Multi-objective optimization in radiotherapy: applications to stereotactic radiosurgery and prostate brachytherapy
Multi-objective optimization in radiotherapy: applications to stereotactic radiosurgery and prostate brachytherapy
复制标题
DOI：
10.1016/s0933-3657(99)00049-4
复制
发表时间：
2000-05-01
影响因子：
7.5
通讯作者：
Okunieff, P
中科院分区：
文献类型：
--
作者：
Yu, Y;Zhang, JB;Okunieff, P
放射治疗计划是一个多目标优化过程。在这里，我们提出了一个机器智能方案的治疗计划的基础上，多目标决策分析（MODA）和遗传算法（GA）优化。在位移理想模型下，多目标排序策略用L-p度量表示。目标设定、方案满意度和目标重要性的模糊排序可以结合到决策方案中，以同化临床决策。对于L-p度规中的距离测度，基于决策系统的状态能量定义了动态规范函数，假设决策系统随着迭代时间经历热力学冷却。MODA方案与强大的GA引擎交互，GA引擎在定义治疗计划质量的多模态景观中自适应地演变。一个传统的具有挑战性的情况下，脑病变的立体定向放射外科手术选择遗传算法优化。将由此产生的剂量分布与人类开发的计划进行比较，这些计划通常被认为是临床相关的和经验最佳的。遗传算法优化的计划实现了更好地保留关键的正常神经解剖周围的脑病变，同时尊重肿瘤剂量均匀性的预设约束。此外，机器优化往往会产生新的治疗策略，补充专家知识。产生最佳计划的运行时间比人类专家的典型计划时间短得多，因此GA也可以用于帮助人类治疗计划过程。在前列腺近距离放射治疗中，MODA-GA被专门应用于非理想条件，在非理想条件下，种子植入定位发生典型的手术不确定性，其中噪声目标被引入优化方案。嘈杂的系统被认为是可管理的MODA-GA在相应的不确定性水平，合理熟练的手术团队。相比之下，人类专家规划者很难探索有噪声的目标。目前正在探索噪声优化与时间序列分析的潜在用途，用于前列腺粒子植入手术室中的纠错计算机引导。总之，MODA和GA优化的结合提供了一个解决方案，以实际的治疗计划任务和潜在的毛皮真实的时间在放射治疗中的应用。(C)2000 Elsevier Science B.V.保留所有权利。
Treatment planning for radiation therapy is a multi-objective optimization process. Here we present a machine intelligent scheme for treatment planning based on multi-objective decision analysis (MODA) and genetic algorithm (GA) optimization. Multi-objective ranking strategies are represented in the L-p metric under the displaced ideal model. Goal setting, protocol satisficing and fuzzy ranking of objective importance can be incorporated into the decision scheme to assimilate clinical decision making. For distance measures in the L-p metric, a dynamic gauge function is defined based on the state energy of the decision system, which is assumed to undergo thermodynamic cooling with iteration time. The MODA scheme interacts with a robust GA engine, which adaptively evolves in the multi-modal landscape that defines the treatment plan quality. A conventionally challenging case of stereotactic radiosurgery of a brain lesion was selected for GA optimization. The resulting dose distributions are compared to human-developed plans, which are commonly regarded as clinically relevant and empirically optimal. The GA-optimized plans achieve substantially better sparing of critical normal neuroanatomy surrounding the brain lesion while respecting the preset constraints on tumor dose uniformity. In addition, machine optimization tends to produce novel treatment strategies which complements expert knowledge. The run time for producing an optimal plan is considerably shorter than the typical planning time for human experts, thus GA can also be used to aid the human treatment planning process. In prostate brachytherapy, MODA-GA was specifically applied to non-ideal conditions in which typical surgical uncertainties in seed implant positioning occur, where noisy objectives were introduced into the optimization scheme. The noisy system is found to be manageable by MODA-GA at uncertainty levels corresponding to reasonably proficient surgery teams. In contrast, noisy objectives would be very difficult to explore by human expert planners. Potential use of noisy optimization with time series analysis is being explored for error-corrective computer guidance in the operating room for prostate seed implantation. In conclusion, the combination of MODA and GA optimization offers both a solution to practical treatment planning tasks and the potential fur real time applications in radiotherapy. (C) 2000 Elsevier Science B.V. All rights reserved.