KU Leuven, Department of Oncology, Laboratory of Experimental Radiotherapy, Leuven, Belgium.
Université catholique de Louvain, Institut de Recherche Expérimentale et Clinique, Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium.
Phys Med Biol. 2021 Feb 2;66(4):045002. doi: 10.1088/1361-6560/abd22f.
Robustness evaluation of proton therapy treatment plans is essential for ensuring safe treatment delivery. However, available evaluation procedures feature a limited exploration of the actual robustness of the plan and generally do not provide confidence levels. This study compared established and more sophisticated robustness evaluation procedures, with quantified confidence levels. We have evaluated several robustness evaluation methods for 5 bilateral head-and-neck patients optimized considering spot scanning delivery and with a conventional CTV-to-PTV margin of 4 mm. Method (1) good practice scenario selection (GPSS) (e.g. +/- 4 mm setup error 3% range uncertainty); (2) statistically sound scenario selection (SSSS) either only on or both on and inside isoprobability hypersurface encompassing 90% of the possible errors; (3) statistically sound dosimetric selection (SSDS). In the last method, the 90% best plans were selected according to either target coverage quantified by D (SSDS_D ) or to an approximation of the final objective function (OF) used during treatment optimization (SSDS_OF). For all methods, we have considered systematic setup and systematic range errors. A mix of systematic and random setup errors were also simulated for SSDS, but keeping the same conventional margin of 4 mm. All robustness evaluations have been performed using the fast Monte Carlo dose engine MCsquare. Both SSSS strategies yielded on average very similar results. SSSS and GPSS yield comparable values for target coverage (within 0.5 Gy). The most noticeable differences were found for the CTV between GPSS, on the one hand, and SSDS_D and SSDS_OF, on the other hand (average worst-case D were 2.8 and 2.0 Gy larger than for GPSS, respectively). Simulating explicitly random errors in SSDS improved almost all DVH metrics. We have observed that the width of DVH-bands and the confidence levels depend on the method chosen to sample the scenarios. Statistically sound estimation of the robustness of the plan in the dosimetric space may provide an improved insight on the actual robustness of the plan for a given confidence level.
质子治疗计划的稳健性评估对于确保安全治疗至关重要。然而，现有的评估程序对计划的实际稳健性的探索有限，通常不提供置信水平。本研究比较了已建立的和更复杂的稳健性评估程序，并提供了量化的置信水平。我们已经评估了几种稳健性评估方法，用于 5 例双侧头颈部患者，这些患者是在考虑点扫描递送的情况下进行优化的，并且常规 CTV 至 PTV 边界为 4mm。方法 (1) 良好实践场景选择 (GPSS) (例如，+/- 4mm 设位误差 3%范围不确定性)；(2) 仅在或同时在包含 90%可能误差的等概率超曲面上进行统计学合理的场景选择 (SSSS)；(3) 统计学合理的剂量选择 (SSDS)。在最后一种方法中，根据目标覆盖范围 (D) 选择 90%最佳计划 (SSDS_D)，或者根据治疗优化过程中使用的最终目标函数 (OF) 的近似值选择最佳计划 (SSDS_OF)。对于所有方法，我们都考虑了系统设置和系统范围误差。对于 SSDS，还模拟了系统和随机设置误差的混合，但保持常规的 4mm 边界。所有稳健性评估都是使用快速蒙特卡罗剂量引擎 MCsquare 进行的。两种 SSSS 策略的结果平均非常相似。SSSS 和 GPSS 对目标覆盖范围的评估结果非常相似 (相差 0.5Gy 以内)。最明显的差异是在 CTV 方面，一方面是 GPSS，另一方面是 SSDS_D 和 SSDS_OF (最差情况 D 的平均值分别比 GPSS 大 2.8 和 2.0Gy)。在 SSDS 中明确模拟随机误差几乎改善了所有 DVH 指标。我们观察到，DVH 带宽和置信水平的宽度取决于选择用于采样场景的方法。在剂量空间中对计划稳健性进行统计学合理的估计，可能会为给定置信水平的计划实际稳健性提供更好的了解。
