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自动跟踪经过刻板式放射性手术后的大脑转移.

Dylan G Hsu1, Åse Ballangrud1, Kayla Prezelski1

  • 1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States.

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概括

现在可以使用深度学习自动跟踪大脑转移 (BMs). 该METRO系统准确量化瘤对放射治疗的反应,有助于患者监测和治疗计划.

关键词:
大脑转移是大脑的转移.深度学习是一种深度学习.图像的注册 图像的注册纵向瘤跟踪系统 纵向瘤跟踪系统立体无线电手术是什么意思在Gd之后的T1 MR.

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科学领域:

  • 放射学和成像学 放射学和成像学
  • 在瘤学瘤学.
  • 人工智能在医学中的应用

背景情况:

  • 脑转移 (BMs) 患者的生存时间更长,需要重复的立体性放射性手术.
  • 随访磁共振成像 (MRI) 对于监测每2-3个月一次的BMs至关重要.
  • 需要在放射治疗后自动跟踪和量化BMs的响应.

研究的目的:

  • 研究在纵向成像上自动跟踪大脑转移 (BMs) 的可行性.
  • 使用自动化方法量化瘤对放射治疗的反应.
  • 开发和验证一个用于纵向BM跟踪和体积测量的系统.

主要方法:

  • 开发了METRO (重复观察的转移追踪) 系统,用于自动化患者数据处理和BM跟踪.
  • 实施了T1 MR后Gd的纵向内科注册方法.
  • 使用深度学习模型来检测BM和体积测量,与手工测量和放射科医生评估进行验证.

主要成果:

  • 成功追踪了123个被辐射的BM和38个新的BM.
  • 证明了手动和METRO测量BM直径变化之间的高相关性 (皮尔森r=0.88).
  • 实现了1.5 ± 0.2mm的平均注册误差,表明了高精度.

结论:

  • 使用深度学习实现BM的自动纵向跟踪是可行的.
  • 该METRO软件系统有效地跟踪和量化BMs的体积变化.
  • 这项技术解决了在放射治疗前后自动监测BMs的需求.