COMPREHENSIVE ADAPTIVE PLAN OPTIMIZATION

全面的自适应计划优化

• 批准号: 7082531
• 负责人: BENEDICK A FRAASS
• 金额: $ 25.93万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-04-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7082531
• 关键词: anatomy; birth; community; model

The fundamental goal of this work is to improve our ability to optimize the overall plan for each patient's treatment course ("comprehensive" treatment course optimization). Current planning methods make use of only a static single instance of the patient's anatomy (typically a treatment planning CT scan), and create an optimized treatment plan based on the clinical therapy prescription, using interactive or inverse planning optimization techniques (for Intensity Modulated Radiation Therapy (IMRT)). However, our knowledge of the patient and treatment goals is not static, but dynamic. Throughout the patient's course of treatment, we obtain new information on geometrical localization, inter- and intra-fraction motion, clinical response, and the precision to which we can predict uncertainties in the data used for planning. To better account for and utiltize this knowledge, the proposed research will develop and evaluate new paradigms for comprehensive optimization of the entire treatment course, evaluating potential improvements over the single-instance planning/optimization techniques which are widely used throughout the radiotherapy community. The project will develop a planning/optimization framework for individualized optimization which incorporates geometric, dosimetric, clinical and biological information as it becomes available (Aim 1), study improvements in single-stage optimization procedures for the multi-criteria problems encountered in therapy planning (Aim 2), and explicitly investigate multi-stage optimization methods for planning and delivery of the entire treatment course (Aim 3). This project will show that development of plan optimization strategies which 1) explicitly account for setup uncertainty and/or organ motion, 2) make use of updated clinical, biological, dosimetric and geometric information to refine the plan, and 3) model and optimize multi-stage adaptive therapy for the entire treatment course will better tailor the overall treatment plan to each individual patient and predict improvements over the current static inverse plan generated once for each patient.

这项工作的根本目标是提高我们的能力，以优化整体计划，为每一个病人的 治疗过程（“全面”治疗过程优化）。目前的规划方法利用 仅患者解剖结构的静态单个实例（通常是治疗计划CT扫描），并创建 基于临床治疗处方，使用交互式或逆向计划，优化治疗计划 优化技术（用于调强放射治疗（IMRT））。然而，我们对 患者和治疗目标不是静态的，而是动态的。在整个治疗过程中，我们 获得关于几何定位、分次间和分次内运动、临床反应的新信息，以及 我们可以预测用于规划的数据中的不确定性的精度。为了更好地解释 利用这些知识，拟议的研究将开发和评估新的范式， 优化整个治疗过程，评估单实例的潜在改善 计划/优化技术，广泛用于整个放射治疗界。的 项目将制定一个规划/优化框架，用于个性化优化，其中包括 几何学、剂量测定、临床和生物学信息（目标1），研究 改进单级优化程序的多准则问题中遇到的 治疗计划（目标2），并明确研究多阶段优化方法的规划和 整个治疗过程（目标3）。该项目将表明，计划优化的发展 策略1）明确说明设置不确定性和/或器官运动，2）利用更新的 临床、生物、剂量测定和几何信息，以完善计划，以及3）建模和优化 整个治疗过程的多阶段适应性治疗将更好地定制整体治疗计划， 每个患者并预测比当前静态逆计划生成一次的改进 每一个病人。