在线自适应性放射治疗中,针对轮不确定性的强有力的优化策略
A Smolders1,2, I Bengtsson3,4, A Forsgren3
1Paul Scherrer Institute, Center for Proton Therapy, Switzerland.
Physics in medicine and biology
|July 18, 2024
概括
强大的优化策略有效地减轻了在线适应性放射治疗中的轮不确定性,改善了目标覆盖率和风险器官节省,而不会增加治疗规划时间.
科学领域:
- 医学物理 医学物理
- 辐射瘤学 辐射瘤学
- 计算生物学 计算生物学
背景情况:
- 在线适应性辐射疗法 (ART) 需要快速,自动的轮,以重新优化治疗计划.
- 自动轮引入的不确定性可能会损害治疗的有效性和安全性.
研究的目的:
- 开发和比较强大的优化策略,以减轻在线ART中的轮不确定性.
- 评估这些策略对目标覆盖率和危险器官 (OAR) 节省的影响.
主要方法:
- 一种深度学习方法预测了可变形图像注册的不确定性,以生成轮样本.
- 我们比较了十种优化策略:两种基线,五种基于概率和三种基于场景的强大优化方法.
- 针对头癌患者的质子疗法计划,对目标覆盖率和OAR节省进行了严格的评估.
主要成果:
- 与基线方法相比,明确地将目标轮不确定性纳入强有力的优化改进了目标覆盖范围和OAR节省,而不会增加优化时间.
- 对OAR轮不确定性的稳定性阻止了OAR的剂量增加.
- 基于场景的方法比基于概率的方法提供了更好的OAR节省,但需要更多的计算.
结论:
- 开发了有效的策略,以减轻治疗计划优化中的轮不确定性.
- 这些发现有助于将自动轮整合到在线ART中,并解决其他轮不确定性来源.
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