Toward coupled multiphysics models of hemodynamics on leadership systems

描述（由申请人提供）：90%以上的癌症相关死亡是由癌症转移引起的，预测这些继发性肿瘤的位置仍然是一个难以捉摸的目标。研究表明，大约三分之二以上的癌症转移部位可以用原发和继发部位之间的血流模式来解释。准确了解细胞通过血管系统的运动和血管壁穿透的可能性，对于实现可靠地预测每个患者最可能发生继发性肿瘤的血管区域的最终目标可能至关重要。预测这些模式的患者特异性方法将有助于癌症分期，能够识别未知的原发部位，并为针对循环中的癌细胞的下一代治疗疗法提供信息。我们开发了一种多尺度计算流体动力学模型，用于评估基于图像的动脉几何中的血流动力学，并证明了其准确预测与疾病定位和进展相关的宏观量的能力。基于这些初步数据，我们假设：(1)细胞的可变形性影响血管系统的运动。(2)体外测量可以量化细胞特异性参数和生理状态的范围，这些参数和生理状态应用于评估可能的转移模式，并验证计算模型。(3)病例特异性模拟可以预测继发肿瘤的可能部位。我们提出三个具体目标来检验这些假设：目标1。考察细胞变形能力对CTC运动精确模型的影响，并确定该方法是否适用于全身尺度。目标2。验证大规模计算模型并预测转移部位的体外测量值。目标3。确定全身细胞特异性计算模型预测体内观察到的转移模式的能力。本应用程序的目标是开发一种方法，通过使用大规模并行血流动力学模拟，以前所未有的规模预测可能的癌症转移部位。

DESCRIPTION (provided by applicant): Cancer metastasis is responsible for more than 90% of cancer-related deaths and predicting the location of these secondary tumor sites remains an elusive goal. Studies have demonstrated that more than approximately two-thirds of cancer metastatic sites could be explained by the blood flow pattern between the primary and secondary sites. Development of a precise understanding of cell movement through the vascular system and the likelihood of penetration of the vessel wall is likely critical to achievin the ultimate goal of reliably predicting the vascular regions most likely to incur secondary tumor sites on a per-patient basis. A patient-specific method to predict these patterns will assist in cancer staging, enable identification of unknown primary sites, and inform next-generation treatment therapies that target cancer cells in circulation. We have developed a multiscale computational fluid dynamics model for assessing hemodynamics in image-based arterial geometries, and demonstrated its ability to accurately predict macroscopic quantities related to disease localization and progression. Based on this preliminary data, we hypothesize that (1) cell deformability impacts movement through the vasculature. (2) In vitro measurements can both quantify the range of cell-specific parameters and physiological states that should be used in assessing likely metastatic patterns and validate the computational models. (3) Case-specific simulations can predict likely secondary tumor sites. We propose three specific aims to test these hypotheses: Aim 1. Examine influence of cell deformability on the accurate models of CTC movement, and identify whether the method can be applied at the scale of the full-body. Aim 2. Validate large-scale computational models and predict in vitro measurements of values metastatic sites. Aim 3. Determine the ability of cell-specific computational models of the full-body to predict metastatic patterns observed in vivo. The goal of this application is to develop a method of predicting likely cancer metastasis sites through the use of massively parallel hemodynamic simulations at an unprecedented scale.