Mullins Joel, Renaud Marc-André, Serban Monica, Seuntjens Jan
Department of Physics & Medical Physics Unit, McGill University, Montréal, QC, H4A 3J1, Canada.
Department of Mathematics and Industrial Engineering, Polytechnique Montréal, Montréal, QC, H3T 1J4, Canada.
Med Phys. 2020 Jul;47(7):3078-3090. doi: 10.1002/mp.14155. Epub 2020 Apr 27.
Trajectory-based treatment planning involves the combination of a gantry-couch trajectory with volumetric modulated arc therapy (VMAT) treatment plan optimization. This work presents the implementation of an optimization methodology that generates a trajectory simultaneous with treatment plan optimization (simTr-VMAT).
The optimization algorithm is based on the column generation approach, in which a treatment plan is iteratively constructed through the solution of a subproblem called the "pricing problem." The property of the pricing problem to rank candidate apertures based on their associated price is leveraged to select an optimal aperture while simultaneously determining the trajectory path. A progressively increasing gantry-couch grid resolution is used to provide an initial coarse sampling of the angular solution space while maintaining fine control point spacing with the final treatment plan. The trajectory optimization was applied and compared to coplanar VMAT treatment plans for a lung patient, a glioblastoma patient, and a prostate patient. Algorithm validation was performed through the generation of 5000 random trajectories and optimization using column generation VMAT for each patient case, representing the solution space for the trajectory optimization problem. The simTr-VMAT trajectories were compared against these random trajectories based on a quality metric that prefers trajectories with few control points and low objective function value over long, inefficient trajectories.
For the lung patient, the simTr-VMAT plan resulted in a decrease of the mean dose of 1.5 and 1.0 Gy to the heart and ipsilateral lung, respectively. For the glioblastoma patient, the simTr-VMAT plan resulted in improved planning target volume coverage with a decrease in mean dose to the eyes, lens, nose, and contralateral temporal lobe between 2 and 7 Gy. The prostate patient showed no clinically relevant dosimetric improvement. The simTr-VMAT treatment plans ranked at the 99.6, 96.3, and 99.4 percentiles compared to the distribution of randomly generated trajectories for the lung, glioblastoma, and prostate patients, respectively.
The simTr-VMAT optimization methodology resulted in treatment plans with equivalent or improved dosimetric outcomes compared to coplanar VMAT treatment plans, with the trajectories resulting from the optimization ranking among the optimal trajectories for each patient case.
基于轨迹的治疗计划涉及将机架-治疗床轨迹与容积调强弧形治疗(VMAT)治疗计划优化相结合。本文介绍了一种优化方法的实现,该方法在优化治疗计划的同时生成轨迹(simTr-VMAT)。
优化算法基于列生成方法,其中通过求解一个称为“定价问题”的子问题来迭代构建治疗计划。利用定价问题根据候选射野的相关价格对其进行排序的特性,在确定轨迹路径的同时选择最优射野。使用逐渐增加的机架-治疗床网格分辨率来提供角度解空间的初始粗采样,同时在最终治疗计划中保持精细的控制点间距。将轨迹优化应用于一名肺癌患者、一名胶质母细胞瘤患者和一名前列腺癌患者,并与共面VMAT治疗计划进行比较。通过为每个患者病例生成5000条随机轨迹并使用列生成VMAT进行优化来进行算法验证,代表轨迹优化问题的解空间。基于一种质量指标将simTr-VMAT轨迹与这些随机轨迹进行比较,该指标更倾向于控制点少、目标函数值低的轨迹,而不是长而低效的轨迹。
对于肺癌患者,simTr-VMAT计划使心脏和同侧肺的平均剂量分别降低了1.5 Gy和1.0 Gy。对于胶质母细胞瘤患者,simTr-VMAT计划改善了计划靶区的覆盖,使眼睛、晶状体、鼻子和对侧颞叶的平均剂量降低了2至7 Gy。前列腺癌患者未显示出临床相关的剂量学改善。与肺癌、胶质母细胞瘤和前列腺癌患者随机生成的轨迹分布相比,simTr-VMAT治疗计划分别排在第99.6、96.3和99.4百分位。
与共面VMAT治疗计划相比,simTr-VMAT优化方法产生的治疗计划在剂量学结果上相当或有所改善,优化产生的轨迹在每个患者病例的最优轨迹中排名靠前。