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Uncertainty incorporated beam angle optimization for IMPT treatment planning

机译:不确定性结合射束角度优化用于IMPT治疗计划

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摘要

>Purpose: 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.>Methods: 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.>Results: 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.>Conclusions: 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.
机译:>目的:到目前为止,束角优化(BAO)仍然是外部束放射疗法治疗计划中一个重要且具有挑战性的问题。先前研究中讨论的常规BAO算法都集中在基于光子的疗法上。 BAO对质子治疗的影响很重要,而质子治疗越来越受到人们的关注。这项研究的重点是BAO在最近开始可用于临床癌症治疗的强度调节质子治疗(IMPT)上的潜在益处。>方法:作者已经开发出一种新的不确定性并入BAO算法用于IMPT治疗计划中。 IMPT计划质量对不确定性(例如质子范围和设置错误)高度敏感。线性编程用于优化鲁棒强度图,以针对入射光束角度配置基于场景的不确定性。与常规的光子强度调制放射疗法(IMXT)不同,IMPT治疗束角的搜索空间可能相对较小,但由于数据量较大,优化IMPT计划可能需要更高的计算成本。因此,在IMPT治疗计划中使用了只需要进行有限数量的计划客观评估的确定性局部邻域搜索算法来优化射束角度。>结果:分别有3例前列腺癌病例和2例颅底脊索瘤病例我们研究了BAO算法,以证明优化光束角的剂量优势和稳健性。针对前列腺病例优化了两束或四束计划,针对颅骨基本病例优化了两束和三束计划。通过将平面图与传统的两个平行相对的角度进行比较,所有具有最佳角度的平面图都可以持续改善处于危险器官(即前列腺的直肠和股骨头,颅底的脑干)的名义剂量分布或基于不确定性的剂量分布。通过与模拟退火和遗传算法等替代方法进行比较,证明了BAO算法的效率。 >结论:不确定性并入的BAO可能会显着改善IMPT计划的质量,其中包括:基于本文的五项临床研究,剂量测定的益处和对不确定性的鲁棒性。此外,本地搜索算法在寻找最佳波束角方面可能比IMPT BAO的全局优化方法更有效。

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