A multiscale model for glioma spread including cell-tissue interactions and proliferation

  • Received: 01 July 2015 Accepted: 29 June 2018 Published: 25 December 2015
  • MSC : Primary: 92C17, 92C50; Secondary: 35Q92.

  • 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.

    Citation: Christian Engwer, Markus Knappitsch, Christina Surulescu. A multiscale model for glioma spread including cell-tissue interactions and proliferation[J]. Mathematical Biosciences and Engineering, 2016, 13(2): 443-460. doi: 10.3934/mbe.2015011

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  • 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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    [1] SIAM Journal on Numerical Analysis, 39 (2002), 1749-1779.
    [2] Computing, 82 (2008), 103-119.
    [3] Computing, 82 (2008), 121-138.
    [4] Blood, 105 (2005), 3561-3568.
    [5] Mathematical and Computer Modelling, 51 (2010), 441-451.
    [6] Math. Models Methods Appl. Sci., 22 (2012), 1130001, 37 pp.
    [7] Math. Models Methods Appl. Sci., 23 (2013), 1861-1913.
    [8] Journal of Neuro-Oncology, 63 (2003), 109-116.
    [9] Mathematical Modelling of Natural Phenomena, 7 (2012), 105-135.
    [10] in The gliomas (eds. M. Berger and C. Wilson), W.B. Saunders Company, Philadelphia, (1999), 210-225.
    [11] J. of Clinical Neurosci., 14 (2007), 1041-1048.
    [12] Journal of Neuro-Oncology, 34 (1997), 37-59.
    [13] Journal of Neuro-Oncology, 70 (2004), 217-228.
    [14] IEEE Transactions on Medical Imaging, 28 (2009), 269-286.
    [15] Ph.D. thesis, Université Nice-Sophia Antipolis, 2008.
    [16] Journal of Math. Biol., 71 (2015), 551-582.
    [17] IMA J. Math. Medicine and Biol., 32 (2015), doi:10.1093/imammb/dqv030, 2015.
    [18] Multiscale Modeling and Simulation, 3 (2005), 362-394.
    [19] IMA Journal of Numerical Analysis, 29 (2009), 235-256.
    [20] Neuro-Oncology, 12 (2010), 466-472.
    [21] International Journal of Cancer, 67 (1996), 275-282.
    [22] Neurosurgery, 39 (1996), 235-252.
    [23] SIAM Journal for Applied Mathematics, 61 (2000), 43-73.
    [24] Cell, 144 (2011), 646-674.
    [25] Math Med Biol, 29 (2012), 49-65.
    [26] Europ. J. Appl. Math., 14 (2003), 613-636.
    [27] Journal of Mathematical Biology, 53 (2006), 585-616.
    [28] SIAM Journal on Applied Mathematics, 61 (2000), 751-775.
    [29] Cancer Res., 68 (2008), 650-656.
    [30] Mathematical Biosciences and Engineering, 8 (2011), 575-589.
    [31] Mathematical Models and Methods in Applied Sciences, 23 (2012), 1150017, 25 pp.
    [32] Oxford University Press, 1993.
    [33] Genes Dev., 23 (2009), 397-418.
    [34] Mathematical Models and Methods in Applied Sciences, 24 (2014), 2383-2436.
    [35] Journal of Neurosurgery, 18 (1961), 636-644.
    [36] Discr. Cont. Dyn. Syst. B, 20 (2015), 189-213.
    [37] Journal of Math. Anal. Appl., 408 (2013), 597-614.
    [38] in Adhesion Molecules and Chemokynes in Lymphocyte Trafficking (ed. A. Hamann), Harwood Acad. Publ. (1997), 55-88.
    [39] J. Theoretical Biol., 323 (2013), 25-39.
    [40] The Journal of Cell Biology, 179 (2007), 777-791.
    [41] IMA Journal of Applied Mathematics, 80 (2015), 1300-1321.
    [42] SIAM J. Math. Analysis, 46 (2014), 1969-2007.
    [43] Neurocarciology, 46 (2004), 339-350.
    [44] Journal of Mathematical Biology, 58 (2009), 819-844.
    [45] Magnetic resonance in Medicine, 52 (2004), 1358-1372.
    [46] Neuron, 40 (2003), 885-895.
    [47] Frontiers in Bioscience, 4 (1999), 188-199.
    [48] Journal of neural engineering, 11 (2014), 016002, 14pp.
    [49] SIAM Journal on Numerical Analysis, 15 (1978), 152-161.
    [50] Neuro-Oncology, 4 (2002), 278-299.
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