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近距离放射治疗的治疗计划:一种整数规划模型、两种计算方法以及永久性前列腺植入计划的实验

Treatment planning for brachytherapy: an integer programming model, two computational approaches and experiments with permanent prostate implant planning.

作者信息

Lee E K, Gallagher R J, Silvern D, Wuu C S, Zaider M

机构信息

School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta 30332-0205, USA.

出版信息

Phys Med Biol. 1999 Jan;44(1):145-65. doi: 10.1088/0031-9155/44/1/012.

DOI:10.1088/0031-9155/44/1/012
PMID:10071881
Abstract

An integer linear programming model is proposed as a framework for optimizing seed placement and dose distribution in brachytherapy treatment planning. The basic model involves using 0/1 indicator variables to describe the placement or non-placement of seeds in a prespecified three-dimensional grid of potential locations. The dose delivered to each point in a discretized representation of the diseased organ and neighbouring healthy tissue can then be modelled as a linear combination of the indicator variables. A system of linear constraints is imposed to attempt to keep the dose level at each point to within specified target bounds. Since it is physically impossible to satisfy all constraints simultaneously, each constraint uses a variable to either record when the target dose level is achieved, or to record the deviation from the desired level. These additional variables are embedded into an objective function to be optimized. Variations on this model are discussed and two computational approaches--a branch-and-bound algorithm and a genetic algorithm--for finding 'optimal' seed placements are described. Results of computational experiments on a collection of prostate cancer cases are reported. The results indicate that both optimization algorithms are capable of producing good solutions within 5 to 15 min, and that small variations in model parameters can have a measurable effect on the dose distribution of the resulting plans.

摘要

提出了一种整数线性规划模型,作为近距离放射治疗治疗计划中优化种子植入位置和剂量分布的框架。基本模型涉及使用0/1指示变量来描述在预先指定的三维潜在位置网格中种子的植入或不植入情况。然后,在患病器官和相邻健康组织的离散表示中,传递到每个点的剂量可以建模为指示变量的线性组合。施加一组线性约束,试图将每个点的剂量水平保持在指定的目标范围内。由于同时满足所有约束在物理上是不可能的,每个约束使用一个变量来记录何时达到目标剂量水平,或者记录与期望水平的偏差。这些额外的变量被嵌入到一个待优化的目标函数中。讨论了该模型的变体,并描述了两种用于找到“最优”种子植入位置的计算方法——分支定界算法和遗传算法。报告了对一组前列腺癌病例的计算实验结果。结果表明,两种优化算法都能够在5到15分钟内产生良好的解决方案,并且模型参数的微小变化会对所得计划的剂量分布产生可测量的影响。

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Treatment planning for brachytherapy: an integer programming model, two computational approaches and experiments with permanent prostate implant planning.近距离放射治疗的治疗计划:一种整数规划模型、两种计算方法以及永久性前列腺植入计划的实验
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引用本文的文献

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A novel greedy heuristic-based approach to intraoperative planning for permanent prostate brachytherapy.一种基于贪婪启发式算法的新型永久性前列腺近距离放射治疗术中规划方法。
J Appl Clin Med Phys. 2015 Jan 8;16(1):5144. doi: 10.1120/jacmp.v16i1.5144.
2
Treatment planning and image guidance for radiofrequency ablation of large tumors.大型肿瘤射频消融的治疗计划与图像引导
IEEE J Biomed Health Inform. 2014 May;18(3):920-8. doi: 10.1109/JBHI.2013.2287202. Epub 2013 Oct 24.
3
Fast patient-specific Monte Carlo brachytherapy dose calculations via the correlated sampling variance reduction technique.
基于相关抽样方差减少技术的快速个体化蒙特卡罗近距离治疗剂量计算。
Med Phys. 2012 Feb;39(2):1058-68. doi: 10.1118/1.3679018.