基于深度学习的统计稳定性评估强度调节的质子治疗头癌的强度调节
Danfu Liang1, Ivan Vazquez1, Mary P Gronberg2
1Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States of America.
Physics in medicine and biology
|September 6, 2024
概括
一种新的深度学习方法准确地预测了头癌的质子疗法剂量分布. 这种方法增强了统计学可靠性评估,提高了临床实践的效率和准确性.
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:
- 在质子疗法中,传统的强度评估在计算上是密集的,并且在统计上是有限的.
- 现有的方法需要大量的剂量计算,这阻碍了日常临床应用.
- 需要有效和统计学上合理的方法来评估计划的稳定性.
研究的目的:
- 验证一种基于深度学习 (DL) 的方法,用于头部和部 (H&N) 质子疗法的统计稳定性评估.
- 评估DL模型直接预测百分位剂量分布的能力,绕过中间剂量计算.
- 为了评估方法的准确性和效率在不同的光束配置和患者数据.
主要方法:
- 一个密集的,扩张的3DU-net被训练来预测从名义剂量和计划CT图像的第5和95百分点剂量分布.
- 该数据集包括582名H&N癌症患者的质子疗法计划.
- 根据每位患者600次剂量重新计算,建立了基本真相百分位;在各种临床场景和训练数据组合中比较了模型性能.
主要成果:
- 基于DL的模型显示出与基本真相百分位数剂量分布的良好一致.
- 平均马指数 (2 mm/2%) 在第5个百分点和第95个百分点的剂量量中始终超过97%.
- 来自H&N和前列腺癌联合训练集的预测显示,与特定地点的训练数据的准确性相当.
结论:
- 拟议的DL方法为质子疗法强度评估提供了百分位数剂量分布的准确和快速预测.
- 该模型具有多功能性,在不同的光束安排和癌症部位上准确执行.
- 这种DL方法为质子疗法中高效准确的稳定性量化提供了有价值的工具.
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