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用深度学习和脚本优化为前列腺放射治疗患者自动化计划生成.

Cody Church1, Michelle Yap1, Mohamed Bessrour1

  • 1Department of Medical Physics, The Ottawa Hospital General Campus, Canada.

Physics and imaging in radiation oncology
|September 23, 2024
PubMed
概括

使用深度学习模型 (DLM) 自动化前列腺放射治疗治疗计划在商业系统中成功部署. 这种单击工作流产生了与临床计划相比非劣质的自动计划,大大减少了规划时间.

关键词:
自动规划 自动规划深度学习是一种深度学习.辐射疗法 辐射疗法

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科学领域:

  • 医学物理 医学物理
  • 辐射疗法 辐射疗法
  • 人工智能在医学中的应用

背景情况:

  • 放射治疗中的治疗规划是一个复杂且耗时的过程.
  • 放射治疗治疗计划的自动化可以提高效率和一致性.

研究的目的:

  • 开发和评估"单击"自动化工作流程,用于前列腺放射治疗治疗计划.
  • 将深度学习模型 (DLM) 预测集成到自动规划的商业治疗计划系统 (TPS) 中.

主要方法:

  • 一个ResUNet深度学习模型 (DLM) 经过训练,可以使用3D轮预测3D剂量分布.
  • 预测的剂量分配被转化为使用TPS目标和逆优化的治疗计划.
  • 在商业TPS中编写和部署了一个自动化工作流程.

主要成果:

  • 自动化计划显示了与临床计划的高度一致,用于规划目标体积 (例如,V100%的差异为0.4%).
  • 膀和直肠剂量的目标在 -6.1%内达成一致.
  • 自动化计划生成过程,包括DLM预测和优化,大约需要15分钟.

结论:

  • 一个完全部署的,单击自动化放射治疗治疗计划工作流程使用DLM在商业TPS中成功实施.
  • 发现生成的自动计划与手工创建的临床计划相比并非劣.
  • 这种自动化方法为有效和准确的放射治疗治疗计划提供了一个有前途的解决方案.