Cao Wenhua, Lim Gino J, Lee Andrew, Li Yupeng, Liu Wei, Ronald Zhu X, Zhang Xiaodong
Department of Industrial Engineering, University of Houston, Houston, Texas 77204, USA.
Med Phys. 2012 Aug;39(8):5248-56. doi: 10.1118/1.4737870.
Beam angle optimization (BAO) by far remains an important and challenging problem in external beam radiation therapy treatment planning. Conventional BAO algorithms discussed in previous studies all focused on photon-based therapies. Impact of BAO on proton therapy is important while proton therapy increasingly receives great interests. This study focuses on potential benefits of BAO on intensity-modulated proton therapy (IMPT) that recently began available to clinical cancer treatment.
The authors have developed a novel uncertainty incorporated BAO algorithm for IMPT treatment planning in that IMPT plan quality is highly sensitive to uncertainties such as proton range and setup errors. A linear programming was used to optimize robust intensity maps to scenario-based uncertainties for an incident beam angle configuration. Unlike conventional intensity-modulated radiation therapy with photons (IMXT), the search space for IMPT treatment beam angles may be relatively small but optimizing an IMPT plan may require higher computational costs due to larger data size. Therefore, a deterministic local neighborhood search algorithm that only needs a very limited number of plan objective evaluations was used to optimize beam angles in IMPT treatment planning.
Three prostate cancer cases and two skull base chordoma cases were studied to demonstrate the dosimetric advantages and robustness of optimized beam angles from the proposed BAO algorithm. Two- to four-beam plans were optimized for prostate cases, and two- and three-beam plans were optimized for skull base cases. By comparing plans with conventional two parallel-opposed angles, all plans with optimized angles consistently improved sparing at organs at risks, i.e., rectum and femoral heads for prostate, brainstem for skull base, in either nominal dose distribution or uncertainty-based dose distributions. The efficiency of the BAO algorithm was demonstrated by comparing it with alternative methods including simulated annealing and genetic algorithm. The numbers of IMPT plan objective evaluations required were reduced by up to a factor of 5 while the same optimal angle plans were converged in selected comparisons.
Uncertainty incorporated BAO may introduce pronounced improvement of IMPT plan quality including dosimetric benefits and robustness over uncertainties, based on the five clinical studies in this paper. In addition, local search algorithms may be more efficient in finding optimal beam angles than global optimization approaches for IMPT BAO.
在体外放射治疗治疗计划中，射束角度优化（BAO）一直是一个重要且具有挑战性的问题。先前研究中讨论的传统BAO算法均聚焦于基于光子的治疗。随着质子治疗越来越受到关注，BAO对质子治疗的影响至关重要。本研究聚焦于BAO对调强质子治疗（IMPT）的潜在益处，IMPT最近已开始应用于临床癌症治疗。
作者开发了一种用于IMPT治疗计划的新型纳入不确定性的BAO算法，因为IMPT计划质量对诸如质子射程和摆位误差等不确定性高度敏感。使用线性规划针对基于场景的不确定性为入射射束角度配置优化稳健的强度图。与传统的光子调强放射治疗（IMXT）不同，IMPT治疗射束角度的搜索空间可能相对较小，但由于数据量较大，优化一个IMPT计划可能需要更高的计算成本。因此，在IMPT治疗计划中使用了一种仅需要非常有限数量的计划目标评估的确定性局部邻域搜索算法来优化射束角度。
研究了三例前列腺癌病例和两例颅底脊索瘤病例，以证明所提出的BAO算法优化后的射束角度在剂量学优势和稳健性方面的表现。针对前列腺病例优化了两到四野计划，针对颅底病例优化了两野和三野计划。通过将计划与传统的两个平行相对角度的计划进行比较，在标称剂量分布或基于不确定性的剂量分布中，所有优化角度的计划均持续改善了危及器官的 sparing，即前列腺病例中的直肠和股骨头、颅底病例中的脑干。通过将BAO算法与包括模拟退火和遗传算法在内的替代方法进行比较，证明了该算法的效率。在选定的比较中，所需的IMPT计划目标评估次数减少了多达五倍，同时收敛到相同的最佳角度计划。
基于本文的五项临床研究，纳入不确定性的BAO可能会显著改善IMPT计划质量，包括在剂量学益处和对不确定性的稳健性方面。此外，对于IMPT的BAO，局部搜索算法在寻找最佳射束角度方面可能比全局优化方法更有效。
