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51. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (gmds)

10. - 14.09.2006, Leipzig

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A Mesoscopic Computational Model of Glioblastoma Growth

Meeting Abstract

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  • Haralampos Hatzikirou - ZIH/TU Dresden, Dresden
  • Karlo Schaller - Uniklinik Bonn, Bonn
  • Matthias Simons - Uniklinik Bonn, Bonn
  • Andreas Deustch - ZIH/TU Dresden, Dresden

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (gmds). 51. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. Leipzig, 10.-14.09.2006. Düsseldorf, Köln: German Medical Science; 2006. Doc06gmds125

The electronic version of this article is the complete one and can be found online at: http://www.egms.de/en/meetings/gmds2006/06gmds302.shtml

Published: September 1, 2006

© 2006 Hatzikirou et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.en). You are free: to Share – to copy, distribute and transmit the work, provided the original author and source are credited.

Outline

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Glioblastoma multiforme (GBM) is the most frequent and most malignant primary brain tumour. Sophisticated mathematical models may further augment our understanding of the dynamics of tumor growth, ultimately resulting in more effective therapies [1]. We develop a quantitative lattice-gas cellular automaton of GBM growth taking into account the two major aspects of this tumour, i.e. proliferative growth and invasion. The physical structure of the brain, in particular so called fibre tracks within the white matter, serve as a “highway” for tumor spreading. Malignant cells are described as moving particles, necrotic material explicitly as “necrotic particles”, and the brain as a porous medium. A local orientation gradient field models the fibre structure of the brain [2]. Recent advances in neuroimaging allow for visualization of these fibre tracks by Magnetic Resonance Imaging (MRI), so called DTI (Diffusion Tensor Imaging) [3]. Our goal is to incorporate these data into our model, as well as anatomical data derived from actual patients. We perform simulations of GBM growth and we analyse tumour invasion speed and pattern formation.

Outline

References

1.
H. Hatzikirou, A. Deutsch, C. Schaller, M. Simon, and K. Swanson (2005). Mathematical modelling of glioblastoma tumour development: a review. Math. Mod. Meth. Appl. Sc., 15(11):1779-1794
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Price S.J., Burnet N.G., Donovan T., Green H.A.L., Pena A., Antoun N.M., Pickard J.D., Carpenter T.A., Gillard J.H. (2003). Diffusion tensor imaging of brain tumours at 3 T: A potential tool for assessing white matter tract invasion? Clinical Radiology 58, 455-462
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Wurzel, M., Schaller, K. L., Simon, M. and Deutsch, A. (2005). Brain cancer invasion of brain tissue: guided by a prepattern. J. Theor. Medic. 6(1), 21-31.
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Swanson, K. R., Bridge, C., Murray, J. D. and Alvord, E. C. Jr. (2003b). Virtual and real brain tumours: Using mathematical modeling to quantify glioma growth and invasion. J. Neurol. Sci. 216(1), 1-10