Department of Radiation Oncology, University of Washington, Seattle, WA 98195-6043, USA.
Med Phys. 2012 Apr;39(4):2261-74. doi: 10.1118/1.3697535.
To evaluate how a more flexible and thorough multiobjective search of feasible IMRT plans affects performance in IMRT optimization.
A multiobjective evolutionary algorithm (MOEA) was used as a tool to investigate how expanding the search space to include a wider range of penalty functions affects the quality of the set of IMRT plans produced. The MOEA uses a population of IMRT plans to generate new IMRT plans through deterministic minimization of recombined penalty functions that are weighted sums of multiple, tissue-specific objective functions. The quality of the generated plans are judged by an independent set of nonconvex, clinically relevant decision criteria, and all dominated plans are eliminated. As this process repeats itself, better plans are produced so that the population of IMRT plans will approach the Pareto front. Three different approaches were used to explore the effects of expanding the search space. First, the evolutionary algorithm used genetic optimization principles to search by simultaneously optimizing both the weights and tissue-specific dose parameters in penalty functions. Second, penalty function parameters were individually optimized for each voxel in all organs at risk (OARs) in the MOEA. Finally, a heuristic voxel-specific improvement (VSI) algorithm that can be used on any IMRT plan was developed that incrementally improves voxel-specific penalty function parameters for all structures (OARs and targets). Different approaches were compared using the concept of domination comparison applied to the sets of plans obtained by multiobjective optimization.
MOEA optimizations that simultaneously searched both importance weights and dose parameters generated sets of IMRT plans that were superior to sets of plans produced when either type of parameter was fixed for four example prostate plans. The amount of improvement increased with greater overlap between OARs and targets. Allowing the MOEA to search for voxel-specific penalty functions improved results for simple cases with three structures but did not improve results for a more complex case with seven structures. For this modification, the amount of improvement increased with less overlap between OARs and targets. The voxel-specific improvement algorithm improved results for all cases, and its clinical relevance was demonstrated in a complex prostate and a very complex head and neck case.
Using an evolutionary algorithm as a tool, it was found that allowing more flexibility in the search space enhanced performance. The two strategies of (a) varying the weights and reference doses in the objective function and (b) removing the constraint of equal penalties for all voxels in a structure both generated sets of plans that dominated sets of plans considered to be "Pareto optimal" within the conventional, more limited search space. When considering voxel-specific objectives, the very large search space can lead to convergence problems in the MOEA for complex cases, but this is not an issue for the VSI algorithm.
评估更灵活和全面的适型调强放疗计划多目标搜索对调强放疗优化性能的影响。
使用多目标进化算法（MOEA）作为工具，研究扩展搜索空间以包括更广泛的惩罚函数范围如何影响生成的调强放疗计划集的质量。MOEA 使用调强放疗计划群体通过确定性最小化重组惩罚函数来生成新的调强放疗计划，重组惩罚函数是多个组织特异性目标函数的加权和。生成计划的质量由一组独立的非凸、临床相关决策标准来判断，所有占优的计划都被消除。随着这个过程的重复，更好的计划被生成，使得调强放疗计划群体将接近 Pareto 前沿。使用三种不同的方法来探索扩展搜索空间的效果。首先，进化算法使用遗传优化原理，通过同时优化惩罚函数中的权重和组织特异性剂量参数来搜索。其次，MOEA 对危及器官（OARs）中的每个体素单独优化了组织特异性剂量参数。最后，开发了一种启发式体素特定改进（VSI）算法，可用于任何调强放疗计划，该算法可逐步改进所有结构（OAR 和靶区）的体素特定惩罚函数参数。使用多目标优化获得的计划集的支配比较概念比较了不同的方法。
对于四个前列腺计划的示例，同时搜索重要性权重和剂量参数的 MOEA 优化生成的调强放疗计划集优于仅固定一种类型参数时生成的计划集。改进的幅度随着 OAR 和靶区之间的重叠增加而增加。允许 MOEA 搜索体素特定的惩罚函数可以改善简单情况下有三个结构的结果，但不能改善有七个结构的更复杂情况的结果。对于这种修改，改进的幅度随着 OAR 和靶区之间的重叠减少而增加。体素特定改进算法改善了所有情况下的结果，并在复杂的前列腺和非常复杂的头颈部病例中证明了其临床相关性。
使用进化算法作为工具，发现允许在搜索空间中具有更大的灵活性可以提高性能。两种策略：（a）在目标函数中改变权重和参考剂量，以及（b）取消结构中所有体素的惩罚相等的约束，都生成了在传统、更有限的搜索空间内被认为是“Pareto 最优”的计划集。在考虑体素特定目标时，MOEA 对于复杂情况的非常大的搜索空间可能会导致收敛问题，但这不是 VSI 算法的问题。
