Receiver Operating Characteristic (ROC) Curve: Practical Review for Radiologists

Abstract

Receiver Operating Characteristic (ROC) Curve: Practical Review for RadiologistsThe receiver operating characteristic (ROC) curve, which is defined as a plot of test sensitivity as the y coordinate versus its 1-specificity or false positive rate (FPR) as the x coordinate, is an effective method of evaluating the performance of diagnostic tests.The purpose of this article is to provide a nonmathematical introduction to ROC analysis.Important concepts involved in the correct use and interpretation of this analysis, such as smooth and empirical ROC curves, parametric and nonparametric methods, the area under the ROC curve and its 95% confidence interval, the sensitivity at a particular FPR, and the use of a partial area under the ROC curve are discussed.Various considerations concerning the collection of data in radiological ROC studies are briefly discussed.An introduction to the software frequently used for performing ROC analyses is also presented.he receiver operating characteristic (ROC) curve, which is defined as a plot of test sensitivity as the y coordinate versus its 1-specificity or false positive rate (FPR) as the x coordinate, is an effective method of evaluating the quality or performance of diagnostic tests, and is widely used in radiology to evaluate the performance of many radiological tests.Although one does not necessarily need to understand the complicated mathematical equations and theories of ROC analysis, understanding the key concepts of ROC analysis is a prerequisite for the correct use and interpretation of the results that it provides.This article is a nonmathematical introduction to ROC analysis for radiologists who are not mathematicians or statisticians.Important concepts are discussed along with a brief discussion of the methods of data collection to use in radiological ROC studies.An introduction to the software programs frequently used for performing ROC analyses is also presented. What is the ROC Curve?Sensitivity and specificity, which are defined as the number of true positive decisions/the number of actually positive cases and the number of true negative decisions/the number of actually negative cases, respectively, constitute the basic measures of performance of diagnostic tests (Table 1).When the results of a test fall into one of two obviously defined categories, such as either the presence or absence of a disease, then the test has only one pair of sensitivity and specificity values.However, in many diagnostic situations, making a decision in a binary mode is both difficult and impractical.Image findings may not be obvious or clean-cut.There may be a considerable variation in the diagnostic confidence levels between the radiologists who interpret the findings.As a result, a single pair of sensitivity and specificity values is