Mathematical modelling of glioma growth: The use of Diffusion Tensor Imaging (DTI) data to predict the anisotropic pathways of cancer invasion
Mathematical modelling of glioma growth: The use of Diffusion Tensor Imaging (DTI) data to predict the anisotropic pathways of cancer invasion
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DOI：
10.1016/j.jtbi.2013.01.014
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发表时间：
2013-04-21
影响因子：
2
通讯作者：
Hillen, T.
中科院分区：
文献类型：
--
作者：
Painter, K. J.;Hillen, T.
某些癌症的不均匀生长可能会给治疗带来严重的并发症，这在神经胶质瘤中是一个特别严重的问题。许多实验结果表明，细胞沿着排列的神经纤维束定向运动促进了侵袭，神经纤维束构成了白质的重要组成部分。扩散张量成像（DTI）提供了一个可视化这种各向异性并深入了解潜在侵入途径的窗口。在本文中，我们基于入侵细胞沿纤维束的个体迁移途径开发了神经胶质瘤侵袭的细观模型。通过缩放，我们获得了一个宏观模型，使我们能够探索肿瘤的整体生长。为了将 DTI 数据与宏观模型中的参数联系起来，我们假设沿纤维束的方向引导由双峰 von Mises-Fisher 分布（单位球体上的正态分布）描述，并根据扩散张量中各向异性的方向性和程度进行参数化。我们在一个简单的神经胶质瘤生长模型中展示了结果，利用合成和真实的 DTI 数据集来揭示各向异性结构对侵袭的潜在关键作用。 (c) 2013 Elsevier Ltd. 保留所有权利。
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. (c) 2013 Elsevier Ltd. All rights reserved.