Glioblastoma Multiforme is the most common and most aggressive type of brain tumors grade four astrocytoma. Although accurate prediction of Glioblastoma borders and shape is absolutely essential for neurosurgeons, there are not many in silico platforms that can make such predictions. In the current study, an automatic patient-specific simulation of Glioblastoma growth is described. A finite element approach is used to analyze the magnetic resonance images from patients in the early stages of their tumors. For segmentation of the tumor, support vector machine method, which is an automatic segmentation algorithm, is used. Using in situ and in vivo data, the main parameters of tumor prediction and growth are estimated with high precision in proliferation-invasion partial differential equation, using genetic algorithm optimization method. The results show that for a C57BL mouse, the differences between the surface and perimeter of in vivo test and simulation prediction data, as objective function, are 3.7% and 17.4%, respectively.

中文翻译：

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用于全自动个性化脑肿瘤生长的新型in silico平台。

胶质母细胞瘤是最常见和最具侵略性的四级星形细胞瘤。尽管准确预测神经胶质母细胞瘤的边界和形状对于神经外科医生绝对是必不可少的，但是在计算机平台上并没有很多可以做出这种预测的平台。在当前的研究中，描述了胶质母细胞瘤生长的患者特异性自动模拟。有限元方法用于分析患者肿瘤早期的磁共振图像。为了分割肿瘤，使用了支持向量机方法，它是一种自动分割算法。利用原位和体内数据，利用遗传算法优化方法，在增殖-侵袭偏微分方程中高精度地估计了肿瘤预测和生长的主要参数。