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. 2022 Apr;96:62-69. doi: 10.1016/j.ejmp.2022.02.018. Epub 2022 Feb 26.
Robust planning is essential in proton therapy for ensuring adequate treatment delivery in the presence of uncertainties. For both robust optimization and evaluation, commonly-used techniques can be overly conservative in selecting error scenarios and lack in providing quantified confidence levels. In this study, established techniques are compared to comprehensive alternatives to assess the differences in target coverage and organ at risk (OAR) dose.
Thirteen lung cancer patients were planned. Two robust optimization methods were used: scenario selection from marginal probabilities (SSMP) based on using maximum setup and range error values and scenario selection from joint probabilities (SSJP) that selects errors on a predefined 90% hypersurface. Two robust evaluation methods were used: conventional evaluation (CE) based on generating error scenarios from combinations of maximum errors of each uncertainty source and statistical evaluation (SE) via the Monte Carlo dose engine MCsquare which considers scenario probabilities.
Plans optimized using SSJP had, on average, 0.5 Gy lower dose in CTV D than SSMP-optimized plans. When evaluated using SE, 92.3% of patients passed our clinical threshold in both optimization methods. Average gains in OAR sparing were recorded when transitioning from SSMP to SSJP: esophagus (0.6 Gy D, 0.9 Gy D), spinal cord (3.9 Gy D, 4.1 Gy D) heart (1.1 Gy D, 1.9% V), lungs-GTV (1.0 Gy D , 1.9% V).
Optimization using SSJP yielded significant OAR sparing in all recorded metrics with a target robustness within our clinical objectives, provided that a more statistically sound robustness evaluation method was used.
在质子治疗中，稳健规划对于确保在存在不确定性的情况下充分进行治疗至关重要。对于稳健优化和评估，常用的技术在选择误差场景时可能过于保守，并且缺乏提供量化置信水平的能力。在这项研究中，我们比较了成熟的技术和全面的替代方法，以评估在靶区覆盖和危及器官（OAR）剂量方面的差异。
对 13 例肺癌患者进行了计划。使用了两种稳健优化方法：基于最大摆位和范围误差值选择边缘概率（SSMP）的场景选择，以及选择预定 90%超曲面误差的联合概率（SSJP）的场景选择。使用了两种稳健评估方法：基于各不确定性源最大误差组合生成误差场景的常规评估（CE），以及通过考虑场景概率的 MCsquare 蒙特卡罗剂量引擎进行的统计评估（SE）。
使用 SSJP 优化的计划，CTV D 中的剂量平均比 SSMP 优化的计划低 0.5 Gy。在使用 SE 进行评估时，两种优化方法的 92.3%的患者均通过了我们的临床阈值。从 SSMP 过渡到 SSJP 时，OAR 保护方面的平均增益被记录下来：食管（0.6 Gy D，0.9 Gy D），脊髓（3.9 Gy D，4.1 Gy D），心脏（1.1 Gy D，1.9% V），肺部-GTV（1.0 Gy D，1.9% V）。
使用 SSJP 进行优化可在所有记录的指标中显著保护 OAR，并且目标稳健性在我们的临床目标范围内，前提是使用更具统计学意义的稳健性评估方法。
