Towards fast and robust 4D optimization for moving tumors with scanned proton therapy
Towards fast and robust 4D optimization for moving tumors with scanned proton therapy
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DOI：
10.1002/mp.13850
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发表时间：
2019-10-29
期刊：
影响因子：
3.8
通讯作者：
Sterpin, Edmond
中科院分区：
文献类型：
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作者：
Buti, Gregory;Souris, Kevin;Sterpin, Edmond
目的鲁棒优化正成为针对各种处理不确定性生成鲁棒方案的金标准。今天，大多数稳健的优化策略使用一组实用的治疗方案（所谓的不确定性集），由每个考虑的不确定性源（如肿瘤运动、设置和图像转换错误）的最大误差组合组成。这种方法提出了两个关键问题。首先，考虑的场景子集是不必要的不可能的，这可能会潜在地损害计划质量。其次，由此产生的大不确定性集导致计划计算时间长，这限制了作为标准临床工具的鲁棒优化的潜力。为了解决这些问题，引入了一种方法，该方法能够预先选择一组有限的相关处理错误场景。方法考虑系统设置误差、图像转换误差和呼吸系统肿瘤运动的不确定性。定义了考虑上述不确定性源联合概率的四维等概率超曲面。仅考虑位于预定义的4D超曲面上的场景，保证了不确定性集的统计一致性。在这方面，我们选择了12种情况，包括呼吸过程中肿瘤的最大空间位移。随后，考虑其他场景（从上述4D超表面采样），以覆盖任何估计的残差误差。测试了两种不同的情景选择程序：(a)最大位移（MD）方法，只考虑了12个缩放最大位移情景；(b)最大位移和剩余距离（MDR）方法，除了缩放最大位移情景外，还考虑了额外的最大距离不确定性情景。通过综合蒙特卡罗稳健性评估，对5例肺癌患者进行了方法检验。结果应用MD方法可获得78%的计划计算时间增益，同时在最坏情况下的目标稳健性为D95，大于规定剂量的95%。此外，MD方法具有完全自动化的潜力，这使它成为快速自动计划工作流的有希望的候选者。MDR方法产生的计划具有出色的目标鲁棒性（即使在最坏情况下，D99也大于规定剂量的95%），同时仍然获得57%的显著计划计算时间增益。结论开发了两种场景选择程序，在不影响计划质量或鲁棒性的情况下，显著减少了计划计算时间和内存消耗。
Purpose Robust optimization is becoming the gold standard for generating robust plans against various kinds of treatment uncertainties. Today, most robust optimization strategies use a pragmatic set of treatment scenarios (the so-called uncertainty set) consisting of combinations of maximum errors, of each considered uncertainty source (such as tumor motion, setup and image-conversion errors). This approach presents two key issues. First, a subset of considered scenarios is unnecessarily improbable which could potentially compromise the plan quality. Second, the resulting large uncertainty set leads to long plan computation times, which limits the potential for robust optimization as a standard clinical tool. In order to address these issues, a method is introduced which is able to preselect a limited set of relevant treatment error scenarios. Methods Uncertainties due to systematic setup errors, image-conversion errors and respiratory tumor motion are considered. A four-dimensional (4D)-equiprobability hypersurface is defined, which takes into account the joint probabilities of the above-mentioned uncertainty sources. Only scenarios that lie on the predefined 4D hypersurface are considered, guaranteeing statistical consistency of the uncertainty set. In this regard, twelve scenarios are selected that cover maximum spatial displacements of the tumor during breathing. Subsequently, additional scenarios are considered (sampled from the aforementioned 4D hypersurface) in order to cover any estimated residual range errors. Two different scenario-selection procedures were tested: (a) the maximum displacements (MD) method that only considers twelve scaled maximum displacement scenarios and (b) maximum displacements and residual range (MDR) method which, in addition to the scaled maximum displacement scenarios, considers additional maximum range uncertainty scenarios. The methods were tested for five lung cancer patients by performing comprehensive Monte Carlo robustness evaluations. Results A plan computation time gain of 78% is achieved by applying the MD method, whilst obtaining a target robustness of D95 larger than 95% of the prescribed dose, for the worst-case scenario. Additionally, the MD method has the potential to be fully automatic which makes it a promising candidate for fast automatic planning workflows. The MDR method produced plans with excellent target robustness (D99 larger than 95% of the prescribed dose, even for the worst-case scenario), whilst still obtaining a significant plan computation time gain of 57%. Conclusions Two scenario-selection procedures were developed which achieved significant reduction of plan computation time and memory consumption, without compromising plan quality or robustness.