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: 8699715
• 负责人: Paul E. Kinahan
• 金额: $ 83.07万
• 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/8699715
• 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具有侵袭性和高度侵袭性的神经胶质瘤 原发性脑肿瘤与不良预后相关。由于组织相对难以接近， 神经胶质瘤的临床管理很大程度上受影像学指导，因此工具整合了影像学的变化 迫切需要对癌症系统有动态的了解。我们项目的目标有两个： 应对当前神经胶质瘤治疗的临床挑战，并为开发神经胶质瘤提供工具 针对这些具有挑战性的癌症的新疗法。 我们的首要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，如解剖成像 (MR) 和功能成像（PET 和高级 MR）所示。我们将 使用数学模型开发针对患者的未经治疗虚拟成像控制 (UVIC) 量化每个患者肿瘤系统的动态。然后我们将针对一组新颖的模型来测试 UVIC 模型 20 名胶质母细胞瘤患者中每人在多个时间点（平均 5 个）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 预测进行比较 缺氧 (FMISO-PET)、坏死 (T1-Gd MR) 和细胞结构 (DWI MR) 图像。 该项目的第二个总体目标是将 UVIC 模型扩展到早期响应评估 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。该项目中开发的方法可以通过以下方式扩展： 完善生物模型，并且还可以通过使用适当的生长应用于其他癌症 动力学模型。