Craft David L, Halabi Tarek F, Shih Helen A, Bortfeld Thomas R
Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA.
Med Phys. 2006 Sep;33(9):3399-407. doi: 10.1118/1.2335486.
Radiotherapy planning involves inherent tradeoffs: the primary mission, to treat the tumor with a high, uniform dose, is in conflict with normal tissue sparing. We seek to understand these tradeoffs on a case-to-case basis, by computing for each patient a database of Pareto optimal plans. A treatment plan is Pareto optimal if there does not exist another plan which is better in every measurable dimension. The set of all such plans is called the Pareto optimal surface. This article presents an algorithm for computing well distributed points on the (convex) Pareto optimal surface of a multiobjective programming problem. The algorithm is applied to intensity-modulated radiation therapy inverse planning problems, and results of a prostate case and a skull base case are presented, in three and four dimensions, investigating tradeoffs between tumor coverage and critical organ sparing.
主要任务是以高剂量且均匀的剂量治疗肿瘤，这与保护正常组织相冲突。我们试图通过为每位患者计算帕累托最优计划数据库，逐案理解这些权衡。如果不存在在每个可测量维度上都更好的另一个计划，那么该治疗计划就是帕累托最优的。所有此类计划的集合称为帕累托最优表面。本文提出了一种算法，用于计算多目标规划问题的（凸）帕累托最优表面上分布良好的点。该算法应用于调强放射治疗逆向计划问题，并给出了前列腺病例和颅底病例在三维和四维情况下的结果，研究了肿瘤覆盖和关键器官保护之间的权衡。
