Morrill S M, Lam K S, Lane R G, Langer M, Rosen I I
University of Texas Medical Branch, Department of Radiation Therapy, Galveston 77550.
Int J Radiat Oncol Biol Phys. 1995 Jan 1;31(1):179-88. doi: 10.1016/0360-3016(94)00350-T.
Very Fast Simulated Reannealing is a relatively new (1989) and sophisticated algorithm for simulated annealing applications. It offers the advantages of annealing methods while requiring shorter execution times. The purpose of this investigation was to adapt Very Fast Simulated Reannealing to conformal treatment planning optimization.
We used Very Fast Simulated Reannealing to optimize treatments for three clinical cases with two different cost functions. The first cost function was linear (minimum target dose) with nonlinear dose-volume normal tissue constraints. The second cost function (probability of uncomplicated local control) was a weighted product of normal tissue complication probabilities and the tumor control probability.
For the cost functions used in this study, the Very Fast Simulated Reannealing algorithm achieved results within 5-10% of the final solution (100,000 iterations) after 1000 iterations and within 3-5% of the final solution after 5000-10000 iterations. These solutions were superior to those produced by a conventional treatment plan based on an analysis of the resulting dose-volume histograms. However, this technique is a stochastic method and results vary in a statistical manner. Successive solutions may differ by up to 10%.
Very Fast Simulated Reannealing, with modifications, is suitable for radiation therapy treatment planning optimization. It produced results within 3-10% of the optimal solution, produced using another optimization algorithm (Mixed Integer Programming), in clinically useful execution times.
超快速模拟退火算法是一种相对较新的(1989年)且复杂的模拟退火应用算法。它兼具退火方法的优点,同时所需执行时间更短。本研究的目的是使超快速模拟退火算法适用于适形治疗计划优化。
我们使用超快速模拟退火算法,针对三个临床病例,采用两种不同的代价函数来优化治疗方案。第一个代价函数是线性的(最小靶剂量),伴有非线性的剂量 - 体积正常组织约束。第二个代价函数(无并发症局部控制概率)是正常组织并发症概率与肿瘤控制概率的加权乘积。
对于本研究中使用的代价函数,超快速模拟退火算法在1000次迭代后,得到的结果与最终解(100,000次迭代)相差5 - 10%,在5000 - 10000次迭代后,与最终解相差3 - 5%。基于对所得剂量 - 体积直方图的分析,这些解优于传统治疗计划所产生的解。然而,该技术是一种随机方法,结果会以统计方式变化。连续的解可能相差高达10%。
经过改进的超快速模拟退火算法适用于放射治疗计划优化。在临床可用的执行时间内,它产生的结果与使用另一种优化算法(混合整数规划)得到的最优解相差3 - 10%。