Robust optimization of intensity modulated proton therapy
Robust optimization of intensity modulated proton therapy
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DOI：
10.1118/1.3679340
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发表时间：
2012-02-01
期刊：
影响因子：
3.8
通讯作者：
Mohan, Radhe
中科院分区：
文献类型：
--
作者：
Liu, Wei;Zhang, Xiaodong;Mohan, Radhe
目的：调强质子治疗（IMPT）对射程不确定性和由设置变化引起的不确定性高度敏感。基于计划靶体积（PTV）优化的IMPT的常规逆治疗计划通常不足以确保治疗计划的鲁棒性。在本文中，一种方法，考虑到计划优化过程中的不确定性是用来减轻不确定性的影响IMPT。方法：作者使用所谓的“最坏情况下的鲁棒优化”，使IMPT计划的鲁棒性，在面对的不确定性。对于每次迭代，计算9个不同的剂量分布-沿着前后（A-P）、横向（R-L）和上下（S-I）方向的+/-设置不确定性、+/-范围不确定性和标称剂量分布各一个。通过将9个剂量中的最低剂量分配给临床靶体积（CTV）中的每个体素，并将最高剂量分配给CTV外的每个体素，获得最差情况剂量分布。从概念上讲，最差情况剂量分布的使用类似于传统计划中基于PTV使用实现的剂量分布。使用该最差情况剂量分布计算给定迭代的目标函数值。所使用的目标函数已被扩展到进一步约束的目标剂量inhomogeneity.Results：最坏情况下的鲁棒优化方法被应用到肺的情况下，颅底的情况下，和前列腺的情况下。与IMPT计划使用传统的方法优化的PTV的基础上相比，我们的方法产生的计划是相当不敏感的范围和设置的不确定性。这里提出的工作的一个有趣的发现是，除了降低对不确定性的敏感性之外，鲁棒优化还导致与基于PTV的优化相比改善治疗计划的最优性。这反映在减少计划分数和正常组织剂量相同的覆盖范围的目标volume.Conclusions时，受到不确定性：作者发现，最坏情况下的强大的优化提供了强大的目标覆盖范围，而不牺牲，甚至可能提高，正常组织的保留。我们的研究结果证明了鲁棒优化的重要性。作者断言，所有的IMPT计划都应该进行鲁棒优化。(C)2012年美国医学物理学家协会。[ DOI：10.1118/1.3679340]
Purpose: Intensity modulated proton therapy (IMPT) is highly sensitive to range uncertainties and uncertainties caused by setup variation. The conventional inverse treatment planning of IMPT optimized based on the planning target volume (PTV) is not often sufficient to ensure robustness of treatment plans. In this paper, a method that takes the uncertainties into account during plan optimization is used to mitigate the influence of uncertainties in IMPT.Methods: The authors use the so-called "worst-case robust optimization" to render IMPT plans robust in the face of uncertainties. For each iteration, nine different dose distributions are computed-one each for +/- setup uncertainties along anteroposterior (A-P), lateral (R-L) and superior-inferior (S-I) directions, for +/- range uncertainty, and the nominal dose distribution. The worst-case dose distribution is obtained by assigning the lowest dose among the nine doses to each voxel in the clinical target volume (CTV) and the highest dose to each voxel outside the CTV. Conceptually, the use of worst-case dose distribution is similar to the dose distribution achieved based on the use of PTV in traditional planning. The objective function value for a given iteration is computed using this worst-case dose distribution. The objective function used has been extended to further constrain the target dose inhomogeneity.Results: The worst-case robust optimization method is applied to a lung case, a skull base case, and a prostate case. Compared with IMPT plans optimized using conventional methods based on the PTV, our method yields plans that are considerably less sensitive to range and setup uncertainties. An interesting finding of the work presented here is that, in addition to reducing sensitivity to uncertainties, robust optimization also leads to improved optimality of treatment plans compared to the PTV-based optimization. This is reflected in reduction in plan scores and in the lower normal tissue doses for the same coverage of the target volume when subjected to uncertainties.Conclusions: The authors find that the worst-case robust optimization provides robust target coverage without sacrificing, and possibly even improving, the sparing of normal tissues. Our results demonstrate the importance of robust optimization. The authors assert that all IMPT plans should be robustly optimized. (C) 2012 American Association of Physicists in Medicine. [ DOI: 10.1118/1.3679340]