An Yu, Liang Jianming, Schild Steven E, Bues Martin, Liu Wei
Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA.
Department of Biomedical Informatics, Arizona State University, Tempe, AZ, 85281, USA.
Med Phys. 2017 Jan;44(1):28-36. doi: 10.1002/mp.12001. Epub 2017 Jan 3.
Intensity-modulated proton therapy (IMPT) is highly sensitive to range uncertainties and uncertainties caused by setup variation. The conventional inverse treatment planning of IMPT based on the planning target volume (PTV) is not often sufficient to ensure robustness of treatment plans. We applied a probabilistic framework (chance-constrained optimization) in IMPT planning to hedge against the influence of uncertainties.
We retrospectively selected one patient with lung cancer, one patient with head and neck (H&N) cancer, and one with prostate cancer for this analysis. Using their original images and prescriptions, we created new IMPT plans using two methods: (1) a robust chance-constrained treatment planning method with the clinical target volume (CTV) as the target; (2) the margin-based method with PTV as the target, which was solved by commercial software, CPLEX, using linear programming. For the first method, we reformulated the model into a tractable mixed-integer programming problem and sped up the calculation using Benders decomposition. The dose-volume histograms (DVHs) from the nominal and perturbed dose distributions were used to assess and compare plan quality. DVHs for all uncertain scenarios along with the nominal DVH were plotted. The width of the "bands" of DVHs was used to quantify the plan sensitivity to uncertainty. The newly developed Benders decomposition method was compared with a commercial solution to demonstrate its computational efficiency. The trade-off between nominal plan quality and plan robustness was investigated.
Our chance-constrained model outperformed the PTV method in terms of tumor coverage, tumor dose homogeneity, and plan robustness. Our model was shown to produce IMPT plans to meet the dose-volume constraints of organs at risk (OARs) and had better sparing of OARs than the PTV method in the three clinical cases included in this study. The chance-constrained model provided a flexible tool for users to balance between plan robustness and plan quality. In addition, our in-house developed method was found to be much faster than the commercial solution.
With explicit control of plan robustness, the chance-constrained robust optimization model generated superior IMPT plans compared to the PTV-based method.
调强质子治疗（IMPT）对射程不确定性以及由摆位变化引起的不确定性高度敏感。基于计划靶区（PTV）的IMPT传统逆向治疗计划通常不足以确保治疗计划的稳健性。我们在IMPT计划中应用了一种概率框架（机会约束优化）来应对不确定性的影响。
我们回顾性选择了一名肺癌患者、一名头颈（H&N）癌患者和一名前列腺癌患者进行此项分析。利用他们的原始图像和处方，我们使用两种方法创建了新的IMPT计划：（1）一种以临床靶区（CTV）为靶区的稳健机会约束治疗计划方法；（2）以PTV为靶区的基于边界的方法，该方法由商业软件CPLEX通过线性规划求解。对于第一种方法，我们将模型重新表述为一个易于处理的混合整数规划问题，并使用Benders分解加速计算。使用标称剂量分布和扰动剂量分布的剂量体积直方图（DVH）来评估和比较计划质量。绘制了所有不确定场景的DVH以及标称DVH。DVH“带”的宽度用于量化计划对不确定性的敏感性。将新开发的Benders分解方法与商业解决方案进行比较以证明其计算效率。研究了标称计划质量与计划稳健性之间的权衡。
我们的机会约束模型在肿瘤覆盖、肿瘤剂量均匀性和计划稳健性方面优于PTV方法。在本研究纳入的三个临床病例中，我们的模型显示能够生成满足危及器官（OAR）剂量体积约束的IMPT计划，并且比PTV方法对OAR的保护更好。机会约束模型为用户提供了一个在计划稳健性和计划质量之间进行平衡的灵活工具。此外，我们内部开发的方法比商业解决方案快得多。
通过对计划稳健性的明确控制，与基于PTV的方法相比，机会约束稳健优化模型生成了更优的IMPT计划。
