Robust optimization to reduce the impact of biological effect variation from physical uncertainties in intensity-modulated proton therapy
Robust optimization to reduce the impact of biological effect variation from physical uncertainties in intensity-modulated proton therapy
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DOI：
10.1088/1361-6560/aaf5e9
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发表时间：
2019-01-01
影响因子：
3.5
通讯作者：
Cao, Wenhua
中科院分区：
文献类型：
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作者：
Bai, Xuemin;Lim, Gino;Cao, Wenhua
鲁棒优化（RO）方法被应用于调强质子治疗（IMPT）计划中，以确保其在面对治疗实施过程中的不确定性（如质子射程和患者摆位误差）时的稳健性。然而，这些不确定性对质子生物学效应的影响尚未得到专门考虑。在本研究中，我们将基于生物学效应的目标添加到用于IMPT优化的常规RO代价函数中，以最小化生物学效应的变化。
本研究选取了一个脑肿瘤病例、一个前列腺肿瘤病例和一个头颈部肿瘤病例。针对每个病例，使用三种不同的优化方法生成了三个计划：基于计划靶区体积（PTV）的优化、常规RO以及结合生物学效应的RO（BioRO）。在BioRO中，除了常规的基于体素的最坏情况RO目标函数外，对于靶区体积和关键结构中的体素，还最小化了由IMPT实施不确定性导致的生物学效应变化。生物学效应通过剂量平均的线性能量传递（LET）与物理剂量的乘积来近似。假设相对生物学效应（RBE）恒定为1.1，所有计划均进行归一化以提供相同的靶区剂量覆盖。对每个患者病例的三种优化方法的剂量、生物学效应及其不确定性进行了评估和比较。
与基于PTV的计划相比，RO计划实现了更稳健的靶区剂量覆盖，并减少了靶区附近关键结构中的生物学效应热点。此外，凭借其持续稳健的剂量分布，与RO计划相比，BioRO计划不仅减少了靶区和正常组织中生物学效应的变化，还进一步减少了关键结构中的生物学效应热点。
我们的研究结果表明，IMPT可受益于常规RO的使用，与基于PTV的优化相比，它将减少正常组织中的生物学效应并产生更稳健的剂量分布。更重要的是，本研究提供了一个概念验证，即把生物学效应不确定性纳入常规RO不仅能在物理剂量和生物学效应方面控制IMPT计划的稳健性，还能进一步减少正常组织中的生物学效应。
Robust optimization (RO) methods are applied to intensity-modulated proton therapy (IMPT) treatment plans to ensure their robustness in the face of treatment delivery uncertainties, such as proton range and patient setup errors. However, the impact of those uncertainties on the biological effect of protons has not been specifically considered. In this study, we added biological effect-based objectives into a conventional RO cost function for IMPT optimization to minimize the variation in biological effect.One brain tumor case, one prostate tumor case and one head & neck tumor case were selected for this study. Three plans were generated for each case using three different optimization approaches: planning target volume (PTV)-based optimization, conventional RO, and RO incorporating biological effect (BioRO). In BioRO, the variation in biological effect caused by IMPT delivery uncertainties was minimized for voxels in both target volumes and critical structures, in addition to a conventional voxel-based worst-case RO objective function. The biological effect was approximated by the product of dose-averaged linear energy transfer (LET) and physical dose. All plans were normalized to give the same target dose coverage, assuming a constant relative biological effectiveness (RBE) of 1.1. Dose, biological effect, and their uncertainties were evaluated and compared among the three optimization approaches for each patient case.Compared with PTV-based plans, RO plans achieved more robust target dose coverage and reduced biological effect hot spots in critical structures near the target. Moreover, with their sustained robust dose distributions, BioRO plans not only reduced variations in biological effect in target and normal tissues but also further reduced biological effect hot spots in critical structures compared with RO plans.Our findings indicate that IMPT could benefit from the use of conventional RO, which would reduce the biological effect in normal tissues and produce more robust dose distributions than those of PTV-based optimization. More importantly, this study provides a proof of concept that incorporating biological effect uncertainty gap into conventional RO would not only control the IMPT plan robustness in terms of physical dose and biological effect but also achieve further reduction of biological effect in normal tissues.