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使用Python编程语言在辐射瘤学中有效和可靠的数据提取:试点研究

Rohit Singh Chauhan1,2, Anirudh Pradhan3, Anusheel Munshi2

  • 1Department of Physics, GLA University, Mathura, Uttar Pradesh, India.

Journal of medical physics
|June 21, 2023
PubMed
概括

使用Python脚本的自动化数据挖掘方法显著加快了从治疗计划系统 (TPS) 提取数据的速度. 这种方法比用于放射性瘤学应用的手动数据提取速度快6000倍以上,准确度更高.

关键词:
数据挖掘是一种数据挖掘.编程语言的编程语言.辐射疗法 辐射疗法软件 软件 软件 软件 软件时间管理时间管理.

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科学领域:

  • 在医疗保健中的数据科学.
  • 辐射瘤学 信息学 数据科学
  • 医学物理 医学物理

背景情况:

  • 数据科学越来越多地融入医疗保健,包括辐射瘤学.
  • 从治疗计划系统 (TPS) 中手动提取数据是耗时且容易出现错误的.
  • 在TPS中需要有效和准确的自动化数据提取方法.

研究的目的:

  • 开发和评估一种自动化数据挖掘方法,从TPS中提取患者和治疗数据.
  • 为了比较自动数据提取与手动方法的时间效率和准确性.

主要方法:

  • 开发了一个Python脚本,使用应用程序编程界面从TPS提取25个指定的功能.
  • 该脚本应用于接受外部光束辐射治疗的427名患者的数据.
  • 数据提取时间和准确性在自动脚本和手动提取之间进行了比较.

主要成果:

  • Python 脚本在 0.28 ± 0.03 分钟内提取了 427 名患者的 25 个特征,准确度为 100%.
  • 手动提取平均每患者4.5±0.33分钟,有错误和缺失的数据.
  • 自动化的方法比手工提取速度快6850倍以上.
  • 与手动方法相比,扩展功能数量对脚本提取时间的影响最小.

结论:

  • 公司内部的Python脚本为从TPS中提取计划数据提供了一种高效和准确的方法.
  • 自动化数据挖掘在辐射瘤学数据的速度和准确性方面明显优于手工提取.
  • 这种方法有可能简化放射瘤学的研究和临床工作流程.