Applying a RapidPlan model trained on a technique and orientation to another: a feasibility and dosimetric evaluation.
Applying a RapidPlan model trained on a technique and orientation to another: a feasibility and dosimetric evaluation.
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将在一种技术和方向上训练的 RapidPlan 模型应用于另一种技术和方向：可行性和剂量测定评估
DOI：
10.1186/s13014-016-0684-9
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发表时间：
2016-08-18
期刊：
影响因子：
--
通讯作者：
Zhang Y
中科院分区：
文献类型：
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作者：
Wu H;Jiang F;Yue H;Zhang H;Wang K;Zhang Y
背景为基于知识的计划开发剂量体积直方图（DVH）估计模型是非常耗时的，并且如果仅用于假设的类似即将发生的病例，则可能是低效的。临床上需要探索和验证配置模型的其他潜在应用。本研究测试的假设，仰卧容积调强弧治疗（VMAT）模型可以优化强度调制放射治疗（IMRT）计划的其他患者设置orientations.MethodsBased RapidPlan，DVH估计模型进行了训练，使用81仰卧VMAT直肠计划和验证10个类似的情况下，以确保其设计目的的鲁棒性。然后尝试应用该模型重新优化来自30名历史患者（20名俯卧和10名仰卧）的重复IMRT计划的动态MLC序列，这些患者接受了与模型相同的处方治疗（50.6和41.8戈伊，同时达到95%的PGTV和PTV/22次）。基于知识的再优化的性能和设置方向的影响进行了评估dosetric.ResultsThe VMAT模型验证类似的情况下，显示可比的目标剂量分布和显着提高器官保留（由10.77 ~ 18.65%）比原来的计划。任何一种设置的IMRT计划都可以使用仰卧VMAT模型重新优化，这显著降低了膀胱剂量（D_（50）%由33.85 ± 2.96提高到25.09 ± 1.32戈伊，提高了25.88%;平均剂量从33.99 ± 2.77增加到26.25 ± 1.22戈伊，增加了22.77（D50%由15.65 ± 3.33增至13.73 ± 1.43戈伊，提高了12.27%;平均剂量由16.26 ± 2.74增至14.62 ± 1.10戈伊，提高了10.09%），均P < 0.01。在VMAT优化器中手动设定靶-目标，虽然剂量均匀性和PGTV符合性指数（CI_PGTV）变化不大（≤0.01），但IMRT计划的CI_PTV显著增加（Δ = 0.17，P < 0.01）。半自动调强放射治疗计划增加了全球最大剂量和V107%，由于缺乏热点抑制通过特定的手动优化或注量map editing.ConclusionsThe瓦里安RapidPlan模型训练的技术和方向可以用于另一个。基于知识的计划提高了器官保留和质量一致性，但为VMAT优化器定义的靶目标应重新适应IMRT计划，然后进行手动热点处理。
BackgroundThe development of a dose-volume-histogram (DVH) estimation model for knowledge-based planning is very time-consuming and it could be inefficient if it was only used for similar upcoming cases as supposed. It is clinically desirable to explore and validate other potential applications for a configured model. This study tests the hypothesis that a supine volumetric modulated arc therapy (VMAT) model can optimize intensity modulated radiotherapy (IMRT) plans of other patient setup orientations.MethodsBased on RapidPlan, a DVH estimation model was trained using 81 supine VMAT rectal plans and validated on 10 similar cases to ensure the robustness of its designed purpose. Attempts were then made to apply the model to re-optimize the dynamic MLC-sequences of the duplicated IMRT plans from 30 historical patients (20 prone and 10 supine) that were treated with the same prescription as for the model (50.6 and 41.8 Gy to 95 % of PGTV and PTV simultaneously/22 fractions). The performance of knowledge-based re-optimization and the impact of setup orientations were evaluated dosimetrically.ResultsThe VMAT model validation on similar cases showed comparable target dose distribution and significantly improved organ sparing (by 10.77 ~ 18.65 %) than the original plans. IMRT plans of either setup can be re-optimized using the supine VMAT model, which significantly reduced the dose to the bladder (by 25.88 % from 33.85 ± 2.96 to 25.09 ± 1.32 Gy for D50 %; by 22.77 % from 33.99 ± 2.77 to 26.25 ± 1.22 Gy for mean dose) and femoral head (by 12.27 % from 15.65 ± 3.33 to 13.73 ± 1.43 Gy for D50 %; by 10.09 % from 16.26 ± 2.74 to 14.62 ± 1.10 Gy for mean dose), allP< 0.01. Although the dose homogeneity and PGTV conformity index (CI_PGTV) changed slightly (≤0.01), CI_PTVof IMRT plans was significantly increased (Δ = 0.17,P< 0.01) by the manually defined target-objectives in the VMAT optimizer. The semi-automated IMRT planning increased the global maximum dose and V107 %due to the missing of hot spot suppression by specific manual optimizing or fluence map editing.ConclusionsThe Varian RapidPlan model trained on a technique and orientation can be used for another. Knowledge-based planning improves organ sparing and quality consistency, yet the target-objectives defined for VMAT-optimizer should be readapted to IMRT planning, followed by manual hot spot processing.