Park SH, Goo JM, Jo CH - Korean J Radiol (2004 Jan-Mar)

Related In: Results  -  Collection

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Figure 4: Schematic illustration of a comparison between the sensitivities of two ROC curves (A and B) at a particular false positive rate and comparison between two partial ROC areas. For this example, the false positive rate and partial range of false positive rate (e1 - e2) are arbitrarily chosen as 0.7 and 0.6 ~ 0.8, respectively.
Mentions: One way to consider only a portion of an ROC curve is to use the ROC curve to estimate the sensitivity at a particular FPR, and to compare the sensitivities of different ROC curves at a particular FPR (Fig. 4). Another way is to use the partial area under the ROC curve (Fig. 4) (11, 12). Partial ROC area is defined as the area between two FPRs or between two sensitivities. The partial area under the ROC curve between two FPRs, FPR1 = e1 and FPR2 = e2, can be denoted as A(e1 ≤ FPR ≤ e2) (2). Unlike AUC, whose maximum possible value is always 1, the magnitude of the partial area under the ROC curve is dependent on the two FPRs chosen. Therefore, the standardization of the partial area by dividing it by its maximum value is recommended and Jiang et al. (12) referred to this standardized partial area as the partial area index. The maximum value of the partial area between FPR1 = e1 and FPR2 = e2 is equal to the width of the interval, e2 - e1. The partial area index is interpreted as the average sensitivity for the range of FPRs or specificities chosen (1, 2).

Bottom Line: 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.

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Affiliation: Department of Radiology, Seoul National University College of Medicine and Institute of Radiation Medicine, SNUMRC, Seoul, Korea.

ABSTRACT
The receiver operating characteristic (ROC) curve, which is defined as a plot of test sensitivity as they 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.

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