Development of methodology for evaluation of diagnostic performance has been an essential part of our department’s research for many decades. The primary focus of this work has been on Receiver Operating Characteristic (ROC) analysis, which describes the accuracy of a diagnostic modality, for a particular clinical task, in terms of its trade-off between sensitivity and specificity [2-4]. Three early insights of our group [5-9] were that novel ROC methodology was needed to meet the needs of medical image evaluation; that newly proposed statistical methods are of little value without careful validation of their behavior using both simulated and real datasets; and that development and free distribution of convenient, reliable software for ROC analysis could substantially encourage acceptance of the methodology.
Currently, software developed in accord with this theme (ROCFIT, LABROC4, PROPROC, CORROC, INDROC, ROCKIT and LABMRMC) or in collaboration with others (DBM-MRMC, with the University of Iowa) is used by thousands of registered users. We are now in the final stages of developing and releasing a Java-based, nearly platform-independent software package for ROC analysis that will include many alternative ROC analysis approaches, with release expected (as of 04/16/10) in early July of 2010 (alpha versions are available upon request, which can be submitted here).
Our recent progress has included the addition of many non-parametric methods for analysis of ROC data  and a review of the medical-imaging literature to determine the spectrum of ROC methods used in published observer performance studies . Current research efforts that eventually will be incorporated into our released software include development of “proper” ROC models for analysis of both partially- and fully-paired datasets (to be released soon), investigation of Bayesian approaches to ROC curve fitting, and development of quantitative methods that relate different test-result scales to each other and to individual operating points on ROC curves.
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