Novel Tools for Evaluation and Prediction of Radiotherapy Response in Individual

评估和预测个体放射治疗反应的新工具

• 批准号: 7730125
• 负责人: Kristin R Swanson
• 金额: $ 32.95万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-05 至 2014-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7730125
• 关键词: Aftercare; Autopsy; Benchmarking; Biological Markers; Biology; Brain; Cell Density; Cell Hypoxia; Cells; Clinical; Clinical Management; Computer Simulation; Data; Decision Making; Detection; Development; Diagnosis; Diffuse; Disease; Disease Progression; Dose; Evaluation; Evolution; Excision; Fractionation; Future; Gadolinium; Glioma; Goals; Growth; Human; Hypoxia; Iceberg; Image; Imaging Techniques; Individual; Investigation; Kinetics; Left; Life; Location; Magnetic Resonance Imaging; Malignant Glioma; Maps; Measures; Medical Imaging; Methodology; Methods; Metric; Microscopic; Modeling; Patient observation; Patients; Pattern; Peripheral; Play; Positron-Emission Tomography; Primary Brain Neoplasms; Process; Public Health; Radiation; Radiation therapy; Radio; Radiobiology; Recurrence; Relative (related person); Resistance; Role; Simulate; Specimen; Techniques; Testing; Therapeutic; Time; Tissues; Treatment Efficacy; Update; Validation; Weight; base; clinical application; clinically relevant; design; effective therapy; follow-up; imaging modality; improved; in vivo; in vivo Model; insight; mathematical model; model design; model development; movie; neoplastic cell; novel; prognostic; public health relevance; relating to nervous system; resistance factors; response; success; tool; treatment planning; tumor

DESCRIPTION (provided by applicant): Gliomas are uniformly fatal primary brain tumors, the diagnosis of which has been greatly impacted by improvements in medical imaging techniques over the last several decades. However, a significant gap remains between the obvious goal of more effective therapy and the present understanding of the dynamics of the tumor's proliferation and invasion in humans in vivo. That gap pivots on the concept that treatment of gliomas fails because of the diffuse dispersal of glioma cells throughout the neural axis even before diagnosis: the spatial and temporal evolution of which has been shown to be of quantitative and clinical importance as well as predicable with our current modeling methodology. Further, every imaging technique has a threshold of detection leaving much of the dispersed tumor invisible on imaging. The long-term objectives of this proposal are to provide new tools designed to quantify and predict the net proliferation and dispersal of glioma cells accurately enough to quantify and predict response to radiation therapy that are validated by and compared against information obtained through routine medical imaging of individual patients. The specific aims are to investigate the use of a spatio-temporal bio-mathematical model as a metric for glioma concentration, dispersal, response to radiation therapy, and location of post-treatment recurrence of individual gliomas in living patients in sufficient time to impact clinical decision making. This involves a gross but necessary assumption that medical imaging such as T1-weighted, gadolinium enhanced, T2-weighted MRI and PET imaging techniques directly correlate with disease distribution and biology. As the primary clinical window into disease progression, imaging techniques are used as benchmarks and metrics against which accuracy and success of model predictions are measured. Methods involve modern techniques and tools including, co-registration of clinical imaging, 3D radiation dose- distribution maps and the 4D patient-specific, model-simulated movie of the spatio-temporal growth and dispersal of each glioma. Comparisons are made between the model predicted invasion and therapy response patterns and that observed on follow-up imaging and, ultimately, autopsy. PUBLIC HEALTH RELEVANCE: The relevance of this proposal to public health lies in its applicability to any individual patient (and to the composition of any proposed group of "similar" patients) who has a primary brain tumor (glioma) and is being treated or is being considered for radiation therapy. Since disease progression and response to therapy are largely gauged by changes in current imaging techniques, there is an inherent limit to the clinical observation of a glioma to a "tip of the iceberg" view. Tools to predict and assess the dispersal (invasion) of gliomas cells throughout the brain in addition to the response to therapy which we cannot view on imaging is essential to the development of new and effective therapies for this uniformly fatal tumor. Specifically, as radiation therapy is targeted towards the dispersed glioma cells, peripheral to the imaging abnormality, it is necessary to calculate beyond the limits of imaging and to design mathematical models to dynamically assess that component of the tumor as well as take advantage of the tumor's proliferation rate in real time and in real patients.

描述(由申请人提供)： 胶质瘤是一种致命的原发脑肿瘤，在过去几十年里，医学成像技术的进步极大地影响了其诊断。然而，在更有效的治疗的明显目标与目前对体内肿瘤增殖和侵袭动力学的了解之间，仍然存在着显著的差距。这一差距基于这样一个概念，即胶质瘤的治疗失败是因为胶质瘤细胞甚至在诊断之前就在神经轴上扩散：其空间和时间演变已经被证明具有量化和临床重要性，并且可以用我们目前的建模方法进行预测。此外，每种成像技术都有一个检测阈值，使大部分分散的肿瘤在成像时看不见。这项提议的长期目标是提供新的工具，旨在足够准确地量化和预测胶质瘤细胞的净增殖和扩散，以量化和预测对放射治疗的反应，并与通过个别患者的常规医学成像获得的信息进行验证和比较。其具体目的是研究在足够的时间内使用时空生物数学模型作为衡量脑胶质瘤集中、扩散、对放射治疗的反应以及单个脑胶质瘤患者治疗后复发位置的指标，以影响临床决策。这涉及一个粗略但必要的假设，即医学成像，如T1加权、Gd增强、T2加权MRI和PET成像技术与疾病分布和生物学直接相关。作为了解疾病进展的主要临床窗口，成像技术被用作衡量模型预测的准确性和成功率的基准和指标。方法涉及现代技术和工具，包括临床成像的联合配准，3D辐射剂量分布图和每个胶质瘤时空生长和扩散的4D患者特有的模型模拟电影。模型预测的侵袭和治疗反应模式与后续成像和最终尸检观察到的模式进行了比较。公共卫生相关性：这项建议与公共健康的相关性在于它适用于任何患有原发脑瘤(胶质瘤)并正在接受或正在考虑进行放射治疗的个人患者(以及任何拟议的“类似”患者组的组成)。由于疾病的进展和对治疗的反应在很大程度上是由当前成像技术的变化来衡量的，因此对胶质瘤的临床观察存在固有的限制，只能看到冰山一角的观点。除了我们在影像上无法看到的治疗反应外，预测和评估胶质瘤细胞在整个大脑中的扩散(侵袭)的工具对于开发这种一致致命的肿瘤的新的有效治疗方法是必不可少的。具体地说，由于放射治疗的目标是分散的胶质瘤细胞，成像异常的外围，有必要进行超出成像限制的计算，并设计数学模型来动态评估肿瘤的该部分，以及实时和在真实患者中利用肿瘤的增殖率。