Institute of Experimental and Clinical Research, UCLouvain, MIRO Lab, Brussels, Belgium.
Department of Radiation Oncology, Emory University, Atlanta, Georgia, USA.
Med Phys. 2023 Oct;50(10):6554-6568. doi: 10.1002/mp.16706. Epub 2023 Sep 7.
An accurate estimation of range uncertainties is essential to exploit the potential of proton therapy. According to Paganetti's study, a value of 2.4% (1.5 standard deviation) is currently recommended for planning robust treatments with Monte Carlo dose engines. This number is based on a dominant contribution from the mean excitation energy of tissues. However, it was recently shown that expressing tissues as a mixture of water and "dry" material in the CT calibration process allowed for a significant reduction of this uncertainty. We thus propose an adapted framework for pencil beam scanning robust optimization. First, we move towards a spot-specific range uncertainty (SSRU) determination. Second, we use the water-based formalism to reduce range uncertainties and, potentially, to spare better the organs at risk.
The stoichiometric calibration was adapted to provide a molecular decomposition (including water) of each voxel of the CT. The SSRU calculation was implemented in MCsquare, a fast Monte Carlo dose engine dedicated to proton therapy. For each spot, a ray-tracing method was used to propagate molecular I-values uncertainties and obtain the corresponding effective range uncertainty. These were then combined with other sources of range uncertainties, according to Paganetti's study of 2012. The method was then assessed on three head-and-neck patients. Two plans were optimized for each patient: the first one with the classical 2.4% flat range uncertainty (FRU), the second one with the variable range uncertainty. Both plans were then compared in terms of target coverage and OAR mean dose reduction. Robustness evaluations were also performed, using the SSRU for both plans in order to simulate errors as realistically as possible.
For patient 1, it was found that the median SSRU was 1.04% (1.5 standard deviation), yielding, therefore, a very large reduction from the 2.4% FRU. All three SSRU plans were found to have a very good robustness level at a 90% confidence interval while sparing OAR better than the classical plan. For instance, in nominal cases, average reductions in the mean dose of 15.7, 8.4, and 13.2% were observed in the left parotid, right parotid, and pharyngeal constrictor muscle, respectively. As expected, the classical plans showed a higher but unnecessary level of robustness.
Promising results of the SSRU framework were observed on three head-and-neck cases, and more patients should now be considered. The method could also benefit to other tumor sites and, in the long run, the variable part of the range uncertainty could be generalized to other sources of uncertainty in order to move towards more and more patient-specific treatments.
准确估计射程不确定性对于充分发挥质子治疗的潜力至关重要。根据 Paganetti 的研究，目前建议使用蒙特卡罗剂量引擎为稳健治疗规划使用 2.4%（1.5 个标准差）的值。这个数字基于组织的平均激发能的主要贡献。然而，最近的研究表明，在 CT 校准过程中将组织表示为水和“干燥”材料的混合物，可以显著降低这种不确定性。因此，我们提出了一种适用于笔束扫描稳健优化的框架。首先，我们朝着特定点的射程不确定性（SSRUs）的确定迈进。其次，我们使用基于水的公式来降低射程不确定性，并有可能更好地保护危及器官。
我们对化学计量校准进行了调整，以提供 CT 中每个体素的分子分解（包括水）。SSRUs 的计算是在 MCsquare 中实现的，MCsquare 是一种专用于质子治疗的快速蒙特卡罗剂量引擎。对于每个点，我们使用射线追踪方法来传播分子 I 值的不确定性，并获得相应的有效射程不确定性。然后，根据 Paganetti 于 2012 年的研究，将这些不确定性与其他射程不确定性源结合起来。然后，我们在三个头颈部患者中评估了该方法。每个患者都优化了两个计划：第一个计划使用经典的 2.4% 平坦射程不确定性（FRU），第二个计划使用可变射程不确定性。然后，根据靶区覆盖和危及器官平均剂量的降低来比较这两个计划。还进行了稳健性评估，为了尽可能真实地模拟误差，我们使用 SSRUs 对这两个计划进行了评估。
对于患者 1，发现中位数 SSRU 为 1.04%（1.5 个标准差），因此与 2.4% FRU 相比，这大大降低了不确定性。在 90%置信区间内，所有三个 SSRU 计划都被发现具有非常好的稳健性水平，同时比经典计划更好地保护了危及器官。例如，在名义情况下，左腮腺、右腮腺和咽缩肌的平均剂量分别观察到 15.7%、8.4%和 13.2%的降低。正如预期的那样，经典计划显示出更高但不必要的稳健性水平。
在三个头颈部病例中观察到 SSRU 框架的有希望的结果，现在应该考虑更多的患者。该方法也可能受益于其他肿瘤部位，从长远来看，射程不确定性的可变部分可以推广到其他不确定性来源，以便朝着越来越多的个体化治疗方向发展。
