使用多输出回归的NSCLC治疗计划的基于知识的权衡预测
Tenzin Kunkyab1, Yang Lei1, Hao Guo1
1Department of Radiation Oncology, The Mount Sinai Hospital, New York, New York, USA.
Medical physics
|August 24, 2025
概括
这项研究引入了一种新的基于知识的规划 (KBP) 对非小细胞肺癌 (NSCLC) 治疗的权衡模式. 该模型准确地预测了规划变化,提高了效率并帮助临床决策.
科学领域:
- 辐射瘤学
- 医学物理
- 计算生物学
背景情况:
- 基于知识的规划 (KBP) 利用过去的治疗数据来预测危险器官 (OAR) 的剂量体积图 (DVH) 参数.
- 目前的KBP方法预测单一的规划结果,忽略了临床决策的潜在权衡.
- 这限制了对非小细胞肺癌 (NSCLC) 等复杂病例的治疗计划的优化.
研究的目的:
- 为本地先进的NSCLC开发一个KBP权衡预测模型.
- 帮助临床医生在治疗规划过程中做出明智的决定.
- 探索超出单个帕雷托最佳点的规划权衡.
主要方法:
- 每个患者生成了13个VMAT计划变化 (n=53),包括平衡和权衡计划,优先节省OAR.
- 使用OAR DVH的前三个主要组成部分作为目标和53个解剖特征作为预测因素.
- 训练了一种随机森林多输出回归模型,以预测13个计划-OAR变化的DVH主要组成部分.
主要成果:
- KBP的权衡模型显著优于平衡模型,显示较低的平均RMSE (5.32对27.3).
- 权衡模型实现了关键DVH指标的较低平均绝对误差,包括脊髓Dmax,食道Dmax和肺/心脏V20Gy/V30Gy.
- 对所有比较指标观察到统计显著性 (p < 0.01),突出显示该模型的优异预测准确性.
结论:
- 开发的KBP权衡模型可靠地预测NSCLC治疗的规划变化.
- 该模型可以作为决策支持工具,在预规划期间提供可行的权衡估计.
- 整合到工作流程可以提高治疗规划效率,并可能提高计划质量.
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