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: 8531689
• 负责人: Paul E. Kinahan
• 金额: $ 80.61万
• 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/8531689
• 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; abstracting; 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

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.

抽象的 我们正在与晚期癌症整合肿瘤增殖和侵袭的数学模型 成像方法。我们将这种方法应用于胶质瘤，这些方法是侵略性且高度侵入性的 原发性脑肿瘤与沮丧的预后有关。由于组织的相对无法获取性 神经胶质瘤的临床管理是通过成像强烈指导的，因此工具整合了成像上的变化 迫切需要对癌症系统的动态理解。我们项目的目标是双重的： 通过治疗神经胶质瘤影响当前的临床挑战，并为开发的工具提供 这些具有挑战性的癌症的新疗法。 我们的第一个目标是根据每个患者的生长动力学制定基于图像的响应指标 肿瘤，如解剖成像（MR）和功能成像（PET和Advanced MR）所见。我们将 使用数学建模来开发特定于患者的未处理的虚拟成像控制（UVIC） 量化每个患者肿瘤系统的动力学。然后，我们将针对新型集合测试UVIC模型 20名胶质母细胞瘤患者中的每一个，每位分别以多个时间点的配对PET和MR图像。 配对图像将在整个治疗过程中获取，并与UVIC预测的 缺氧（FMISO-PET），坏死（T1-GD MR）和细胞（DWI MR）的图像。 该项目的第二个也是总体上的目标是将UVIC模型扩展到早期响应评估 临床试验中的个别患者。这将为开发急需的疗法提供工具 对神经胶质瘤更有效。项目中开发的方法可以扩展 完善生物建模，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。