Department of Radiation Oncology, Mayo Clinic Hospital, Phoenix, Arizona.
Department of Biomedical Informatics, Arizona State University, Tempe, Arizona.
Med Phys. 2017 Dec;44(12):6138-6147. doi: 10.1002/mp.12610. Epub 2017 Oct 26.
We propose a robust treatment planning model that simultaneously considers proton range and patient setup uncertainties and reduces high linear energy transfer (LET) exposure in organs at risk (OARs) to minimize the relative biological effectiveness (RBE) dose in OARs for intensity-modulated proton therapy (IMPT). Our method could potentially reduce the unwanted damage to OARs.
We retrospectively generated plans for 10 patients including two prostate, four head and neck, and four lung cancer patients. The "worst-case robust optimization" model was applied. One additional term as a "biological surrogate (BS)" of OARs due to the high LET-related biological effects was added in the objective function. The biological surrogate was defined as the sum of the physical dose and extra biological effects caused by the dose-averaged LET. We generated nine uncertainty scenarios that considered proton range and patient setup uncertainty. Corresponding to each uncertainty scenario, LET was obtained by a fast LET calculation method developed in-house and based on Monte Carlo simulations. In each optimization iteration, the model used the worst-case BS among all scenarios and then penalized overly high BS to organs. The model was solved by an efficient algorithm (limited-memory Broyden-Fletcher-Goldfarb-Shanno) in a parallel computing environment. Our new model was benchmarked with the conventional robust planning model without considering BS. Dose-volume histograms (DVHs) of the dose assuming a fixed RBE of 1.1 and BS for tumor and organs under nominal and uncertainty scenarios were compared to assess the plan quality between the two methods.
For the 10 cases, our model outperformed the conventional robust model in avoidance of high LET in OARs. At the same time, our method could achieve dose distributions and plan robustness of tumors assuming a fixed RBE of 1.1 almost the same as those of the conventional robust model.
Explicitly considering LET in IMPT robust treatment planning can reduce the high LET to OARs and minimize the possible toxicity of high RBE dose to OARs without sacrificing plan quality. We believe this will allow one to design and deliver safer proton therapy.
我们提出了一种稳健的治疗计划模型，该模型同时考虑质子射程和患者摆位不确定性，并降低危及器官（OAR）中的高线性能量传递（LET）暴露，以最大限度地降低调强质子治疗（IMPT）中 OAR 的相对生物效应（RBE）剂量。我们的方法可能会降低 OAR 不必要的损伤。
我们回顾性地为 10 名患者生成了计划，包括 2 名前列腺癌患者、4 名头颈部癌患者和 4 名肺癌患者。应用了“最坏情况鲁棒优化”模型。在目标函数中添加了一个额外的术语，即 OAR 的“生物替代物（BS）”，因为高 LET 相关的生物效应。生物替代物被定义为物理剂量和由剂量平均 LET 引起的额外生物效应的总和。我们生成了九个不确定性场景，这些场景考虑了质子射程和患者摆位不确定性。针对每个不确定性场景，通过我们内部开发的快速 LET 计算方法和基于蒙特卡罗模拟的方法获得 LET。在每次优化迭代中，模型使用所有场景中的最坏情况 BS，然后对器官进行过度高的 BS 惩罚。该模型在并行计算环境中通过有效的算法（有限内存 Broyden-Fletcher-Goldfarb-Shanno）求解。我们的新模型与不考虑 BS 的传统稳健规划模型进行了基准测试。在名义和不确定性场景下，比较了假设固定 RBE 为 1.1 和 BS 的肿瘤和器官的剂量-体积直方图（DVH），以评估两种方法之间的计划质量。
对于 10 个病例，我们的模型在避免 OAR 中的高 LET 方面优于传统稳健模型。同时，我们的方法可以在假设固定 RBE 为 1.1 的情况下实现肿瘤的剂量分布和计划稳健性，几乎与传统稳健模型相同。
在 IMPT 稳健治疗计划中明确考虑 LET 可以降低 OAR 中的高 LET，并最大限度地减少 OAR 中高 RBE 剂量的潜在毒性，而不会牺牲计划质量。我们相信这将允许设计和提供更安全的质子治疗。
