一种基于机器学习的方法来预测每个场的能量层,用于肺癌现场扫描质子弧疗法:可行性研究
Yuanyuan Ma1,2,3,4, Yazhou Li1,2,3,5,6, Penghui Xu1,2,3,7
1Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China.
Medical physics
|May 21, 2024
概括
机器学习可以自动选择最佳能量层 (ELs) 用于质子弧疗法 (PAT) 肺癌计划. 这种ML方法为PAT中EL选择提供了一种快速有效的方法,与手工方法相比.
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:
- 在质子弧疗法 (PAT) 中优化能量层 (EL) 选择对于平衡剂量约束和输送效率至关重要.
- 先进的PAT技术依赖于高效和准确的方法来确定每个处理领域的最佳EL.
研究的目的:
- 研究使用机器学习 (ML) 进行肺癌PAT中自动化能量层 (EL) 选择的可行性和临床益处.
- 开发和验证一个ML模型,用于预测PAT肺癌治疗计划中每个领域的最佳EL.
主要方法:
- 使用质子布拉格峰位 (BPP) 进行特征能量层 (EL).
- 提取了几何,水相当厚度 (WET) 和beamlet特征,以训练BPP预测的Ridge回归模型.
- 使用 ML 预测的 BPP (PAT_PRED) 开发了 PAT 计划,并将其与地面真相 (PAT_TRUTH) 和中期WET (PAT_MID) 计划进行了比较.
主要成果:
- 在预测BPP时,ML模型实现了9.165mm的根平均平方误差 (RMSE) 和6.572mm的平均绝对误差 (MAE),表明2-3 ELs的差异.
- PAT_PRED计划表现出与PAT_TRUTH计划相比的可靠性,目标覆盖率和危险器官 (OAR) 保护 (差异<0.5 Gy(RBE)).
- PAT_MID计划显示了改善的OAR节约和稳定性,但损害了剂量均性,小瘤的合规性差.
结论:
- 机器学习有效地预测了肺癌患者的质子弧疗法 (PAT) 计划的能量层 (EL).
- ML方法提供了一种快速 (不到2秒) 和记忆效率高的方法,用于在PAT中选择EL.
- 这种自动化方法显示了推进PAT技术的可行性和潜在的临床益处.
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