title: New methods for using computer-aided detection information for the detection of lung nodules on chest radiographs.
abstract: OBJECTIVE
  To investigate two new methods of using computer-aided detection (CAD) system information for the detection of lung nodules on chest radiographs. We evaluated an interactive CAD application and an independent combination of radiologists and CAD scores.
  
  
  METHODS
  300 posteroanterior and lateral digital chest radiographs were selected, including 111 with a solitary pulmonary nodule (average diameter, 16 mm). Both nodule and control cases were verified by CT. Six radiologists and six residents reviewed the chest radiographs without CAD and with CAD (ClearRead +Detect™ 5.2; Riverain Technologies, Miamisburg, OH) in two reading sessions. The CAD system was used in an interactive manner; CAD marks, accompanied by a score of suspicion, remained hidden unless the location was queried by the radiologist. Jackknife alternative free response receiver operating characteristics multireader multicase analysis was used to measure detection performance. Area under the curve (AUC) and partial AUC (pAUC) between a specificity of 80% and 100% served as the measure for detection performance. We also evaluated the results of a weighted combination of CAD scores and reader scores, at the location of reader findings.
  
  
  RESULTS
  AUC for the observers without CAD was 0.824. No significant improvement was seen with interactive use of CAD (AUC = 0.834; p = 0.15). Independent combination significantly improved detection performance (AUC = 0.834; p = 0.006). pAUCs without and with interactive CAD were similar (0.128), but improved with independent combination (0.137).
  
  
  CONCLUSIONS
  Interactive CAD did not improve reader performance for the detection of lung nodules on chest radiographs. Independent combination of reader and CAD scores improved the detection performance of lung nodules.
  
  
  CONCLUSIONS
  (1) Interactive use of currently available CAD software did not improve the radiologists' detection performance of lung nodules on chest radiographs. (2) Independently combining the interpretations of the radiologist and the CAD system improved detection of lung nodules on chest radiographs.
authors.affiliation: Radboud University Medical Center Nijmegen, Nijmegen, Netherlands.
authors.forename: S
authors.initials: S
authors.lastname: Schalekamp
authors.forename: B
authors.initials: B
authors.lastname: van Ginneken
authors.forename: Bgf
authors.initials: B
authors.lastname: Heggelman
authors.forename: M
authors.initials: M
authors.lastname: Imhof-Tas
authors.forename: I
authors.initials: I
authors.lastname: Somers
authors.forename: M
authors.initials: M
authors.lastname: Brink
authors.forename: M
authors.initials: M
authors.lastname: Spee
authors.forename: Cm
authors.initials: C
authors.lastname: Schaefer-Prokop
authors.forename: N
authors.initials: N
authors.lastname: Karssemeijer
mesh_terms: D000328:Adult; D019540:Area Under Curve; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods*; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*