Department of Radiation Oncology, Amsterdam UMC, location Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
Life Sciences and Health research group, Centrum Wiskunde & Informatica, Amsterdam, The Netherlands.
Phys Med Biol. 2021 Feb 13;66(5):055001. doi: 10.1088/1361-6560/abe04e.
Recently, we introduced a bi-objective optimization approach based on dose-volume indices to automatically create clinically good HDR prostate brachytherapy plans. To calculate dose-volume indices, a reconstruction algorithm is used to determine the 3D organ shape from 2D contours, inevitably containing settings that influence the result. We augment the optimization approach to quickly find plans that are robust to differences in 3D reconstruction.
Studied reconstruction settings were: interpolation between delineated organ contours, overlap between contours, and organ shape at the top and bottom contour. Two options for each setting yields 8 possible 3D organ reconstructions per patient, over which the robust model defines minimax optimization. For the original model, settings were based on our treatment planning system. Both models were tested on data of 26 patients and compared by re-evaluating selected optimized plans both in the original model (1 organ reconstruction, the difference determines the cost), and in the robust model (8 organ reconstructions, the difference determines the benefit).
Robust optimization increased the run time from 3 to 6 min. The median cost for robust optimization as observed in the original model was -0.25% in the dose-volume indices with a range of [-0.01%, -1.03%]. The median benefit of robust optimization as observed in the robust model was 0.93% with a range of [0.19%, 4.16%]. For 4 patients, selected plans that appeared good when optimized in the original model, violated the clinical protocol with more than 1% when considering different settings. This was not the case for robustly optimized plans.
Plans of high quality, irrespective of 3D organ reconstruction settings, can be obtained using our robust optimization approach. With its limited effect on total runtime, our approach therefore offers a way to account for dosimetry uncertainties that result from choices in organ reconstruction settings that is viable in clinical practice.
最近，我们引入了一种基于剂量-体积指标的双目标优化方法，用于自动创建临床效果良好的 HDR 前列腺近距离治疗计划。为了计算剂量-体积指标，使用重建算法从 2D 轮廓确定 3D 器官形状，这不可避免地包含影响结果的设置。我们扩充了优化方法，以便快速找到对 3D 重建差异具有鲁棒性的计划。
研究的重建设置包括：勾画器官轮廓之间的插值、轮廓之间的重叠以及轮廓上下的器官形状。每个设置有两种选择，每个患者有 8 种可能的 3D 器官重建，鲁棒模型在这些重建上定义了极大极小优化。对于原始模型，设置基于我们的治疗计划系统。对 26 名患者的数据进行了两种模型的测试，并通过在原始模型（1 种器官重建，差异决定成本）和鲁棒模型（8 种器官重建，差异决定收益）中重新评估选定的优化计划进行了比较。
鲁棒优化将运行时间从 3 分钟增加到 6 分钟。在原始模型中观察到的鲁棒优化的中位数成本为剂量-体积指标中的-0.25%，范围为[-0.01%，-1.03%]。在鲁棒模型中观察到的鲁棒优化的中位数收益为 0.93%，范围为[0.19%，4.16%]。对于 4 名患者，在原始模型中优化时看起来良好的选定计划，在考虑不同设置时，超过 1%的计划违反了临床方案。但对于鲁棒优化的计划并非如此。
使用我们的鲁棒优化方法可以获得高质量的计划，而与 3D 器官重建设置无关。由于对总运行时间的影响有限，因此我们的方法为解决因器官重建设置选择而导致的剂量不确定性提供了一种可行的方法，这种方法在临床实践中是可行的。
