突破门:开发使用计算机辅助胸部X射线分析的多变量模型,用于结核病选
Coralie Geric1, Gamuchirai Tavaziva2, Marianne Breuninger3
1McGill International TB Centre, Research Institute of the McGill University Health Centre, Montreal, Canada; Department of Medicine, McGill University, Montreal, Canada; Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, Canada.
使用计算机辅助检测 (CAD) 分数进行胸部X射线 (CXR) 分类的多变量模型与标准值方法相比,显著提高了结核病 (TB) 诊断特异性. 这种方法提高了结核病检测的诊断准确性.
科学领域:
- 医疗成像医学成像
- 传染病诊断 传染病诊断 传染病诊断
- 生物统计学 生物统计学
背景情况:
- 计算机辅助检测 (CAD) 软件将结核病 (TB) 兼容的胸部X射线 (CXR) 异常量化为连续分数.
- 目前的做法涉及选择二进制CXR分类的值,这可能会限制诊断准确性.
研究的目的:
- 评估使用多变量建模中的CAD分数用于结核病选的替代方法的诊断准确性.
- 将多变量模型的性能与当前基于值的CAD应用程序进行比较.
主要方法:
- 从四项研究中汇集了个体患者数据.
- 使用后勤回归来模型微生物学确认的结核病,包括CAD分数,研究地点,年龄,性别,艾滋病毒状况和以前的结核病.
- 在多变量模型和基于值的CAD方法之间的目标灵敏度 (≥90%) 的比较特异性.
主要成果:
- 多变量模型显示出出色的性能 (AUC:软件A,0.91;软件B,0.92).
- 与90%灵敏度的值得分相比,多变量模型增加了特异性 (例如,软件A:75%与71%相比;软件B:75%与69%).
- 包括4733名参与者;17%患有结核病.
结论:
- 将CAD分数纳入多变量模型显著超过了目前用于结核病诊断的基于值的CXR分类的做法.
- 这种精细的方法可以提高使用胸部X射线检测结核病的特异性.
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