title: Computer-aided Detection Fidelity of Pulmonary Nodules in Chest Radiograph.
abstract: OBJECTIVE
  The most ubiquitous chest diagnostic method is the chest radiograph. A common radiographic finding, quite often incidental, is the nodular pulmonary lesion. The detection of small lesions out of complex parenchymal structure is a daily clinical challenge. In this study, we investigate the efficacy of the computer-aided detection (CAD) software package SoftView™ 2.4A for bone suppression and OnGuard™ 5.2 (Riverain Technologies, Miamisburg, OH, USA) for automated detection of pulmonary nodules in chest radiographs.
  
  
  METHODS
  We retrospectively evaluated a dataset of 100 posteroanterior chest radiographs with pulmonary nodular lesions ranging from 5 to 85 mm. All nodules were confirmed with a consecutive computed tomography scan and histologically classified as 75% malignant. The number of detected lesions by observation in unprocessed images was compared to the number and dignity of CAD-detected lesions in bone-suppressed images (BSIs).
  
  
  RESULTS
  SoftView™ BSI does not affect the objective lesion-to-background contrast. OnGuard™ has a stand-alone sensitivity of 62% and specificity of 58% for nodular lesion detection in chest radiographs. The false positive rate is 0.88/image and the false negative (FN) rate is 0.35/image. From the true positive lesions, 20% were proven benign and 80% were malignant. FN lesions were 47% benign and 53% malignant.
  
  
  CONCLUSIONS
  We conclude that CAD does not qualify for a stand-alone standard of diagnosis. The use of CAD accompanied with a critical radiological assessment of the software suggested pattern appears more realistic. Accordingly, it is essential to focus on studies assessing the quality-time-cost profile of real-time (as opposed to retrospective) CAD implementation in clinical diagnostics.
authors.affiliation: Department of Experimental Radiology, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Friedrich-Schiller University, Jena, Germany; Institute of Radiology, Suedharz Hospital Nordhausen gGmbH, Nordhausen, Germany.
authors.forename: Nikolaos
authors.initials: N
authors.lastname: Dellios
authors.affiliation: Department of Experimental Radiology, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Friedrich-Schiller University, Jena, Germany.
authors.forename: Ulf
authors.initials: U
authors.lastname: Teichgraeber
authors.affiliation: Institute of Radiology, Suedharz Hospital Nordhausen gGmbH, Nordhausen, Germany.
authors.forename: Robert
authors.initials: R
authors.lastname: Chelaru
authors.affiliation: Institute of Radiology, Suedharz Hospital Nordhausen gGmbH, Nordhausen, Germany.
authors.forename: Ansgar
authors.initials: A
authors.lastname: Malich
authors.affiliation: Institute of Radiology, Suedharz Hospital Nordhausen gGmbH, Nordhausen, Germany.
authors.forename: Ismini E
authors.initials: IE
authors.lastname: Papageorgiou
keywords: Bone suppression imaging; chest radiograph; computer-aided detection; lung cancer; pulmonary nodule