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OBJECTIVES: The objectives are to describe the disadvantages of the area under the receiver operating characteristic curve (ROC AUC) to measure diagnostic test performance and to propose an alternative based on net benefit. METHODS: We use a narrative review supplemented by data from a study of computer-assisted detection for CT colonography. RESULTS: We identified problems with ROC AUC. Confidence scoring by readers was highly non-normal, and score distribution was bimodal. Consequently, ROC curves were highly extrapolated with AUC mostly dependent on areas without patient data. AUC depended on the method used for curve fitting. ROC AUC does not account for prevalence or different misclassification costs arising from false-negative and false-positive diagnoses. Change in ROC AUC has little direct clinical meaning for clinicians. An alternative analysis based on net benefit is proposed, based on the change in sensitivity and specificity at clinically relevant thresholds. Net benefit incorporates estimates of prevalence and misclassification costs, and it is clinically interpretable since it reflects changes in correct and incorrect diagnoses when a new diagnostic test is introduced. CONCLUSIONS: ROC AUC is most useful in the early stages of test assessment whereas methods based on net benefit are more useful to assess radiological tests where the clinical context is known. Net benefit is more useful for assessing clinical impact. KEY POINTS: • The area under the receiver operating characteristic curve (ROC AUC) measures diagnostic accuracy. • Confidence scores used to build ROC curves may be difficult to assign. • False-positive and false-negative diagnoses have different misclassification costs. • Excessive ROC curve extrapolation is undesirable. • Net benefit methods may provide more meaningful and clinically interpretable results than ROC AUC.

Original publication




Journal article


Eur Radiol

Publication Date





932 - 939


Area Under Curve, Colonography, Computed Tomographic, Diagnostic Imaging, Humans, ROC Curve, Reproducibility of Results, Sensitivity and Specificity