title: Predicting readers' diagnostic accuracy with a new CAD algorithm.
abstract: OBJECTIVE
  Before computer-aided detection (CAD) algorithms can be used in clinical practice, they must be shown to improve readers' diagnostic accuracy over their unaided performance. This is usually accomplished through a large multireader, multicase (MRMC) clinical trial. It is burdensome, however, for an MRMC study to be performed with each new release of a CAD algorithm. The aim of this report is to present an approach for building models to predict readers' accuracy with a new CAD algorithm.
  
  
  METHODS
  A modeling approach for predicting readers' results with a new CAD algorithm is described. Multiple-variable logistic regression was used to build models for readers' sensitivity and false-positive rate, given the results of an MRMC study with an older CAD algorithm and the stand-alone performance results of a new CAD algorithm. Data from a large lung MRMC CAD trial are used to illustrate the modeling approach and test the ability of the models to predict readers' accuracy with the new CAD algorithm.
  
  
  RESULTS
  The model overestimated the readers' actual sensitivity with the new CAD algorithm, but this did not reach statistical significance (0.621 vs 0.603, P = .147). The observed and predicted false-positive rates also did not differ significantly (0.275 vs 0.285, P = .250).
  
  
  CONCLUSIONS
  Using one clinical study as a test case, it is shown that the modeling approach is feasible. More testing of the approach is needed to determine if and under what circumstances it can be used as an alternative to a full-scale MRMC study. Meanwhile, the approach can be used to determine if a new CAD algorithm is likely to improve readers' accuracy before embarking on a full-scale MRMC study.
authors.affiliation: Cleveland Clinic Foundation, Department of Quantitative Health Sciences, Cleveland, OH 44195, USA. obuchon@ccf.org
authors.forename: Nancy A
authors.initials: NA
authors.lastname: Obuchowski
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D002983:Clinical Competence; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D016015:Logistic Models; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D013902:Radiography, Thoracic; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging; D014057:Tomography, X-Ray Computed