Department of Mathematics, KTH Royal Institute of Technology, Stockholm, Sweden.
Research Department, RaySearch Laboratories AB, Stockholm, Sweden.
Med Phys. 2017 Jun;44(6):2054-2065. doi: 10.1002/mp.12226. Epub 2017 Jun 1.
To set up a framework combining robust treatment planning with adaptive re-optimization in order to maintain high treatment quality, to respond to interfractional geometric variations and to identify those patients who will benefit the most from an adaptive fractionation schedule.
The authors propose robust adaptive strategies based on stochastic minimax optimization for a series of simulated treatments on a one-dimensional patient phantom. The plan applied during the first fractions should be able to handle anticipated systematic and random errors. Information on the individual geometric variations is gathered at each fraction. At scheduled fractions, the impact of the measured errors on the delivered dose distribution is evaluated. For a patient having received a dose that does not satisfy specified plan quality criteria, the plan is re-optimized based on these individually measured errors. The re-optimized plan is then applied during subsequent fractions until a new scheduled adaptation becomes necessary. In this study, three different adaptive strategies are introduced and investigated. (a) In the first adaptive strategy, the measured systematic and random error scenarios and their assigned probabilities are updated to guide the robust re-optimization. (b) In the second strategy, the degree of conservativeness is adapted in response to the measured dose delivery errors. (c) In the third strategy, the uncertainty margins around the target are recalculated based on the measured errors. The simulated treatments are subjected to systematic and random errors that are either similar to the anticipated errors or unpredictably larger in order to critically evaluate the performance of these three adaptive strategies.
According to the simulations, robustly optimized treatment plans provide sufficient treatment quality for those treatment error scenarios similar to the anticipated error scenarios. Moreover, combining robust planning with adaptation leads to improved organ-at-risk protection. In case of unpredictably larger treatment errors, the first strategy in combination with at most weekly adaptation performs best at notably improving treatment quality in terms of target coverage and organ-at-risk protection in comparison with a non-adaptive approach and the other adaptive strategies.
The authors present a framework that provides robust plan re-optimization or margin adaptation of a treatment plan in response to interfractional geometric errors throughout the fractionated treatment. According to the simulations, these robust adaptive treatment strategies are able to identify candidates for an adaptive treatment, thus giving the opportunity to provide individualized plans, and improve their treatment quality through adaptation. The simulated robust adaptive framework is a guide for further development of optimally controlled robust adaptive therapy models.
建立一个结合稳健治疗计划和自适应重新优化的框架，以保持高治疗质量，应对分次间的几何变化，并确定那些最受益于自适应分次方案的患者。
作者提出了基于随机极大极小优化的稳健自适应策略，用于一维患者体模上的一系列模拟治疗。在最初的几次治疗中应用的计划应能够处理预期的系统和随机误差。在每次治疗时收集关于个体几何变化的信息。在预定的治疗时，评估测量误差对所给予剂量分布的影响。对于已接受剂量不符合特定计划质量标准的患者，根据这些个体测量误差对计划进行重新优化。然后，在随后的治疗时应用重新优化的计划，直到需要新的预定适应为止。在本研究中，引入并研究了三种不同的自适应策略。（a）在第一种自适应策略中，更新测量的系统和随机误差情况及其分配的概率，以指导稳健的重新优化。（b）在第二种策略中，根据测量的剂量传递误差适应保守程度。（c）在第三种策略中，根据测量误差重新计算目标周围的不确定性边界。将模拟治疗应用于类似于预期误差或不可预测地更大的系统和随机误差，以严格评估这三种自适应策略的性能。
根据模拟结果，对于类似于预期误差情况的治疗误差情况，稳健优化的治疗计划提供了足够的治疗质量。此外，将稳健计划与自适应相结合可导致改善器官风险保护。在治疗误差不可预测地更大的情况下，第一种策略与每周最多一次的自适应相结合，在提高目标覆盖率和器官风险保护方面明显优于非自适应方法和其他自适应策略，从而显著改善治疗质量。
作者提出了一个框架，该框架提供了在分次治疗过程中针对分次间几何误差进行稳健计划重新优化或边界适应的方法。根据模拟结果，这些稳健自适应治疗策略能够识别出适合自适应治疗的候选者，从而提供个体化的计划，并通过自适应来改善他们的治疗质量。模拟的稳健自适应框架为进一步开发最优控制的稳健自适应治疗模型提供了指导。
