在肺瘤体积调节弧线治疗中,基于知识的剂量体积直方体预测是OVH概念的延伸
Johann Brand1,2,3,4, Juliane Szkitsak1,2,3,4, Oliver J Ott1,2,3,4
1Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Journal of applied clinical medical physics
|April 4, 2025
概括
这项研究开发了一种自动化系统,用于预测体积组图 (DVH) 的剂量,用于接受体积调制弧线疗法 (VMAT) 的肺癌患者. 这种新方法通过基于几何关系准确预测DVH来改善治疗规划.
科学领域:
- 辐射瘤学 辐射瘤学
- 医学物理 医学物理
- 癌症治疗 治疗 癌症治疗
背景情况:
- 卷度调制弧线疗法 (VMAT) 规划平衡了目标覆盖率和危险器官 (OAR) 节省.
- 优化肺瘤的VMAT计划在实现足够的目标覆盖率的同时最大限度地降低OAR剂量方面存在挑战.
- 需要自动化工具来协助复杂的决策过程,以改进VMAT计划.
研究的目的:
- 开发一种基于知识的,结构依赖的,用于肺瘤的自动化剂量体积组图 (DVH) 预测模块.
- 评估使用规划目标量 (PTV) 和OARs之间的几何关系来预测DVH的可行性和准确性.
- 提高肺癌VMAT治疗规划的效率和质量.
主要方法:
- 利用了106例用VMAT治疗的肺癌病例的治疗计划和结构数据.
- 采用一个二维度量,重叠体积直方图 (OVH) 和其旋转对称的补充,重叠-z-直方图 (OZH).
- 通过在数据库中识别类似的OVH和OZH指标来预测可实现的DVH,并使用确定系数 (R2) 评估预测能力.
主要成果:
- 在OVH-OZH组合中,整个肺部的预测和实现的DVH曲线之间显示出强烈的线性 (R2 = 0.975 ± 0.022).
- 与单独使用OVH (R2 = 0.897 ± 0.087) 相比,OZH指标显著提高了心脏DVH预测准确度 (R2 = 0.962 ± 0.036).
- 开发的模块显示了关键OAR的高预测能力,表明其潜在的临床实用性.
结论:
- 结合OZH和OVH,可以有效地创建基于知识的自动化DVH预测模块.
- 将这种方法集成到临床VMAT治疗规划工作流中可以提高计划质量.
- 自动DVH预测系统为优化肺癌放射治疗提供了宝贵的工具.
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