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规划复杂性和剂量学特征用于体积调制弧线疗法中的马通过率分类:在不同的LINAC中进行外部验证

Chao Li¹, Zhuo Su¹, Bing Li²

¹Department of Radiation Oncology (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences, Zhejiang Province, China), The Second Affiliated Hospital, Zhejiang University School of Medicine, China.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)

|March 30, 2025

概括

这项研究通过结合计划复杂性和剂量学来改进对体积调制弧线疗法 (VMAT) 计划的马通过率 (GPR) 分类. 这种方法增强了针对患者的质量保证 (PSQA),并确保了不同线性加速器 (LINAC) 的治疗准确性.

科学领域:

医学物理医学物理
辐射瘤学辐射瘤学
辐射疗法质量保证辐射疗法质量保证

背景情况:

卷度调制弧线疗法 (VMAT) 需要强大的质量保证.
马合格率 (Gamma Passing Rate,简称GPR) 是VMAT计划验证的一个关键指标.
目前的方法可能无法完全捕捉计划的复杂性和剂量测量变化.

研究的目的:

为了提高VMAT计划的GPR分类准确性.
将计划复杂性和dosiomics签名与计划参数集成在一起.
通过不同的线性加速器 (LINAC) 验证拟议的方法.

主要方法:

使用了来自TrueBeam的235个VMAT计划和来自VitalBeam LINAC的47个.
提取的计划复杂性,剂量学特征和计划参数.
开发了使用LASSO进行PCscore和Doscore的分类模型,并结合了用于GPR预测的计划参数.

主要成果:

综合模型在训练,测试和外部数据集中实现了高分辨能力,AUC高达0.904.
在不同的玛标准下 (例如,0.842在γ3%/2毫米) 保持了强大的性能.
校准曲线和决策曲线分析证实了模型的有效性.

结论:

整合计划复杂性和剂量学签名显著改善了VMAT的GPR分类.
这为患者特异性质量保证 (PSQA) 提供了一个强大的框架.
该方法证明了在不同的LINAC平台上具有通用性.

关键词:

多西奥米克签名的签名马测试通过率 (GPR)这个名字叫做Nomogram.计划复杂性签名计划复杂性签名卷度调制弧线疗法 (VMAT) 是一种体积调制弧线疗法.

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