Department of Physics, Engineering Physics and Optics and Cancer Research Center, Université Laval, Quebec City, QC, G1V 0A6, Canada. Department of Radiation Oncology and Research Center of CHU de Québec-Université Laval, Quebec City, QC, G1R 2J6, Canada.
Phys Med Biol. 2018 Oct 16;63(20):205005. doi: 10.1088/1361-6560/aae24f.
The current iterative approach to inverse planning of high dose rate treatment planning can be time consuming. The purpose of this two-part study is to streamline the planning process while maintaining plan quality. In this second part, a multi-criteria optimization (MCO) planning algorithm is proposed and benchmarked against a standard planning algorithm. With a set of previously established regression models, a patient-specific valid solution space on the Pareto surface was predicted based on the anchor plans results. Alternative plans generated alongside the partial Pareto front were presented to the planner, and one plan was selected as the MCO plan. The dosimetric parameters results as well as the planning time were compared between the MCO plans and the physician-approved standard plans for 236 prostate cases. Results show that the urethra is better spared with MCO planning than with standard planning (a lower mean urethral D value of 2.25%). The overall MCO plan quality also outperforms the standard plan quality, since MCO planning is able to increase the frequency of clinically acceptable plans meeting all of RTOG criteria simultaneously without any human intervention (from 83.05% to 97.46%). Finally, the average MCO planning time is [Formula: see text] without any interventions of treatment planners. The presented MCO planning algorithm constitutes a robust and automated way to improve treatment quality in brachytherapy.
目前，高剂量率治疗计划逆向规划的迭代方法可能很耗时。本研究分为两部分，旨在简化规划过程，同时保持计划质量。在第二部分中，提出了一种多准则优化（MCO）规划算法，并与标准规划算法进行了基准测试。基于一组先前建立的回归模型，根据锚点计划的结果，预测了患者特定的帕累托表面上的有效解空间。与部分帕累托前沿一起生成的替代方案被呈现给规划师，并选择一个方案作为 MCO 方案。比较了 236 例前列腺病例的 MCO 计划和医师批准的标准计划之间的剂量学参数结果和规划时间。结果表明，与标准计划相比，MCO 计划能更好地保护尿道（尿道的平均 D 值更低，为 2.25%）。由于 MCO 规划能够在没有任何人为干预的情况下同时增加满足 RTOG 所有标准的临床可接受计划的频率（从 83.05%增加到 97.46%），因此 MCO 规划的整体计划质量也优于标准计划质量。最后，平均 MCO 规划时间为[公式：见文本]，无需治疗计划者的任何干预。所提出的 MCO 规划算法构成了一种在近距离治疗中提高治疗质量的稳健且自动化的方法。
