摘要目的:提出改进最差场景算法，能够提升计划鲁棒性并且能平衡计划在标称场景下剂量分布质量与计划鲁棒性。方法:对C形靶模型计划优化中，以标称场景优化为主，同时在每次迭代时计算每个体素在9种场景下的剂量值，取其与在标称场景下该体素剂量值的最大差值作为鲁棒性优化项添加入优化目标函数进行优化。结果:在自主开发的鲁棒性优化计算模块验证，当权重因子p robust＝0.8时，相比常规优化，临床靶体积的 ΔD 95%由9.8 Gy减小至7.6 Gy。当p robust由1减小到0时， ΔD 95%由7.0 Gy增大至9.8 Gy，计划鲁棒性降低，而标称场景下CTV的D 95%、D max和危及器官的D 5%、D max减小，剂量分布质量得到提高。 结论:改进最差场景算法能够有效地提高计划对于射程和摆位不确定性的鲁棒性，并且该方法中p robust可提供给计划制定者用于权衡治疗计划在标称场景的剂量分布质量和计划的鲁棒性。

abstractsObjective:To propose a new robust optimization method, known as modified worst case method, was proposed, which can enable users to control the trade-off between nominal plan quality and plan robustness.Methods:In each iteration of the plan optimization process, the dose value of each voxel in nine scenarios, which corresponded to a nominal scenario and eight perturbed scenarios with range or set-up uncertainties, were calculated and the maximum of deviations of each scenario voxel dose from that of the nominal scenario was included as an additive robust optimization term in the objective function. A weighting factor p robust was used to this robust optimization term to balance the nominal plan quality and plan robustness. Results:The robust optimization methods were implemented and compared in an in-house developed robust optimization module. When p robust=0.8, compared with conventional optimization, the ΔD 95% of CTV was reduced from 9.8 Gy to 7.6 Gy. When p robust was reduced from 1 to 0, ΔD 95% was increased from 7.0 Gy to 9.8 Gy, whereas the D 95% and D max of CTV, and the D 5% and D max of organs at risk (OAR) in the nominal scenario were reduced. Conclusions:The proposed modified worst case method can effectively improve the robustness of the plan to the range and set-up uncertainties. Besides, the weighting factor p robust in this method can be adopted to control the trade-off between nominal plan quality and plan robustness.