Department of Radiation Oncology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, PO Box 2040, 3000 CA Rotterdam, The Netherlands.
Phys Med Biol. 2019 Jan 22;64(3):035002. doi: 10.1088/1361-6560/aaf9fe.
Automated treatment planning algorithms have demonstrated capability in generating consistent and high-quality treatment plans. Their configuration (i.e. determining the algorithm's parameters), however, can be a labour-intensive and time-consuming trial-and-error procedure. Previously, we introduced the reference point method (RPM) for fast automated multi-objective treatment planning. The RPM generates a single Pareto optimal plan for each patient. When the RPM is configured appropriately, this plan has clinically favourable trade-offs between all plan objectives. This paper proposes a new procedure to automatically generate a single configuration of the RPM per tumour site. The procedure was tested for prostate cancer. Planning CT scans of 287 previously treated patients were included in a database, together with corresponding Pareto optimal plans generated using our clinically applied two-phase [Formula: see text]-constraint method (part of Erasmus-iCycle) for automated multi-objective treatment planning. The procedure developed acquires plan characteristics observed in a training set. Based on these, an RPM configuration is automatically generated according to user preferences which specify acceptable differences between training set plans and corresponding RPM generated plans. For example, compared to the training set plans, the RPM generated plans need to have similar PTV coverage, and preferably reduced high rectum dose while slight deteriorations in other objectives are allowed. Training sets of different sizes were tested, and the quality of the resulting RPM configurations was evaluated on the test set (subset of the database not used for training). Using the new procedure, an RPM configuration was generated for each training set. The quality of RPM generated plans was similar or slightly better than that of the corresponding test set plans. The proposed automated configuration procedure greatly reduces the manual configuration workload, and thereby improves the efficiency and effectiveness of an automated clinical treatment planning workflow.
自动化治疗计划算法已经证明了在生成一致和高质量治疗计划方面的能力。然而，它们的配置（即确定算法的参数）可能是一项劳动密集型和耗时的反复试验过程。之前，我们引入了参考点方法（RPM）来快速自动化多目标治疗计划。RPM 为每个患者生成一个单一的 Pareto 最优计划。当 RPM 配置得当时，该计划在所有计划目标之间具有临床有利的权衡。本文提出了一种新的方法，可自动为每个肿瘤部位生成 RPM 的单个配置。该方法针对前列腺癌进行了测试。将 287 名先前治疗过的患者的计划 CT 扫描纳入数据库，同时还包括使用我们临床应用的两阶段[Formula: see text]-约束方法（Erasmus-iCycle 的一部分）生成的相应 Pareto 最优计划，用于自动化多目标治疗计划。所开发的过程获取训练集中观察到的计划特征。根据这些特征，并根据用户偏好自动生成 RPM 配置，用户偏好指定了训练集计划和相应 RPM 生成计划之间可接受的差异。例如，与训练集计划相比，RPM 生成的计划需要具有相似的 PTV 覆盖，并且优选降低高直肠剂量，同时允许其他目标略有恶化。测试了不同大小的训练集，并在测试集（未用于训练的数据库子集）上评估了生成的 RPM 配置的质量。使用新的程序，为每个训练集生成了一个 RPM 配置。RPM 生成计划的质量与相应的测试集计划相似或略好。所提出的自动化配置过程大大减少了手动配置的工作量，从而提高了自动化临床治疗计划工作流程的效率和效果。
