医学
统计分析
数据科学
研究设计
临床研究设计
医学物理学
管理科学
人工智能
风险分析(工程)
计算机科学
统计
病理
临床试验
数学
经济
作者
Chaya S. Moskowitz,Mattea Welch,Michael A. Jacobs,Brenda F. Kurland,Amber L. Simpson
出处
期刊:Radiology
[Radiological Society of North America]
日期:2022-08-01
卷期号:304 (2): 265-273
被引量:22
标识
DOI:10.1148/radiol.211597
摘要
Rapid advances in automated methods for extracting large numbers of quantitative features from medical images have led to tremendous growth of publications reporting on radiomic analyses. Translation of these research studies into clinical practice can be hindered by biases introduced during the design, analysis, or reporting of the studies. Herein, the authors review biases, sources of variability, and pitfalls that frequently arise in radiomic research, with an emphasis on study design and statistical analysis considerations. Drawing on existing work in the statistical, radiologic, and machine learning literature, approaches for avoiding these pitfalls are described. © RSNA, 2022
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