A multiscale model for glioma spread including cell-tissue interactions and proliferation
Glioma is a broad class of brain and spinal cord tumors arising from glia cells, which are the main brain cells that can develop into neoplasms.They are highly invasive and lead to irregular tumor margins which are not precisely identifiable by medical imaging, thus rendering a precise enough resect...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIMS Press
2015-12-01
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| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2015011 |
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| Summary: | Glioma is a broad class of brain and spinal cord tumors arising from glia cells, which are the main brain cells that can develop into neoplasms.They are highly invasive and lead to irregular tumor margins which are not precisely identifiable by medical imaging, thus rendering a precise enough resection very difficult.The understanding of glioma spread patterns is hence essential for both radiological therapy as well as surgical treatment.In this paper we propose a multiscale model for glioma growth including interactions of the cells with the underlying tissuenetwork, along with proliferative effects. Our current accounting for two subpopulations of cells to accomodate proliferation according to the go-or-grow dichtomotyis an extension of the setting in [16].As in that paper, we assume that cancer cells use neuronal fiber tracts as invasive pathways. Hence, the individualstructure of brain tissue seems to be decisive for the tumor spread. Diffusion tensor imaging (DTI) is able to provide suchinformation, thus opening the way for patient specificmodeling of glioma invasion. Starting from a multiscale model involving subcellular (microscopic) and individual (mesoscale)cell dynamics, we perform a parabolic scaling to obtain an approximating reaction-diffusion-transport equation on themacroscale of the tumor cell population. Numerical simulations based on DTI data are carried out in order to assess theperformance of our modeling approach. |
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| ISSN: | 1551-0018 |