors: K J
authors: KJ
authors: Painter
authors: T
authors: T
authors: Hillen
mesh_terms: D016880:Anisotropy; D001932:Brain Neoplasms / Q000473:pathology*; D002465:Cell Movement; D049109:Cell Proliferation; D003198:Computer Simulation; D056324:Diffusion Tensor Imaging / Q000379:methods*; D005910:Glioma / Q000473:pathology*; D006801:Humans; D008954:Models, Biological; D009361:Neoplasm Invasiveness