title: Influence of nodule detection software on radiologists' confidence in identifying pulmonary nodules with computed tomography.
abstract: OBJECTIVE
  With advances in technology, detection of small pulmonary nodules is increasing. Nodule detection software (NDS) has been developed to assist radiologists with pulmonary nodule diagnosis. Although it may increase sensitivity for small nodules, often there is an accompanying increase in false-positive findings. We designed a study to examine the extent to which computed tomography (CT) NDS influences the confidence of radiologists in identifying small pulmonary nodules.
  
  
  METHODS
  Eight radiologists (readers) with different levels of experience examined thoracic CT scans of 131 cases and identified all the clinically relevant pulmonary nodules. The reference standard was established by an expert, dedicated thoracic radiologist. For each nodule, the readers recorded nodule size, density, location, and confidence level. Two weeks (or more) later, the readers reinterpreted the same scans; however, this time they were provided marks, when present, as indicated by NDS and asked to reassess their level of confidence. The effect of NDS on changes in reader confidence was assessed using multivariable generalized linear regression models.
  
  
  RESULTS
  A total of 327 unique nodules were identified. Declines in confidence were significantly (P<0.05) associated with the absence of an NDS mark and smaller nodules (odds ratio=71.0, 95% confidence interval =14.8-339.7). Among nodules with pre-NDS confidence less than 100%, increases in confidence were significantly (P<0.05) associated with the presence of an NDS mark (odds ratio=6.0, 95% confidence interval =2.7-13.6) and larger nodules. Secondary findings showed that NDS did not improve reader diagnostic accuracy.
  
  
  CONCLUSIONS
  Although in this study NDS does not seem to enhance reader accuracy, the confidence of the radiologists in identifying small pulmonary nodules with CT is greatly influenced by NDS.
authors.affiliation: Department of Medicine, Division of Biostatistics and Epidemiology, Medical University of South Carolina, Charleston, SC 29425, USA. nieterpj@musc.edu
authors.forename: Paul J
authors.initials: PJ
authors.lastname: Nietert
authors.forename: James G
authors.initials: JG
authors.lastname: Ravenel
authors.forename: Katherine K
authors.initials: KK
authors.lastname: Taylor
authors.forename: Gerard A
authors.initials: GA
authors.lastname: Silvestri
mesh_terms: D003951:Diagnostic Errors; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging; D015588:Observer Variation; D011857:Radiographic Image Interpretation, Computer-Assisted; D012984:Software / Q000592:standards; D003074:Solitary Pulmonary Nodule / Q000175:diagnosis* / Q000000981:diagnostic imaging; D014057:Tomography, X-Ray Computed / Q000379:methods*