多变量测试和效果大小测量用于对放射性特征进行批量效应评估
Hannah Horng1,2,3, Christopher Scott4, Stacey Winham4
1Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA. hannah.horng@gmail.com.
Scientific reports
|June 17, 2024
概括
新的统计方法PERMANOVA和RESI改善了放射学数据中批量效应的检测和量化,提高了精准医学应用的可重复性.
科学领域:
- 医学成像分析 医学成像分析
- 生物统计学 生物统计学
- 无线电学 (Radiomics) 是一种放射学.
背景情况:
- 放射学分析对精密医学具有前景,但图像采集的变性引入了损害可重现性的批量效应.
- 目前在放射学中评估批量效应的方法不一致,阻碍了可靠的下游预测分析.
研究的目的:
- 引入和评估PERMANOVA和RESI作为强大的统计工具,用于量化放射学数据中的批量效应.
- 为了比较PERMANOVA和RESI的性能与标准单变量统计测试进行批量效应评估.
主要方法:
- 使用了多变量统计测试PERMANOVA和强大的效应大小指数 (RESI).
- 使用模拟放射性特征和真实放射性特征从全场数字造乳镜 (FFDM) 数据评估的方法.
- 我们比较了PERMANOVA对单变量统计测试的强度和RESI对大样本大小的解释性.
主要成果:
- 与检测批量效应的标准单变量测试相比,PERMANOVA显示出更高的统计能力.
- RESI有效量化了特定地点变化的效果大小,即使使用非常大的数据集.
- 这两种方法在放射学特征中的批量效应的表征方面都非常有价值.
结论:
- 在放射学研究中,PERMANOVA 和 RESI 提供了更强大,更易于解释的方法来检测和量化批量效应.
- 这些方法可以提高精准医学放射学分析的可复制性和可靠性.
- 加强批量效应评估对于推进放射学临床应用至关重要.
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