Strategies for automatic online treatment plan reoptimization using clinical treatment planning system: A planning parameters study
Strategies for automatic online treatment plan reoptimization using clinical treatment planning system: A planning parameters study
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DOI：
10.1118/1.4823473
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发表时间：
2013-11-01
期刊：
影响因子：
3.8
通讯作者：
Wu, Q. Jackie
中科院分区：
文献类型：
--
作者：
Li, Taoran;Wu, Qiuwen;Wu, Q. Jackie
目的：使用在线重新优化的前列腺癌自适应放射治疗可以改善对分次间解剖结构变化的控制。然而，在线重新优化的临床实施目前受到当前策略效率低下以及与当前治疗计划系统集成相关的困难的限制。本研究探讨了使用基于临床能量图的治疗计划系统执行快速（类似于 2 分钟）自动在线重新优化的策略；并探索不同输入参数设置的性能：剂量体积直方图 (DVH) 目标设置、起始阶段和迭代次数（在实时计划的背景下）。方法：在治疗的第一周（5 个部分），使用 12 种不同的优化策略组合每天重新优化 10 名患者的模拟治疗。目标设置选项包括基于指南的 RTOG 目标、基于计划 CT 解剖结构的患者特定目标，以及根据计划 CT 目标改编的每日 CBCT 基于解剖结构的目标。开始阶段的选项包括在有或没有原始计划的流量图的情况下开始重新优化。迭代次数的选项为 50 和 100。然后通过统计模型对调整后的计划进行分析，并在剂量测定和交付效率方面进行比较。结果：所有在线重新优化的计划均在大约 2 分钟内完成，具有良好的覆盖率并符合每日目标。三个输入参数，即 DVH 目标、起始阶段和迭代次数，几乎独立地影响优化结果。与基于指南的目标相比，针对患者的特定目标通常可以提供更好的 OAR 保护。将日常解剖纳入目标设置所带来的高剂量保留的益处与从计划 CT 到每日 CBCT 的 OAR 体积的相对变化呈正相关。使用原始计划注量图作为起始阶段减少了中间剂量区域的OAR剂量，但增加了17%的监测单位。在 100 到 50 次迭代之间观察到 OAR V50%/V70Gy/V76Gy 的差异仅为 2cc 或更小。结论：使用临床治疗计划系统在大约 2 分钟内执行自动在线重新优化是可行的。选择最佳的输入参数集是实现高质量重新优化计划的关键，并且应基于个体患者的日常解剖结构、分娩效率和计划调整所允许的时间。 (C) 2013 年美国医学物理学家协会。
Purpose: Adaptive radiation therapy for prostate cancer using online reoptimization provides an improved control of interfractional anatomy variations. However, the clinical implementation of online reoptimization is currently limited by the low efficiency of current strategies and the difficulties associated with integration into the current treatment planning system. This study investigates the strategies for performing fast (similar to 2 min) automatic online reoptimization with a clinical fluence-map-based treatment planning system; and explores the performance with different input parameters settings: dose-volume histogram (DVH) objective settings, starting stage, and iteration number (in the context of real time planning).Methods: Simulated treatments of 10 patients were reoptimized daily for the first week of treatment (5 fractions) using 12 different combinations of optimization strategies. Options for objective settings included guideline-based RTOG objectives, patient-specific objectives based on anatomy on the planning CT, and daily-CBCT anatomy-based objectives adapted from planning CT objectives. Options for starting stages involved starting reoptimization with and without the original plan's fluence map. Options for iteration numbers were 50 and 100. The adapted plans were then analyzed by statistical modeling, and compared both in terms of dosimetry and delivery efficiency.Results: All online reoptimized plans were finished within similar to 2 min with excellent coverage and conformity to the daily target. The three input parameters, i.e., DVH objectives, starting stage, and iteration number, contributed to the outcome of optimization nearly independently. Patient-specific objectives generally provided better OAR sparing compared to guideline-based objectives. The benefit in high-dose sparing from incorporating daily anatomy into objective settings was positively correlated with the relative change in OAR volumes from planning CT to daily CBCT. The use of the original plan fluence map as the starting stage reduced OAR dose at the mid-dose region, but increased the monitor units by 17%. Differences of only 2cc or less in OAR V50%/V70Gy/V76Gy were observed between 100 and 50 iterations.Conclusions: It is feasible to perform automatic online reoptimization in similar to 2 min using a clinical treatment planning system. Selecting optimal sets of input parameters is the key to achieving high quality reoptimized plans, and should be based on the individual patient's daily anatomy, delivery efficiency, and time allowed for plan adaptation. (C) 2013 American Association of Physicists in Medicine.