论文标题
肿瘤生长的图像信息信息形成的Cahn-Hilliard Keller-Segel多相模型与血管生成
An image-informed Cahn-Hilliard Keller-Segel multiphase field model for tumor growth with angiogenesis
论文作者
论文摘要
我们开发了一种具有血管生成的新的四相肿瘤生长模型，该模型源自由可行的，坏死，液体和血管生成成分组成的弥漫性接口混合模型，并与两种代表完美稀释的营养剂和血管生成因子的无质量化学物质相结合。 This model is derived from variational principles complying with the second law of thermodynamics in isothermal situations, starting from biological constitutive assumptions on the tumor cells adhesion properties and on the infiltrative mechanics of tumor-induced vasculature in the tumor tissues, and takes the form of a coupled degenerate Cahn-Hilliard Keller-Segel system for the mixture components with reaction diffusion化学品的方程式。该模型通过神经影像学数据提供了信息，这些数据提供了有关患者特异性脑几何形状和组织微观结构，不同肿瘤成分的分布，白质纤维方向以及脉管密度的信息。我们描述了特定而健壮的预处理步骤，以从神经影像数据中提取定量信息，并构建一个计算平台，以特定于患者的方式解决该模型。我们引入了模型的有限元近似值，该模型保留了连续溶液的定性特性。最后，我们显示了由多形胶质母细胞瘤影响的患者特异性肿瘤演变的模拟结果，考虑了手术前的两个不同的测试用例，对应于肿瘤内部养分较高或低营养的情况，以及手术后的检查病例。我们表明，我们的模型正确预测了肿瘤分布的总体扩展和血管生成过程的强度，为协助临床医生正确评估治疗结果并设计最佳患者特异性治疗时间表铺平了道路。
We develop a new four-phase tumor growth model with angiogenesis, derived from a diffuse-interface mixture model composed by a viable, a necrotic, a liquid and an angiogenetic component, coupled with two massless chemicals representing a perfectly diluted nutrient and an angiogenetic factor. This model is derived from variational principles complying with the second law of thermodynamics in isothermal situations, starting from biological constitutive assumptions on the tumor cells adhesion properties and on the infiltrative mechanics of tumor-induced vasculature in the tumor tissues, and takes the form of a coupled degenerate Cahn-Hilliard Keller-Segel system for the mixture components with reaction diffusion equations for the chemicals. The model is informed by neuroimaging data, which give informations about the patient-specific brain geometry and tissues microstructure, the distribution of the different tumor components, the white matter fiber orientations and the vasculature density. We describe specific and robust preprocessing steps to extract quantitative informations from the neuroimaging data and to construct a computational platform to solve the model on a patient-specific basis. We introduce a finite element approximation of the model which preserve the qualitative properties of the continuous solutions. Finally, we show simulation results for the patient-specific tumor evolution of a patient affected by GlioBlastoma Multiforme, considering two different test cases before surgery, corresponding to situations with high or low nutrient supply inside the tumor, and a test case after surgery. We show that our model correctly predicts the overall extension of the tumor distribution and the intensity of the angiogenetic process, paving the way for assisting the clinicians in properly assessing the therapy outcomes and in designing optimal patient-specific therapeutic schedules.
