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胶质瘤，这种肿瘤具有侵袭性和高度侵袭性。 与预后不佳相关的原发脑瘤。由于组织相对难以接触， 神经胶质瘤的临床治疗很大程度上是由影像指导的，因此可以综合影像上的变化。 对癌症系统有一个动态的了解是非常需要的。我们项目的目标有两个： 冲击当前胶质瘤治疗的临床挑战，并为发展 针对这些具有挑战性的癌症的新疗法。 我们的第一个目标是开发基于图像的反应指标，基于每个患者的生长动力学 肿瘤，在解剖成像(MR)和功能成像(PET和高级MR)上都可以看到。我们会 使用数学建模来开发特定于患者的未经治疗的虚拟成像控制(UVIC) 量化每个患者肿瘤系统的动态。然后，我们将针对一组新的UVIC模型进行测试 在多个时间点(平均5个时间点)对20名胶质母细胞瘤患者进行配对的PET和MR图像分析。 在整个治疗过程中将获得成对的图像，并与UVIC预测的图像进行比较 低氧(FMISO-PET)、坏死(T1-Gd)和细胞密度(DWI)图像。 该项目的第二个目标，也是总体目标，是将UVIC模型扩展到早期响应评估 临床试验中的个别患者。这将为开发急需的治疗方法提供一个工具 对神经胶质瘤更有效。项目中开发的方法可以通过以下方式进行扩展 完善了生物学模型，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.