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基于深度学习的细分,使用前列腺癌放射治疗的个体患者数据.

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  • 1Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.

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这项研究开发了一种针对患者的自细分模型,用于使用深度学习增强的适应性放射治疗 (ART). 该模型准确地对前列腺癌患者的风险器官进行细分,提高了ART的效率.

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科学领域:

  • 医学物理 医学物理
  • 辐射疗法 辐射疗法
  • 医疗成像医学成像

背景情况:

  • 风险器官细分对于适应性放射治疗 (ART) 是至关重要的.
  • 使用深度学习的自动细分可以提高效率,减少人工劳动在ART.
  • 前列腺癌的治疗需要精确划分有风险的器官.

研究的目的:

  • 为前列腺癌患者的风险器官开发一种自细分模型.
  • 利用个体患者数据集和基于深度学习的增强来实现个性化的ART.
  • 根据治疗期间的解剖学变化来定制放射治疗.

主要方法:

  • 使用可变形向量场 (DVFs) 的基于深度学习的增强方法.
  • 在增强CT图像上训练了一个nnU-net自分区网络.
  • 使用子相似系数 (DSC),豪斯多夫距离和平均表面距离创建和评估了针对患者的模型.

主要成果:

  • 成功开发了针对患者的自我细分模型.
  • 在膀 (0.94 ± 0.03),前列腺 (0.84 ± 0.07) 和直肠 (0.83 ± 0.04) 中实现了高的DSC值.
  • 该模型的准确性与在大型数据集上训练的模型相提并论.

结论:

  • 使用个人患者数据和增强技术证明了自动细分的可行性.
  • 拟议的方法显示了在ART的前列腺自动细分中临床应用的潜力.
  • 这种方法可以为前列腺癌患者个性化放射治疗.