Jiang Fan, Wu Hao, Yue Haizhen, Jia Fei, Zhang Yibao
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing Cancer Hospital, Beijing, China.
Department of Radiation Oncology, The First Affiliated Hospital of Zhengzhou University and Department of Radiation Oncology, Basic Medical College of Zhengzhou University, Zhengzhou, Henan, China.
J Appl Clin Med Phys. 2017 Mar;18(2):9-14. doi: 10.1002/acm2.12038. Epub 2017 Jan 24.
The enhanced dosimetric performance of knowledge-based volumetric modulated arc therapy (VMAT) planning might be jointly contributed by the patient-specific optimization objectives, as estimated by the RapidPlan model, and by the potentially improved Photon Optimizer (PO) algorithm than the previous Progressive Resolution Optimizer (PRO) engine. As PO is mandatory for RapidPlan estimation but optional for conventional manual planning, appreciating the two optimizers may provide practical guidelines for the algorithm selection because knowledge-based planning may not replace the current method completely in a short run. Using a previously validated dose-volume histogram (DVH) estimation model which can produce clinically acceptable plans automatically for rectal cancer patients without interactive manual adjustment, this study reoptimized 30 historically approved plans (referred as clinical plans that were created manually with PRO) with RapidPlan solution (PO plans). Then the PRO algorithm was utilized to optimize the plans again using the same dose-volume constraints as PO plans, where the line objectives were converted as a series of point objectives automatically (PRO plans). On the basis of comparable target dose coverage, the combined applications of new objectives and PO algorithm have significantly reduced the organs-at-risk (OAR) exposure by 23.49-32.72% than the clinical plans. These discrepancies have been largely preserved after substituting PRO for PO, indicating the dosimetric improvements were mostly attributable to the refined objectives. Therefore, Eclipse users of earlier versions may instantly benefit from adopting the model-generated objectives from other RapidPlan-equipped centers, even with PRO algorithm. However, the additional contribution made by the PO relative to PRO accounted for 1.54-3.74%, suggesting PO should be selected with priority whenever available, with or without RapidPlan solution as a purchasable package. Significantly increased monitor units were associated with the model-generated objectives but independent from the optimizers, indicating higher modulation in these plans. As a summary, PO prevails over PRO algorithm for VMAT planning with or without knowledge-based technique.
基于知识的容积调强弧形放疗（VMAT）计划的剂量学性能提升，可能是由RapidPlan模型估计的患者特异性优化目标，以及比之前的渐进分辨率优化器（PRO）引擎可能有所改进的光子优化器（PO）算法共同促成的。由于PO是RapidPlan估计所必需的，但对于传统手动计划是可选的，了解这两种优化器可能为算法选择提供实用指南，因为基于知识的计划在短期内可能无法完全取代当前方法。本研究使用先前验证的剂量体积直方图（DVH）估计模型，该模型可以在无需交互式手动调整的情况下为直肠癌患者自动生成临床可接受的计划，用RapidPlan解决方案（PO计划）对30个既往批准的计划（称为使用PRO手动创建的临床计划）进行重新优化。然后使用与PO计划相同的剂量体积约束，利用PRO算法再次优化这些计划，其中线目标自动转换为一系列点目标（PRO计划）。在可比的靶区剂量覆盖基础上，新目标与PO算法的联合应用使危及器官（OAR）的照射量比临床计划显著降低了23.49 - 32.72%。在用PRO替代PO后，这些差异在很大程度上得以保留，表明剂量学改善主要归因于优化的目标。因此，早期版本的Eclipse用户即使使用PRO算法，也可能通过采用其他配备RapidPlan的中心生成的模型目标而立即受益。然而，PO相对于PRO的额外贡献占1.54 - 3.74%，这表明无论是否有作为可购买套餐的RapidPlan解决方案，只要可行，应优先选择PO。显著增加的监测单位与模型生成的目标相关，但与优化器无关，表明这些计划中的调制更高。总之，无论有无基于知识的技术，在VMAT计划中PO都优于PRO算法。
