为局部性前列腺癌治疗规划和机器参数优化提供一个协同强化学习框架
Nathan Shaffer1, Avinash Reddy Mudireddy2, Joel St-Aubin3
1Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA.
Medical physics
|January 30, 2026
概括
一个新的深度强化学习 (RL) 算法自动化了前列腺癌的体积调节弧疗法 (VMAT) 机器参数优化,比传统方法更快地生成临床上可比拟的计划.
科学领域:
- 医学物理 医学物理
- 辐射瘤学 辐射瘤学
- 机器学习 机器学习
背景情况:
- 卷度调制弧线疗法 (VMAT) 机器参数优化 (MPO) 是计算密集的.
- 现有的机器学习方法往往补充,而不是取代传统的优化器,并依赖于广泛的训练数据.
- 深度强化学习 (RL) 为VMAT MPO提供了一种新的方法,通过试错学习最佳策略.
研究的目的:
- 为自动化VMAT MPO开发和验证一个深度RL算法.
- 创建独立于商业治疗计划系统 (TPS) 优化器的临床可比性前列腺癌治疗计划.
- 确保生成的计划符合机器的限制.
主要方法:
- 100名前列腺癌患者的数据集被用于培训,验证和测试 (分为70-10-20).
- 一个近接政策优化 (PPO) 算法训练了两个卷积神经网络以优化多叶聚合器 (MLC) 位置和监控单元 (MU).
- RL网络使用剂量,结构面罩和机器参数作为输入,优化基于剂量-体积直方图 (DVH) 的奖励函数.
主要成果:
- 该RL算法在平均6.3±4.7秒内生成VMAT计划.
- 与参考计划相比,RL生成的计划显示了改善的膀和直肠节省.
- 在PTV D2%和降低平均直肠剂量 (Dmean) 中观察到有统计学意义的改善,实现了所有临床目标.
结论:
- 一个深度的RL框架已经成功地为VMAT MPO开发和验证.
- 该算法快速生成高质量的VMAT前列腺癌计划,与手动优化可比.
- 这表明RL有潜力完全自动化VMAT规划,减少时间,同时保持质量.
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