基于 HyperArc 计划训练的知识型计划模型在脑转移瘤中的剂量学潜力。
Dosimetric potential of knowledge-based planning model trained with HyperArc plans for brain metastases.
机构信息
Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan.
Department of Medical Physics and Engineering, Graduate School of Medicine, Osaka University, Suita, Japan.
出版信息
J Appl Clin Med Phys. 2023 Feb;24(2):e13836. doi: 10.1002/acm2.13836. Epub 2022 Nov 5.
OBJECTIVE
Dosimetric potential of knowledge-based RapidPlan planning model trained with HyperArc plans (Model-HA) for brain metastases has not been reported. We developed a Model-HA and compared its performance with that of clinical volumetric modulated arc therapy (VMAT) plans.
METHODS
From 67 clinical stereotactic radiosurgery (SRS) HyperArc plans for brain metastases, 47 plans were used to build and train a Model-HA. The other 20 clinical HyperArc plans were recalculated in RapidPlan system with Model-HA. The model performance was validated with the 20 plans by comparing dosimetric parameters for normal brain tissue between clinical plans and model-generated plans. The 20 clinical conventional VMAT-based SRS or stereotactic radiotherapy plans (CL-VMAT) were reoptimized with Model-HA (RP) and HyperArc system (HA), respectively. The dosimetric parameters were compared among three plans (CL-VMAT vs. RP vs. HA) in terms of planning target volume (PTV), normal brain excluding PTVs (Brain - PTV), brainstem, chiasm, and both optic nerves.
RESULTS
In model validation, the optimization performance of Model-HA was comparable to that of HyperArc system. In comparison to CL-VMAT, there were no significant differences among three plans with respect to PTV coverage (p > 0.17) and maximum dose for brainstem, chiasm, and optic nerves (p > 0.40). RP provided significantly lower V , V , and V for Brain - PTV than CL-VMAT (p < 0.01).
CONCLUSION
The Model-HA has the potential to significantly reduce the normal brain dose of the original VMAT plans for brain metastases.
目的
基于 HyperArc 计划（HA）训练的知识库 RapidPlan 计划模型（HA 模型）在脑转移瘤中的剂量学潜力尚未见报道。我们开发了一个 HA 模型，并将其与临床容积旋转调强弧形治疗（VMAT）计划的性能进行了比较。
方法
从 67 例脑转移瘤立体定向放射外科（SRS）的 HyperArc 计划中，使用 47 例计划来构建和训练 HA 模型。其余 20 例临床 HyperArc 计划在 RapidPlan 系统中使用 HA 模型重新计算。通过比较临床计划和模型生成计划之间正常脑组织的剂量学参数，验证模型性能。用 HA 模型（RP）和 HyperArc 系统（HA）分别对 20 例临床常规 VMAT 为基础的 SRS 或立体定向放射治疗计划（CL-VMAT）进行重新优化。比较三种计划（CL-VMAT 与 RP 与 HA）在计划靶区（PTV）、PTV 外正常脑组织（Brain-PTV）、脑干、视交叉和视神经中的剂量学参数。
结果
在模型验证中，HA 模型的优化性能与 HyperArc 系统相当。与 CL-VMAT 相比，三种计划在 PTV 覆盖（p＞0.17）和脑干、视交叉和视神经的最大剂量（p＞0.40）方面无显著差异。与 CL-VMAT 相比，RP 为 Brain-PTV 提供的 V 、 V 、 V 显著降低（p＜0.01）。
结论
HA 模型有可能显著降低脑转移瘤原始 VMAT 计划的正常脑组织剂量。
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