Statistical properties of a utility measure of observer performance compared to area under the ROC curve

Craig K. Abbey; Frank W. Samuelson; Brandon D. Gallas; John M. Boone; Loren T. Niklason

doi:10.1117/12.2007708

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28 March 2013 Statistical properties of a utility measure of observer performance compared to area under the ROC curve

Craig K. Abbey, Frank W. Samuelson, Brandon D. Gallas, John M. Boone, Loren T. Niklason

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Proceedings Volume 8673, Medical Imaging 2013: Image Perception, Observer Performance, and Technology Assessment; 86730D (2013) https://doi.org/10.1117/12.2007708
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

The receiver operating characteristic (ROC) curve has become a common tool for evaluating diagnostic imaging technologies, and the primary endpoint of such evaluations is the area under the curve (AUC), which integrates sensitivity over the entire false positive range. An alternative figure of merit for ROC studies is expected utility (EU), which focuses on the relevant region of the ROC curve as defined by disease prevalence and the relative utility of the task. However if this measure is to be used, it must also have desirable statistical properties keep the burden of observer performance studies as low as possible. Here, we evaluate effect size and variability for EU and AUC. We use two observer performance studies recently submitted to the FDA to compare the EU and AUC endpoints. The studies were conducted using the multi-reader multi-case methodology in which all readers score all cases in all modalities. ROC curves from the study were used to generate both the AUC and EU values for each reader and modality. The EU measure was computed assuming an iso-utility slope of 1.03. We find mean effect sizes, the reader averaged difference between modalities, to be roughly 2.0 times as big for EU as AUC. The standard deviation across readers is roughly 1.4 times as large, suggesting better statistical properties for the EU endpoint. In a simple power analysis of paired comparison across readers, the utility measure required 36% fewer readers on average to achieve 80% statistical power compared to AUC.

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Craig K. Abbey, Frank W. Samuelson, Brandon D. Gallas, John M. Boone, and Loren T. Niklason "Statistical properties of a utility measure of observer performance compared to area under the ROC curve", Proc. SPIE 8673, Medical Imaging 2013: Image Perception, Observer Performance, and Technology Assessment, 86730D (28 March 2013); https://doi.org/10.1117/12.2007708

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