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简化数据提取和可视化,使用第三方验证软件进行在线自适应性辐射治疗.

Sean Tanny1, Alexander Podgorsak1, Nicholas Sperling2

  • 1Department of Radiation Oncology, University of Rochester Medical Center, New York, New York, USA.

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此摘要是机器生成的。

一个新的软件工具简化了在线适应性放射治疗 (oART) 数据的提取和分析. 这简化了流程,从日常治疗计划信息中获得更好的见解.

关键词:
基于CBCT的自适应式RTRT.基于道德的适应性适应性.适应性放射疗法适应性放射疗法

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科学领域:

  • 医学物理 医学物理
  • 辐射瘤学 辐射瘤学
  • 数据科学数据科学数据科学

背景情况:

  • 在线适应性辐射疗法 (oART) 产生了大量的每日治疗计划数据.
  • 当前的oART平台经常限制最终用户对这些数据的访问,阻碍了纵向评估.
  • 从oART平台手动提取数据是耗时且低效的.

研究的目的:

  • 开发修改后的软件,用于在线提取适应性辐射治疗 (oART) 数据.
  • 克服访问和分析纵向oART治疗计划数据的限制.
  • 为了促进有效的数据收集,比较和可视化为oART.

主要方法:

  • 创建了一个Python程序来查询二次剂量计算软件中的oART数据.
  • 收集了3年来1200次oART会议的结构,计划和剂量测量数据.
  • 使用Pandas数据框架用于数据存储,比较和可视化,包括非识别工具.

主要成果:

  • 与手工方法相比,开发的软件显著减少了数据提取时间.
  • 实施各种可视化技术用于oART数据分析.
  • 该软件提取关键规划数据,但不包括CT扫描或剂量文件等原始DICOM信息.

结论:

  • 开发的软件提供了一种简化的方法来收集,分析和可视化oART数据.
  • 这种工具提高了专用oART平台的数据可访问性.
  • 需要进一步开发,以改善全面的oART数据的访问和分析能力.