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Fundamental Measures of Diagnostic Examination Performance: Usefulness for Clinical Decision Making and Research¹

¹ From the Departments of Radiology and Epidemiology, University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104. Received June 25, 2001; revision requested August 2; revision received May 3, 2002; accepted May 15. Address correspondence to the author (e-mail: langlotz@rad.upenn.edu).

View larger version (24K): [in a new window]   Figure 1. Six-category scale for rating the presence or absence of breast cancer. By varying a cutoff for rating categories, one can create five two-by-two tables of data from which sensitivity and specificity values can be calculated (sensitivity and specificity #1-#5).

View larger version (26K): [in a new window]   Figure 2. Sample two-by-six table showing the results of an ROC study of breast cancer identification in 200 patients. The two-by-two table at the bottom can be created by setting a cutoff between the ratings of definitely benign and probably benign. This cutoff corresponds to sensitivity #1 and specificity #1 in Figure 1. Sensitivity and specificity values are calculated by using the two-by-two table data. As expected, use of the more flexible criteria leads to high sensitivity but low specificity.

View larger version (23K): [in a new window]   Figure 3. Sample ROC curve. This curve is a plot of sensitivity versus (1 - specificity). The 0,0 point and 1,1 point are included by default to represent the situation in which all images are considered to be either negative or positive, respectively. FPF = false-positive fraction, TPF = true-positive fraction.

View larger version (23K): [in a new window]   Figure 4. Flowchart depicts a simulated population of 20,050 low-risk asymptomatic women who might be screened with breast MR imaging. The assumed prevalence of cancer in this screening population of 50 women with and 20,000 women without cancer is 0.25%. MRI+ and MRI- = positive and negative MR imaging results, respectively.