纳入和量化可变形图像注册剂量积累中的不确定性:关于在线适应性治疗的好处的可行性研究
Evangelia Choulilitsa1,2, Andreas Johan Smolders1,2, Thallis Alves Santos1,3
1Center for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland.
Physics in medicine and biology
|November 19, 2025
概括
这项研究引入了一个新的概率框架,以考虑适应性质子疗法剂量积累中的可变形图像注册不确定性. 该方法改善了目标覆盖率和风险器官节省在日常自适应性质子疗法相比触发重新规划.
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:
- 在适应性放射治疗中,准确的剂量积累对于治疗疗效至关重要.
- 可变形图像注册 (DIR) 对于跨图像的剂量跟踪至关重要,但引入了不确定性.
- 这些不确定性可能会对累积剂量分配和治疗结果产生重大影响.
研究的目的:
- 开发和验证一个概率框架,用于将DIR不确定性纳入剂量积累.
- 为了将这些不确定性转化为临床相关的指标,使用剂量-体积组图 (DVH) 波段.
- 评估框架在改进自适应性质子疗法工作流程方面的可行性.
主要方法:
- 一个深度学习模型量化了5名头癌患者的DIR不确定性.
- 合成CTs使用概率变形向量场生成和注册.
- 概率累积剂量被生成并可视化为两个适应性工作流程的DVH频段:TriggeredAPT和DAPT.
主要成果:
- 该框架成功地将DIR的不确定性整合到剂量积累中,产生可解释的指标.
- 与TriggeredAPT相比,每日适应性质子疗法 (DAPT) 始终改善了目标覆盖率和风险器官 (OAR) 节省.
- 在D98%的改善达到4 GyRBE,即使模拟剂量积累的不确定性.
结论:
- 不确定性意识的剂量积累是可行的,并且与适应性放射治疗相关.
- 拟议的框架提供了一个可解释的DIR相关不确定性可视化.
- 这种方法可以帮助评估和完善DIR依赖的自适应性质子疗法工作流.
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