Duke Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705 and Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710.
Med Phys. 2013 Nov;40(11):111711. doi: 10.1118/1.4823473.
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 (~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).
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.
All online reoptimized plans were finished within ~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.
It is feasible to perform automatic online reoptimization in ~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.
利用在线重新优化对前列腺癌进行适应性放射治疗，可以更好地控制分次间解剖变化。然而，目前在线重新优化的临床应用受到当前策略效率低下以及与当前治疗计划系统集成困难的限制。本研究旨在探讨在基于临床通量图的治疗计划系统中实现快速（约 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（实时计划背景下）。
对 10 例患者的模拟治疗在治疗的第一周（5 个分次）每天进行重新优化，使用 12 种不同的优化策略组合。目标设置选项包括基于 RTOG 指南的目标、基于计划 CT 解剖的患者特异性目标，以及从计划 CT 目标改编的每日 CBCT 解剖目标。起始阶段的选项包括有无原始计划通量图两种。迭代次数选项为 50 和 100。然后通过统计建模分析改编后的计划，并在剂量学和交付效率方面进行比较。
所有在线重新优化的计划都在~2 分钟内完成，具有极好的覆盖范围和对每日靶区的一致性。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。患者特异性目标通常比基于指南的目标提供更好的 OAR 保护。将每日解剖学纳入目标设置中可提高高剂量区域的保护效果，与 OAR 体积从计划 CT 到每日 CBCT 的相对变化呈正相关。使用原始计划通量图作为起始阶段可以减少中剂量区域的 OAR 剂量，但会增加 17%的监测单位。在 100 和 50 次迭代之间，OAR V50%/V70Gy/V76Gy 的差异仅为 2cc 或更小。
使用临床治疗计划系统在~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，应基于患者的个体每日解剖结构、输送效率以及计划适应的可用时间。
