ROC Analysis of Imaging System Evaluations

成像系统评估的 ROC 分析

• 批准号: 6621015
• 负责人: DAVID GUR
• 金额: $ 24.39万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-05-01 至 2006-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6621015
• 关键词: ROC; Analysis; Imaging; System; Evaluations

Many of the investigations that are now carried out during imaging systems' evaluations and comparisons include interpretation (reading) studies of normal and abnormal cases that are evaluated by receiver operating characteristic analyses (ROC) or derivatives thereof. Unfortunately, limitations in present methodology for systems evaluations have results in extremely complicated and costly study designs and data methodology to apply a different formulation of a well- known statistic to this problem and verify the proposed approach using both simulation and a large set of experimental data available to us. The Wilcoxon statistic, a non-parametric method often used to obtain the area under the ROC curve when scoring data is used, has a formulation that compares all possible pairs of observations of a negative and a positive case. Although this conceptualization of the problem can be used to further develop and extend ROC methodology. We plan to exploit this conceptualization to develop a sensitive statistical test to compare the areas under two or more ROC curses in a manner that will enable us to assess the effect of selected variables on the area under the ROC curve as to summarize diagnostic accuracy in the multi-disease setting and to quality the difference in diagnostic accuracy of two modalities (or more) as a function of different parameters and possibly after the incorporation of utility function.

现在在成像系统的评估和比较期间进行的许多调查包括通过接收器工作特性分析(ROC)或其导数来评估的正常和异常情况的解释(读取)研究。不幸的是，目前系统评估方法的局限性导致了极其复杂和昂贵的研究设计和数据方法，以将众所周知的统计的不同公式应用于这个问题，并使用模拟和我们可用的大量实验数据来验证所提出的方法。Wilcoxon统计量是一种非参数方法，通常用于在使用评分数据时获得ROC曲线下的面积，它有一个公式，可以比较所有可能的负值和正值观测对。虽然这种对问题的概念化可以用于进一步发展和扩展ROC方法论。我们计划利用这一概念化来开发一种敏感的统计测试来比较两个或更多ROC曲线下的区域，以使我们能够评估所选变量对ROC曲线下区域的影响，总结多疾病环境中的诊断准确性，并将两种(或更多)模式的诊断准确性的差异定性为不同参数的函数，并且可能在纳入效用函数之后。