Li Yupeng, Niemela Perttu, Liao Li, Jiang Shengpeng, Li Heng, Poenisch Falk, Zhu X Ronald, Siljamaki Sami, Vanderstraeten Reynald, Sahoo Narayan, Gillin Michael, Zhang Xiaodong
Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 and Department of Applied Research, Varian Medical Systems, Palo Alto, California 94304.
Department of Applied Research, Varian Medical Systems, Palo Alto, California 94304.
Med Phys. 2015 Aug;42(8):4840-7. doi: 10.1118/1.4923171.
To develop a new robust optimization strategy for intensity-modulated proton therapy as an important step in translating robust proton treatment planning from research to clinical applications.
In selective robust optimization, a worst-case-based robust optimization algorithm is extended, and terms of the objective function are selectively computed from either the worst-case dose or the nominal dose. Two lung cancer cases and one head and neck cancer case were used to demonstrate the practical significance of the proposed robust planning strategy. The lung cancer cases had minimal tumor motion less than 5 mm, and, for the demonstration of the methodology, are assumed to be static.
Selective robust optimization achieved robust clinical target volume (CTV) coverage and at the same time increased nominal planning target volume coverage to 95.8%, compared to the 84.6% coverage achieved with CTV-based robust optimization in one of the lung cases. In the other lung case, the maximum dose in selective robust optimization was lowered from a dose of 131.3% in the CTV-based robust optimization to 113.6%. Selective robust optimization provided robust CTV coverage in the head and neck case, and at the same time improved controls over isodose distribution so that clinical requirements may be readily met.
Selective robust optimization may provide the flexibility and capability necessary for meeting various clinical requirements in addition to achieving the required plan robustness in practical proton treatment planning settings.
开发一种用于调强质子治疗的新型稳健优化策略,作为将稳健质子治疗计划从研究转化为临床应用的重要一步。
在选择性稳健优化中,扩展了基于最坏情况的稳健优化算法,目标函数的项从最坏情况剂量或标称剂量中选择性计算。使用两个肺癌病例和一个头颈癌病例来证明所提出的稳健计划策略的实际意义。肺癌病例的肿瘤运动极小,小于5毫米,并且为了演示该方法,假设其为静态。
与其中一个肺癌病例中基于临床靶区(CTV)的稳健优化所达到的84.6%的覆盖率相比,选择性稳健优化实现了稳健的CTV覆盖,同时将标称计划靶区覆盖率提高到了95.8%。在另一个肺癌病例中,选择性稳健优化中的最大剂量从基于CTV的稳健优化中的131.3%降低到了113.6%。选择性稳健优化在头颈癌病例中提供了稳健的CTV覆盖,同时改善了对等剂量分布的控制,从而可以轻松满足临床要求。
选择性稳健优化除了在实际质子治疗计划设置中实现所需的计划稳健性之外,还可能提供满足各种临床要求所需的灵活性和能力。