基于 Pareto 最优的点扫描强度调制质子治疗射束方向优化方法。

Department of Radiation Oncology, Mayo Clinic, Rochester, MN, 55905, USA.

Med Phys. 2020 Jun;47(5):2049-2060. doi: 10.1002/mp.14096. Epub 2020 Mar 13.

PURPOSE

To provide a proof of principle of a Pareto-based method to automatically generate optimal intensity-modulated proton therapy (IMPT) plans for various noncoplanar beam orientations.

METHODS

A novel multicriteria beam orientation optimization (MCBOO) method was developed to generate Pareto database of optimal plans. The MCBOO method automatically explores the beam orientations and the scalarization parameters of the IMPT plans simultaneously. The MCBOO method is based on multicriteria bilevel optimization (i.e., hierarchical optimization with two nested levels, named the upper and lower level optimization). In MCBOO, the upper level optimization explores the noncoplanar beam orientation space, while the lower level explores the scalarization parameters for a given beam orientation. Differential evolution method was used in both levels, and the Pareto optimal plans were aggregated from the bilevel optimizations to construct the Pareto database. The MCBOO method was implemented on a multinode multi-GPU cluster, and it was tested on three brain tumor patient cases. The Pareto database of the three patients was generated for a set of DVH-based objectives. A statistical analysis was performed between a selected set of MCBOO plans and the manual plan (plan with manually selected beam orientation based on the clinical experience and optimized with the same single plan iterative optimizer used in the MCBOO). The selected set of MCBOO plans consisted of plans that matched the performance of the manual plan [i.e., MCBOO plans that have the same target coverage (within 2%) as the manual plan or better and achieved the same dose (within 2%) or lower to all of the organs at risks (OARs) but one OAR]. Additionally, a dosimetric comparison between of one of the selected MCBOO plans vs the manual plan was conducted.

RESULTS

The multicriteria beam orientation optimization algorithm automatically generated Pareto plans for the three noncoplanar brain tumor cases. The MCBOO plans provided an alternative objective trade-offs to the manual plan. The selected MCBOO plans showed a reduction in dose to multiple organs at risk vs the manual plan with a maximum value which ranged between 10.8 and 12.9 Gy for the three patients. The trade-off of the OAR dose reduction resulted in higher dose to no more than one OAR for each of the selected MCBOO plans vs the manual plan. The maximum dose increase in the MCBOO plans over the manual plan ranged from 7.8 to 11.8 Gy.

CONCLUSIONS

A novel multicriteria beam orientation optimization method was developed and tested on three IMPT patient cases. The method automatically generates Pareto plans database by exploring the noncoplanar beam orientations. The method was able to identify beam orientations with Pareto optimal plans that are comparable to the manually created plans with varying objective trade-offs.

目的

提供一种基于 Pareto 原理的方法，用于自动为各种非共面射束方向生成最优强度调制质子治疗（IMPT）计划。

方法

开发了一种新的多准则射束方向优化（MCBOO）方法来生成 Pareto 最优计划数据库。MCBOO 方法自动同时探索 IMPT 计划的射束方向和标量参数。MCBOO 方法基于多准则双层优化（即具有两个嵌套级别的分层优化，分别称为上、下层优化）。在 MCBOO 中，上层优化探索非共面射束方向空间，而下层优化则为给定射束方向探索标量参数。在这两个层次中都使用了差分进化方法，并从双层优化中聚合 Pareto 最优计划，以构建 Pareto 数据库。MCBOO 方法在一个多节点多 GPU 集群上实现，并在三个脑肿瘤患者病例上进行了测试。为一组基于剂量体积直方图（DVH）的目标生成了这三个患者的 Pareto 数据库。对一组选定的 MCBOO 计划与手动计划（基于临床经验选择的射束方向的计划，并使用与 MCBOO 中相同的单计划迭代优化器进行优化）进行了统计学分析。选定的 MCBOO 计划集由与手动计划具有相同性能的计划组成[即，MCBOO 计划具有与手动计划相同的靶区覆盖率（2%以内）或更好，并且实现了对所有危及器官（OAR）的相同剂量（2%以内）或更低，但一个 OAR 除外]。此外，对选定的 MCBOO 计划之一与手动计划进行了剂量学比较。

结果

多准则射束方向优化算法自动为三个非共面脑肿瘤病例生成了 Pareto 计划。MCBOO 计划为手动计划提供了替代的目标权衡。与手动计划相比，选定的 MCBOO 计划显示出对多个 OAR 剂量的降低，最大值在三个患者中分别为 10.8 至 12.9Gy。OAR 剂量降低的权衡导致每个选定的 MCBOO 计划对每个 OAR 的剂量增加不超过一个。与手动计划相比，MCBOO 计划中的最大剂量增加范围为 7.8 至 11.8Gy。