Optimization of High Dose Conformal Therapy

高剂量适形治疗的优化

• 批准号: 9065662
• 负责人: THEODORE S LAWRENCE
• 金额: $ 274.85万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-02-01 至 2019-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9065662
• 关键词: Biological; Biological Assay; Characteristics; Clinical; Clinical Research; Conformal Radiotherapy; Data; Dose; Image; Individual; Instruction; Leadership; Liver; Localized Malignant Neoplasm; Lung; Methods; Normal tissue morphology; Organ; Patients; Physiological; Population; Radiation therapy; Resistance; Resource Sharing; Safety; Site; Software Design; System; Testing; Toxic effect; Tumor Tissue; base; cancer therapy; design; expectation; improved; improved outcome; individual patient; patient population; population based; predictive modeling; programs; response; support tools; therapy design; treatment response; tumor

DESCRIPTION (provided by applicant): Cancer treatments are now designed to uniformly treat the "average" patient. The "safe" dose is dictated by the most sensitive 5-15% of the patient population. However, this population-based approach is also accompanied by low expectations of tumor control. Similarly, radiation treatments have been designed to treat the tumor uniformly, although we know from biological and clinical studies that tumors have a heterogeneous response to treatment. We hypothesize that a treatment design that Integrates pretreatment patient factors with an adaptation strategy that uses the first part of treatment to assess tumor and normal tissue sensitivity will permit us to optimize therapy for the individual patient rather than giving a population-averaged treatment that is likely to be less effective. Thi proposal comprises 4 scientific Projects and 4 shared resource Cores within an integrated plan of action: Project 1 and 2 will focus on tumors within the liver and lung. These two sites were chosen for clinical demonstration as their current control rates are low and, as the volume of the harboring normal tissue organ irradiated is the critical factor in toxicity, they offer a great opportunity for adaptation. We will use physiological imaging and other methods to individualize dose redistribution in normal tissues to lower toxicity while also heterogeneously irradiating resistant tumor sub-volumes to improve outcome. Project 3 will establish the spatial and temporal precision of imaging-based methods for both tumor targeting and normal tissue response. Project 4 will develop, investigate, and improve decision support tools to take advantage of predictive models for adaptive therapy patient management. Core A provides administrative and statistical support, Core B will support the clinical therapy planning and delivery, Core C will provide analysis of all Imaging data and Core D will handle software design. We feel that we are developing a new paradigm for radiation therapy and recognize only a few other programs in the world with our ability to develop a system that combines biological assays of toxicity, imaging, planning, delivery, and clinical leadership to safely adapt therapy based on each individual's characteristics and response to therapy.

描述（由申请人提供）：癌症治疗现在被设计为统一治疗“普通”患者。“安全”剂量由最敏感的5-15%患者群体决定。然而，这种基于人群的方法也伴随着对肿瘤控制的低期望。同样，放射治疗被设计成均匀地治疗肿瘤，尽管我们从生物学和临床研究中知道肿瘤对治疗有异质性反应。我们假设，将治疗前患者因素与使用治疗的第一部分评估肿瘤和正常组织敏感性的适应策略相结合的治疗设计将允许我们优化个体患者的治疗，而不是给予可能不太有效的人群平均治疗。该提案包括一个综合行动计划中的4个科学项目和4个共享资源核心：项目1和2将侧重于肝脏和肺部的肿瘤。选择这两个部位进行临床验证是因为它们目前的控制率较低，并且由于受照射的正常组织器官的体积是毒性的关键因素，因此它们提供了很好的适应机会。我们将使用生理成像和其他方法来个体化正常组织中的剂量再分布以降低毒性，同时还对抗性肿瘤子体积进行非均匀辐照以改善结果。项目3将建立用于肿瘤靶向和正常组织反应的基于成像的方法的空间和时间精度。项目4将开发、研究和改进决策支持工具，以利用预测模型进行适应性治疗患者管理。核心A提供管理和统计支持，核心B将支持临床治疗计划和实施，核心C将提供所有成像数据的分析，核心D将处理软件设计。我们认为我们正在开发一种新的放射治疗模式，并且认识到世界上只有少数其他项目能够开发一种系统，该系统将毒性，成像，计划，交付和临床领导的生物测定相结合，以根据每个人的特征和对治疗的反应安全地调整治疗。