Area under the Free-Response ROC Curve (FROC) and a Related Summary Index

• Published in: Biometrics Vol. 65; no. 1; pp. 247 - 256
• Main Authors: Bandos, Andriy I., Rockette, Howard E., Song, Tao, Gur, David
• Format: Journal Article
• Language: English
• Published: Malden, USA Blackwell Publishing Inc 01.03.2009; Blackwell Publishing; Blackwell Publishing Ltd
• Subjects: Algebra; Area Under Curve; Area under the FROC curve; Biometric Methodology; Biometrics; biometry; Bootstrap; Computer Simulation; Confidence interval; Data analysis; data collection; Datasets; Decision Making, Computer-Assisted; Diagnostic Imaging - standards; Diagnostic Imaging - statistics & numerical data; Estimating techniques; Estimators; FROC; Humans; image analysis; Imaging; Interval estimators; Radiology; Random variables; ROC; ROC Curve; Sample size; Simulation; Statistical variance; variance
• DOI: 10.1111/j.1541-0420.2008.01049.x
• ISSN: 0006-341X; 1541-0420

Free-response assessment of diagnostic systems continues to gain acceptance in areas related to the detection, localization, and classification of one or more "abnormalities" within a subject. A free-response receiver operating characteristic (FROC) curve is a tool for characterizing the performance of a free-response system at all decision thresholds simultaneously. Although the importance of a single index summarizing the entire curve over all decision thresholds is well recognized in ROC analysis (e.g., area under the ROC curve), currently there is no widely accepted summary of a system being evaluated under the FROC paradigm. In this article, we propose a new index of the free-response performance at all decision thresholds simultaneously, and develop a nonparametric method for its analysis. Algebraically, the proposed summary index is the area under the empirical FROC curve penalized for the number of erroneous marks, rewarded for the fraction of detected abnormalities, and adjusted for the effect of the target size (or "acceptance radius"). Geometrically, the proposed index can be interpreted as a measure of average performance superiority over an artificial "guessing" free-response process and it represents an analogy to the area between the ROC curve and the "guessing" or diagonal line. We derive the ideal bootstrap estimator of the variance, which can be used for a resampling-free construction of asymptotic bootstrap confidence intervals and for sample size estimation using standard expressions. The proposed procedure is free from any parametric assumptions and does not require an assumption of independence of observations within a subject. We provide an example with a dataset sampled from a diagnostic imaging study and conduct simulations that demonstrate the appropriateness of the developed procedure for the considered sample sizes and ranges of parameters.