Patient-specific predictive modeling that integrates advanced cancer imaging

集成先进癌症成像的患者特异性预测模型

• 批准号: 8657576
• 负责人: Paul E. Kinahan
• 金额: $ 12.1万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-21 至 2016-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8657576
• 关键词: Advanced Malignant Neoplasm; Biological Models; Calibration; Caring; Categories; Cellularity; Characteristics; Clinical; Clinical Management; Clinical Trials; Collection; Computer Simulation; Data; Databases; Development; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Effectiveness; Functional Imaging; Glioblastoma; Glioma; Goals; Growth; Hypoxia; Image; Imaging Device; Individual; Kinetics; Magnetic Resonance Imaging; Malignant Neoplasms; Measurement; Measures; Methodology; Methods; Metric; Modeling; Necrosis; Outcome; Patients; Positron-Emission Tomography; Primary Brain Neoplasms; Relative (related person); Research Personnel; Resources; Scanning; Simulate; System; Testing; Time; Tissues; Tumor Angiogenesis; Uncertainty; Weight; angiogenesis; base; cancer imaging; effective therapy; image reconstruction; imaging modality; insight; mathematical model; multimodality; novel; outcome forecast; predictive modeling; prognostic; prospective; response; simulation; three-dimensional modeling; tool; tool development; treatment effect; treatment response; tumor; tumor progression; virtual

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.

我们正在整合肿瘤增殖和侵袭的数学模型与先进的与令人沮丧的肿瘤。由于组织的相对不可接近性，胶质瘤的临床管理强烈地由成像指导，因此迫切需要将成像变化与对癌症系统的动态理解相结合的工具。我们的项目有两个目标：通过治疗神经胶质瘤来影响当前的临床挑战，并为这些具有挑战性的癌症开发新疗法提供工具。 我们的第一个目标是根据每个患者肿瘤的生长动力学开发基于图像的反应指标，如解剖成像（MR）和功能成像（PET和高级MR）所示。我们将使用数学建模来开发一个患者特定的未经治疗的虚拟成像控制（UVIC），量化每个患者的肿瘤系统的动态。然后，我们将测试UVIC模型对一组新的配对PET和MR图像在多个时间点（平均5）为20例胶质母细胞瘤患者。在整个治疗过程中采集配对图像，并与UVIC预测的缺氧（FMISO-PET）、坏死（T1-Gd MR）和细胞构成（DWI MR）图像进行比较。 该项目的第二个总体目标是将UVIC模型扩展到临床试验中个体患者的早期反应评估。这将为开发对胶质瘤更有效的急需疗法提供工具。该项目中开发的方法可以通过改进生物建模来扩展，也可以通过使用适当的生长动力学模型来应用于其他癌症。