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容积调强弧形放疗计划的自动交互式优化

Automatic interactive optimization for volumetric modulated arc therapy planning.

作者信息

Tol Jim P, Dahele Max, Peltola Jarkko, Nord Janne, Slotman Ben J, Verbakel Wilko F A R

机构信息

Department of Radiotherapy, VU University Medical Center, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.

Varian Medical Systems, Paciuksenkatu 21, 00270, Helsinki, Finland.

出版信息

Radiat Oncol. 2015 Apr 1;10:75. doi: 10.1186/s13014-015-0388-6.

Abstract

BACKGROUND

Intensity modulated radiotherapy treatment planning for sites with many different organs-at-risk (OAR) is complex and labor-intensive, making it hard to obtain consistent plan quality. With the aim of addressing this, we developed a program (automatic interactive optimizer, AIO) designed to automate the manual interactive process for the Eclipse treatment planning system. We describe AIO and present initial evaluation data.

METHODS

Our current institutional volumetric modulated arc therapy (RapidArc) planning approach for head and neck tumors places 3-4 adjustable OAR optimization objectives along the dose-volume histogram (DVH) curve that is displayed in the optimization window. AIO scans this window and uses color-coding to differentiate between the DVH-lines, allowing it to automatically adjust the location of the optimization objectives frequently and in a more consistent fashion. We compared RapidArc AIO plans (using 9 optimization objectives per OAR) with the clinical plans of 10 patients, and evaluated optimal AIO settings. AIO consistency was tested by replanning a single patient 5 times.

RESULTS

Average V95&V107 of the boost planning target volume (PTV) and V95 of the elective PTV differed by ≤0.5%, while average elective PTV V107 improved by 1.5%. Averaged over all patients, AIO reduced mean doses to individual salivary structures by 0.9-1.6Gy and provided mean dose reductions of 5.6Gy and 3.9Gy to the composite swallowing structures and oral cavity, respectively. Re-running AIO five times, resulted in the aforementioned parameters differing by less than 3%.

CONCLUSIONS

Using the same planning strategy as manually optimized head and neck plans, AIO can automate the interactive Eclipse treatment planning process and deliver dosimetric improvements over existing clinical plans.

摘要

背景

对于存在许多不同危及器官(OAR)的部位,调强放射治疗的治疗计划复杂且耗时费力,难以获得一致的计划质量。为解决这一问题,我们开发了一个程序(自动交互式优化器,AIO),旨在使Eclipse治疗计划系统的手动交互式过程自动化。我们描述了AIO并展示了初始评估数据。

方法

我们目前对头颈部肿瘤的机构容积调强弧形放疗(RapidArc)计划方法是在优化窗口中显示的剂量体积直方图(DVH)曲线上设置3 - 4个可调整的OAR优化目标。AIO扫描此窗口并使用颜色编码来区分DVH线,使其能够频繁且更一致地自动调整优化目标的位置。我们将RapidArc AIO计划(每个OAR使用9个优化目标)与10例患者的临床计划进行比较,并评估最佳AIO设置。通过对一名患者进行5次重新计划来测试AIO的一致性。

结果

增强计划靶体积(PTV)的平均V95和V107以及选择性PTV的V95相差≤0.5%,而选择性PTV的平均V107提高了1.5%。在所有患者中平均计算,AIO使各个唾液腺结构的平均剂量降低了0.9 - 1.6Gy,使复合吞咽结构和口腔的平均剂量分别降低了5.6Gy和3.9Gy。AIO运行5次后,上述参数的差异小于3%。

结论

使用与手动优化的头颈部计划相同的计划策略,AIO可以使Eclipse治疗计划的交互式过程自动化,并在剂量学方面优于现有临床计划。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/535c/4394415/53ead3f56da4/13014_2015_388_Fig1_HTML.jpg

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