使用多图像优化进行触发计划调整,以提高头癌质子疗法的稳定性
Nadine Vatterodt1,2, Brian Winey3, Stine S Korreman1,2
1Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark.
Physics and imaging in radiation oncology
|December 24, 2025
概括
这项研究引入了头子质子疗法的触发强大的适应策略,通过适应解剖变异来改善目标覆盖率和危险器官节省. 该方法提高了治疗的稳定性,与其他策略相比,适应的次数较少.
科学领域:
- 医学物理 医学物理
- 辐射瘤学 辐射瘤学
- 图像指导疗法是指导图像的疗法.
背景情况:
- 头部和部 (HNC) 质子疗法的解剖学变化可能会影响目标覆盖率和危险器官 (OAR) 节省.
- 每天的解剖学变化需要适应性策略来保持治疗的有效性和安全性.
研究的目的:
- 引入和评估使用HNC质子疗法的多图像优化触发的强大的适应策略.
- 通过适应目标和OAR中观察到的剂量偏差来提高治疗计划的稳定性.
主要方法:
- 五名口腔癌患者的回顾性分析,这些患者接受了质子疗法治疗.
- 从每日光束CT中生成合成CT扫描,用于剂量重新计算.
- 四种策略的比较:没有适应 (NA),触发适应 (TA),触发强大的适应与单个 (TRA-S) 和多个 (TRA-M) 触发的分数图像.
主要成果:
- 与NA和TA相比,触发的强有力的适应 (TRA-S和TRA-M) 改善了目标覆盖率 (中位数D98%至+0.76 Gy),并减少了值违反.
- 不利的剂量偏差减少,OAR剂量与NA相比.
- TA显示了类似的OAR剂量,但并没有持续改善目标覆盖率.
- 综合剂量随着TRA策略的增加,但TRA-S和TRA-M之间的差异很小.
结论:
- 触发强大的适应有效地平衡了HNC质子疗法中的目标覆盖率和OAR节省.
- 这种策略需要比TA更少的调整,并且避免依赖预测图像或额外的CT扫描.
- 它提出了一种可行的方法,用于在临床实践中实施解剖强大的优化.
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