多中心方法预测机器人内SRS/SRT的计划质量
Valeria Landoni1, Sara Broggi2, Marcello Serra3
1Medical Physics Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy; Medical Physics Unit. A.O. San Camillo Forlanini, Rome, Italy.
概括
网络刀 (CK) 大脑立体辐射手术 (SRS) 和立体辐射疗法 (SRT) 计划显示中心间的变化. 对剂量下降的多中心预测模型是可行的,有助于自动化规划优化.
科学领域:
- 医学物理 医学物理
- 辐射瘤学 辐射瘤学
- 神经外科 神经外科
背景情况:
- 立体辐射手术 (SRS) 和立体辐射疗法 (SRT) 是对脑损伤的先进治疗方法.
- CyberKnife (CK) 是用于这些治疗的机器人放射性手术系统.
- 治疗规划中的中心间变化可能会影响治疗结果.
研究的目的:
- 分析CyberKnife (CK) 大脑SRS/SRT计划中的机构间的合规性和剂量梯度变化.
- 调查用于指导或自动化规划优化多中心预测模型的可行性.
主要方法:
- 收集了来自8个中心的335个临床CK脑SRS/SRT计划的数据.
- 计算的符合性指数 (CI),剂量梯度指数 (DGI) 和有效半径 (Reff).
- 从使用线性回归的规划目标体积 (PTV) 维度分析了剂量下降的可预测性.
主要成果:
- 合规指数 (CI) 在26%的计划中超过1.20,集中在2个中心.
- 剂量梯度指数 (DGI) 显示了可接受的机构间可变性和与PTV大小的强相关性 (p < 0.0001).
- 对于Reff的回归模型显示出高可预测性 (R2 ≥0.958),对于较小的同位素剂量值的变化性增加.
结论:
- 中心间的差异凸显了多机构方法的价值.
- 对于CK大脑SRS/SRT剂量下降的多中心预测模型是可行的和用户友好的.
- Reff模型和DGI分析可以帮助自动化规划,并防止低于最佳的计划.
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