Receiver Operating Characteristic Curves and Their Use in Radiology

TLDR

Sensitivity and specificity vary with the chosen cut point, and the ROC curve plots sensitivity against false‑positive rate across all possible cut points. The paper discusses the advantages of the ROC curve for defining test accuracy, constructing the curve, and selecting the optimal cut point. It describes and compares several summary accuracy measures, such as the percentage of correct diagnoses and the area under the ROC curve. Two radiologic research examples illustrate the application of ROC curves.

Abstract

Sensitivity and specificity are the basic measures of accuracy of a diagnostic test; however, they depend on the cut point used to define "positive" and "negative" test results. As the cut point shifts, sensitivity and specificity shift. The receiver operating characteristic (ROC) curve is a plot of the sensitivity of a test versus its false-positive rate for all possible cut points. The advantages of the ROC curve as a means of defining the accuracy of a test, construction of the ROC, and identification of the optimal cut point on the ROC curve are discussed. Several summary measures of the accuracy of a test, including the commonly used percentage of correct diagnoses and area under the ROC curve, are described and compared. Two examples of ROC curve application in radiologic research are presented.