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Cancer therapies are various modes of treatment, such as surgery, radiation therapy, and chemotherapy that are administered to cancer patients.
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放射治疗的自动治疗规划:一种交叉模式和协议研究.

Gregory Szalkowski1,2, Xuanang Xu3, Shiva Das1

  • 1Department of Radiation Oncology, University of North Carolina, Chapel Hill, North Carolina.

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在强度调节放射治疗 (IMRT) 计划上训练的深度学习模型可以预测其他放射治疗方式的剂量. 将这些预测与多标准优化 (MCO) 整合起来,可以改善处于风险的器官的节约,并减少计划的变化.

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科学领域:

  • 医学物理 医学物理
  • 辐射瘤学 辐射瘤学
  • 医疗保健中的人工智能

背景情况:

  • 自动化治疗计划旨在提高放射治疗的效率和一致性.
  • 跨模式转移学习是适应人工智能模型与不同治疗技术的新兴领域.
  • 多标准优化 (MCO) 允许在治疗规划中平衡竞争的临床目标.

研究的目的:

  • 评估3D剂量预测在强度调节放射治疗 (IMRT) 数据上训练的模型中的跨模式适用性.
  • 评估整合多标准优化器 (MCO) 的影响,以适应剂量预测的机构偏好.
  • 探索减少放射治疗计划生成时间和变异性的潜力.

主要方法:

  • 在340个头部和部IMRT计划上训练的3阶段U-Net模型生成了剂量预测.
  • 预测被用来通过备用功能创建IMRT,VMAT和断层治疗计划.
  • 使用预测剂量作为约束条件,采用MCO优化,并根据临床目标评估计划质量.
  • 交付质量保证 (QA) 在计划的子集上进行.

主要成果:

  • 剂量预测在各种模式中被准确地复制,在关键结构中有轻微的偏差.
  • 通过MCO优化,可显著降低风险器官剂量,同时保持目标覆盖率.
  • 所有生成的计划都显示出临床交付能力,马分析的合格率超过98%.

结论:

  • 在IMRT数据上训练的模型可以有效地应用于其他放射治疗方式.
  • 使用预测作为MCO约束,为自动化规划提供了灵活的热启动.
  • 这些方法有望减少不同医疗机构的计划周转时间和质量差异.