深度学习预测的场景剂量直接计划稳定性评估在IMPT的头部和部的预测
Hazem A A Nomer1, Franziska Knuth1, Joep van Genderingen1
1Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The Netherlands.
Physics in medicine and biology
|November 12, 2024
概括
深度学习模型可以预测头癌患者的质子疗法剂量分布,改善治疗稳定性的评估. 这种方法可以在各种错误场景下更快地评估强度调制质子疗法 (IMPT) 计划.
科学领域:
- 医学物理 医学物理
- 辐射瘤学 辐射瘤学
- 人工智能在医学中的应用
背景情况:
- 强度调制质子疗法 (IMPT) 对设置和范围的不确定性敏感,需要强大的优化.
- 在IMPT规划中的强有力的优化确保了目标覆盖,但也大大增加了规划时间.
- 在多个错误场景中评估计划的稳定性对于临床实施至关重要.
研究的目的:
- 研究一种深度学习 (DL) 策略,用于预测头癌个人错误场景中的剂量分布 IMPT.
- 以使用基于DL的剂量预测来直接评估治疗计划的稳定性.
- 评估DL的可行性和准确性,以便进行可靠的IMPT规划.
主要方法:
- 开发了一个DL模型,包含特定场景的同心距离地图和场景索引嵌入.
- 为392名头癌患者生成了高质量的9个场景基础真相强大的计划.
- 使用自动化的多标准优化和用于场景差异化的单热向量编码来训练DL模型.
主要成果:
- 对于CTV的报道,DL模型与地面真相达成了高度一致 (在1%点内) (96%为CTV低,75%为CTV高).
- 风险器官的中位数剂量差异在所有强度场景中都是临床上可接受的.
- 对于所有场景的完整3D剂量分布预测可以快速实现,大约在14秒内.
结论:
- 深度学习剂量预测是一种可行的方法,用于评估IMPT的个别错误场景中的稳定性.
- 这种DL策略可以简化质子疗法规划中的稳定性评估过程.
- 这些发现支持将DL整合到增强和高效的IMPT治疗计划中.
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