Management of Breathing effects in Radiotherapy Planning

描述（由申请人提供）：强度调制放射治疗已被证明在应用于肺部治疗时可以改善放射治疗剂量分布，特别是如果可以将递送与患者的呼吸周期相匹配。然而，与呼吸记录 IMRT 治疗计划相关的许多技术问题尚未得到解决，并且缺乏潜在计划和实施方法之间的比较。该项目的主要目标是确定增加的计划和交付复杂性与改善累积患者剂量分布以及对治疗结果的预期影响之间的权衡。在跨学科合作中，将开发共享的跨平台工具集、技术和测试数据集，并最终公开存档以供其他研究人员使用。在此框架内，将开发模拟一系列从简单到复杂的潜在临床选项的规划技术，包括：简单边缘、以统计方式解释肿瘤运动、门控以及将 IMRT 传输与呼吸阶段完全同步。据推测，很大一部分模拟肺患者（无法实现屏气技术）将从使用呼吸记录计划和交付中获得预期的结果。在特定目标 SA #1（4-D 优化）中，将开发所有相关交付技术的时空（“4-D”）IMRT 规划和优化方法。在 SA #2（不确定性和日常变化的管理）中，这些方法将被推广以明确考虑不确定性和日常变化。在 SA #3（临床影响）中，将针对统计上显着的模拟患者计划队列估计这些技术在可能的剂量测定和结果改善方面的潜在影响。这项研究首先将为了解 4-D 放射治疗的潜在结果影响提供指导；其次，针对具有挑战性的 4-D 治疗计划问题提供稳健的解决方案；第三，为 4 维规划领域的其他研究人员共享数据和软件工具。

DESCRIPTION (provided by applicant): Intensity modulated radiation therapy has been shown to improve radiotherapy dose distributions when applied to lung treatments, especially if the delivery can be registered to the patient's breathing cycle. However, many technical issues associated with breathing-registered IMRT treatment planning have not yet been addressed, and comparisons between potential planning and delivery approaches are lacking. The primary goal of this project is to determine the tradeoffs between increased planning and delivery complexity vs. improvements in cumulative patient dose distributions and the expected impact on treatment outcomes. In an interdisciplinary collaboration, a shared cross-platform toolset, techniques, and test data sets will be developed and eventually publicly archived for use by other researchers. Within this framework, planning techniques to simulate a range of potential clinical options, from simple to complex, will be developed, including: simple margins, accounting for tumor motion in a statistical way, gating, and fully synchronizing IMRT delivery with breathing-phase. It is hypothesized that a significant fraction of simulated lung patients (who cannot achieve breath-hold techniques) will have a predicted outcome benefit from the use of breathing-registered planning and delivery. In Specific Aim SA #1 (4-D optimization), temporo-spatial ("4-D") IMRT planning and optimization methods for all relevant delivery techniques will be developed. In SA #2 (Management of uncertainties and daily variations), those methods will be generalized to explicitly account for uncertainties and day-to-day variations. In SA #3 (Clinical impact), the potential impact of the techniques in terms of likely dosimetric and outcome improvements will be estimated for a statistically significant cohort of simulated patient plans. This research will provide, firstly, guidance for understanding the potential outcomes impact of 4-D radiotherapy treatments; secondly, robust solutions to challenging 4-D treatment planning problems; and thirdly, shared data and software tools for other researchers in the area of 4-D planning.