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; 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）： 神经胶质瘤是统一致命的原发性脑肿瘤，在过去的几十年中，医学成像技术的改善极大地影响了其诊断。然而，在更有效的治疗的明显目标与当前对肿瘤增殖和体内人类侵袭动力学的理解之间的明显差距仍然存在。由于神经胶质瘤细胞在诊断之前的整个神经轴的弥漫性传播，因此，gap的差距是关于胶质瘤的治疗失败的概念的差异：其空间和时间演变已被证明具有定量和临床重要性，以及我们当前的建模方法具有预测性。此外，每种成像技术都有一个检测阈值，使许多分散的肿瘤在成像上看不见。该提案的长期目标是提供旨在量化和预测神经胶质瘤细胞的净增殖和分散的新工具，足以量化和预测对放射疗法的反应，并通过对单个患者的常规医学成像获得的信息进行验证和比较。具体目的是调查使用时空生物数学模型作为神经胶质瘤浓度，分散，对放射治疗的反应以及在足够时间影响临床决策的足够时间中，单个神经胶质瘤的治疗后复发的位置。这涉及一个总体但必要的假设，即医学成像，例如T1加权，增强的T2加权MRI和PET成像技术与疾病分布和生物学直接相关。作为疾病进展的主要临床窗口，成像技术被用作测量模型预测的准确性和成功的基准和指标。方法涉及现代技术和工具，包括临床成像的共同注册，3D辐射剂量分布图以及每种神经胶质瘤的时空生长和分散的4D患者特异性，模型模拟的电影。该模型预测入侵和治疗反应模式之间进行了比较，并在后续成像和最终尸检中观察到。公共卫生相关性：该提案与公共卫生的相关性在于其对任何患有原发性脑肿瘤（神经胶质瘤）的任何患者（以及任何拟议中的“类似”患者组成）​​的适用性，并且正在接受治疗或正在接受放射治疗。由于疾病的进展和对治疗的反应在很大程度上通过当前成像技术的变化来衡量，因此对神经胶质瘤的临床观察到“冰山一角”视图的固有限制。除了对疗法的反应外，我们无法在成像上观察到的胶质瘤细胞的分散（侵袭）的工具对于为这种统一致命的肿瘤开发新的有效疗法至关重要。具体而言，由于放射疗法针对分散的神经胶质瘤细胞（对成像异常的外围），因此有必要计算超出成像限制和设计数学模型以动态评估肿瘤的成分以及实时和实际患者的肿瘤的增殖率。