Patient-specific predictive modeling that integrates advanced cancer imaging

摘要 我们正在将肿瘤增殖和侵袭的数学模型与晚期癌症相结合 成像方法我们正在将这种方法应用于神经胶质瘤，这是侵略性和高度侵袭性的 原发性脑肿瘤伴发忧郁性脑肿瘤。由于组织的相对不可及性， 神经胶质瘤的临床管理强烈地由成像指导，因此工具整合成像上的变化 对癌症系统的动态理解是非常必要的。我们的项目有两个目标： 影响当前神经胶质瘤治疗的临床挑战，并为开发 新疗法来治疗这些具有挑战性的癌症。 我们的第一个目标是根据每个患者的生长动力学开发基于图像的反应指标。 肿瘤，如在解剖成像（MR）和功能成像（PET和高级MR）上所见。我们将 使用数学建模开发患者特定的未经治疗的虚拟成像控制（UVIC）， 量化每个病人肿瘤系统的动态然后，我们将测试UVIC模型对一个新的集 20名胶质母细胞瘤患者在多个时间点（平均5个时间点）的成对PET和MR图像。 将在整个治疗过程中采集配对图像，并与预测的UVIC进行比较 缺氧（FMISO-PET）、坏死（T1-Gd MR）和细胞构成（DWI MR）图像。 该项目的第二个总体目标是将UVIC模型扩展到早期反应评估 临床试验中的个体患者。这将为开发急需的疗法提供工具 对神经胶质瘤更有效。该项目中制定的方法可以通过以下方式加以推广： 完善生物学模型，也可以通过使用适当的生长来应用于其他癌症。 动力学模型

Abstract We are integrating the mathematical modeling of tumor proliferation and invasion with advanced cancer imaging methods. We are applying this approach to gliomas, which are aggressive and highly invasive primary brain tumors associated with dismal prognoses. Because of the relative inaccessibility of tissue, the clinical management of gliomas are strongly directed by imaging, thus tools integrating changes on imaging with a dynamic understanding of the cancer system are sorely needed. The goals of our project are twofold: To impact current clinical challenges with treatment of gliomas, and to provide tools for the development of new therapies for these challenging cancers. Our first goal is to develop image-based response metrics based on the growth kinetics of each patient's tumor, as seen on both anatomical imaging (MR) and functional imaging (PET and advanced MR). We will use mathematical modeling to develop a patient-specific Untreated Virtual Imaging Control (UVIC) that quantifies the dynamics of each patient's tumor system. We will then test the UVIC model against a novel set of paired PET and MR images at multiple time-points (five on average) for each of 20 glioblastoma patients. The paired images will be acquired throughout the course of therapy and compared with the UVIC predicted images of hypoxia (FMISO-PET), necrosis (T1-Gd MR) and cellularity (DWI MR). The second, and overall, goal of this project is to extend the UVIC model to the early response assessment of individual patients in clinical trials. This will provide a tool for the development of much-needed therapies that are more effective for gliomas. The methodologies developed in the project could be extended by refining the biological modeling, and could also be applied to other cancers by the use of appropriate growth kinetic models.