Determining Roe and Metz model parameters for simulating multireader multicase confidence-of-disease rating data based on real-data or conjectured Obuchowski-Rockette parameter estimates.
Determining Roe and Metz model parameters for simulating multireader multicase confidence-of-disease rating data based on real-data or conjectured Obuchowski-Rockette parameter estimates.
复制标题
根据实际数据或推测的 Obuchowski-Rockette 参数估计确定 Roe 和 Metz 模型参数，用于模拟多读者多病例疾病置信度评级数据。
DOI：
10.1117/12.2550541
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Hillis,StephenL
中科院分区：
文献类型：
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作者：
Hillis,StephenL
最常用的模型是1997年提出的Roe和Metz模型，用于模拟MRMC数据，以模拟诊断成像研究中的疾病置信度评级。RM模型基于底层的等方差二正态模型为每个读者生成连续的疾病置信度评级，正常和异常评级分布之间的间隔在不同的读者中有所不同。RM模型的一个问题是，参数是根据评级分布表示的，而不是根据阅读器的性能结果表示的。由于MRMC分析结果几乎总是根据阅读器的性能结果来表示，而不是根据评级数据分布来表示，因此很难评估模拟数据与实践中遇到的MRMC数据的相似程度。为了纠正这种情况，最近Hillis（2018年）推导出了表达参数的公式，这些参数描述了从RM模拟数据计算的经验AUC结果作为RM参数的函数的分布。对这些值的检查揭示了模拟数据的真实性的几个问题。本文通过提供逆映射来继续这项工作，即通过推导一种将RM参数表示为AUC经验分布参数的函数的算法。这一结果将能够创建一个重新校准的RM模型，该模型更接近于模拟真实数据研究。
The most frequently used model for simulating MRMC data that emulate confidence-of-disease ratings from diagnostic imaging studies has been the Roe and Metz model, proposed in 1997. The RM model generates continuous confidence-of-diseases ratings based on an underlying equal-variance binormal model for each reader, with the separation between the normal and abnormal rating distributions varying across readers. A problem with the RM model is that the parameters are expressed in terms of the rating distributions, as opposed to the reader performance outcomes. Because MRMC analysis results are almost always expressed in terms of the reader performance outcomes, and not in terms of the rating data distributions, it has been difficult to assess how similar the simulated data are to MRMC data encountered in practice. To remedy this situation, recently Hillis (in 2018) derived formulas expressing parameters that describe the distribution of empirical AUC outcomes computed from RM simulated data as functions of the RM parameters. An examination of these values revealed several problems with the realism of the simulated data. This paper continues that work by providing the inverse mapping, i.e., by deriving an algorithm that expresses the RM parameters as functions of the AUC empirical distribution parameters. This result will enable the creation of a recalibrated RM model that more closely emulates real-data studies.