自动放射学:可复制放射学研究的框架
Piotr Woznicki1, Fabian Laqua1, Thorsten Bley1
1Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany.
Frontiers in radiology
|July 26, 2023
概括
本研究介绍了AutoRadiomics,这是一个用于可复制放射学分析的开源框架,证明了其在复制不同临床数据集中的分类结果的有效性.
科学领域:
- 医疗成像医学成像
- 机器学习 机器学习
- 无线电学 (Radiomics) 是一种无线电学.
背景情况:
- 机器学习应用于放射学特征,在临床应用中显示出前景.
- 标准化和可重复性对于将放射学转化为临床实践至关重要.
研究的目的:
- 开发和评估一个新的,直观的开源框架,用于可重复的放射学工作流程.
- 方便所有数据分析步骤,从预处理到模型评估,以简单可重复的方式.
主要方法:
- 开发了一个开源框架 (AutoRadiomics),可以进行图像预处理,特征提取,选择,建模和评估.
- 使用了一个带有交互式用户界面的Web应用程序来实现无需编程技能的可复制性.
- 评估了包括WORC,前列腺-UCLA和PROSTATEx在内的七个临床应用中的八个公共数据集的框架.
主要成果:
- 自动辐射学成功创建和优化了辐射学模型,复制了以前的发现,AUC范围从0.56到0.93.
- 在训练和测试组之间没有观察到任何显著的过度装配.
- 在不同数据集中实现了前列腺癌检测的不同AUC (0.61-0.77).
结论:
- 自动放射学 (AutoRadiomics) 是放射学研究的一个强大且广泛适用的工具,可以作为一个全面的,模块化的或探索性的解决方案.
- 该框架的有效性在各种临床数据集中得到证实.
- 该框架和相关代码可供公众使用和进一步开发.
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