Patient-specific predictive modeling that integrates advanced cancer imaging

我们正在整合肿瘤增殖和侵袭的数学模型与预后不良的晚期相关。由于神经胶质瘤的组织相对难以接近，临床管理强烈依赖于影像学，因此迫切需要将影像学变化与对癌症系统的动态理解相结合的工具。我们项目的目标是双重的：通过治疗胶质瘤来影响当前的临床挑战，并为开发针对这些具有挑战性的癌症的新疗法提供工具。

We are integrating the mathematical modeling of tumor proliferation and invasion with advanced 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.