Analytical probabilistic treatment planning

分析概率治疗计划

• 批准号: 265744405
• 负责人: Dr. Mark Bangert
• 金额: --
• 依托单位: Fachbereich IV: Informatik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2014
• 资助国家: 德国
• 起止时间: 2013-12-31 至 2017-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/265744405?language=en
• 关键词: Analytical; probabilistic; treatment; planning

We propose a three year research program extending our joint work regarding analytical probabilistic modeling (APM) for radiation therapy treatment planning.In current clinical practice, radiation therapy treatment planning does not explicitly model sources of uncertainty such as inter- and intrafractional motion, patient immobilization, and delineation errors. After geometric uncertainties have been reduced as far as possible through image guidance, uncertainties are only implicitly accounted for with standardized margin recipes. Mathematical approaches for patient specific uncertainty quantification and minimization have never found broad clinical application due to conceptual limitations and run time issues. Generic margin approaches compromise the quality of radiation treatments for individual patients. A uniform margin may cause unnecessary exposure of healthy tissue and the margin concept does not prevent "cold spots" within the tumor.We want to close this gap of inadequate clinical uncertainty modeling and develop APM into a computational research framework for probabilistic radiation therapy treatment planning for protons and carbon ions.APM enables the closed form computation of the expectation value and the (co-)variance of intensity-modulated dose distributions (and in principle all higher-order moments). This closed algebraic form provides central advantages for uncertainty quantification and minimization.1. The quality of all numerical simulations can be directly controlled and evaluated; APM is not compromised by algorithmic uncertainties, e.g. statistical fluctuations2. APM explicitly incorporates complex correlation models of the uncertainties and the non-trivial dosimetric interplay of random and systematic uncertainties in fractionated radiation therapy3. APM allows for the closed form definition, differentiation, and thence efficient optimization of existing and novel probabilistic objectives4. The output of APM is a Gaussian probability density which enables the propagation of uncertainty between related computationsWith the proposed research we pursue two goals:1. Exploit the reduction in computational complexity achieved through the analytical formulation of the treatment planning problem, to enable efficient robust planning of carbon ion treatments including uncertainties in the associated biological models.2. Enable closed-form propagation of uncertainty into composite treatment plan quality indicators. This will provide clinicians with error bars on quantities directly relevant for their decision and subsequently enable inverse planning through direct specification of probabilistic treatment plan features.Through accounting for potential discrepancies between the simulated and the actually delivered treatment plan we want to help reduce both local failure and normal tissue complication during radiation therapy.

我们提出了一个为期三年的研究计划，扩展我们的联合工作，关于分析概率建模（APM）的放射治疗治疗planning.In目前的临床实践中，放射治疗治疗计划没有明确的模型来源的不确定性，如间和intrafraction运动，病人固定，和描绘错误。在通过图像引导尽可能减少几何不确定性之后，不确定性仅通过标准化的边缘配方被隐含地考虑。由于概念上的限制和运行时间问题，用于患者特定不确定性量化和最小化的数学方法从未发现广泛的临床应用。一般的边缘方法损害了个体患者的放射治疗质量。均匀的边界可能会导致健康组织不必要的暴露，并且边界概念不能防止肿瘤内的“冷点”。我们希望弥补临床不确定性建模不足的这一差距，并将APM发展成为质子和碳离子概率放射治疗治疗计划的计算研究框架。APM使强度的期望值和（协）方差的封闭形式计算成为可能。调制剂量分布（以及原则上所有高阶矩）。这种封闭的代数形式为不确定性量化和最小化提供了核心优势。所有数值模拟的质量都可以直接控制和评估; APM不会受到算法不确定性的影响，例如统计波动2。APM明确纳入了不确定性的复杂相关模型，以及分次放射治疗中随机和系统不确定性的非平凡剂量相互作用3。APM允许封闭形式的定义，分化，从而有效地优化现有的和新的概率目标4。APM的输出是一个高斯概率密度，这使得相关计算之间的不确定性传播与建议的研究，我们追求两个目标：1。利用通过治疗计划问题的分析公式实现的计算复杂性的降低，以实现碳离子治疗的有效鲁棒计划，包括相关生物模型中的不确定性。使不确定性能够以封闭形式传播到复合治疗计划质量指标中。这将为临床医生提供与其决策直接相关的数量上的误差条，并随后通过直接指定概率治疗计划特征来实现逆向计划。通过考虑模拟和实际交付的治疗计划之间的潜在差异，我们希望帮助减少放射治疗期间的局部失败和正常组织并发症。