title: [Computer-aided detection of lung nodules on thin collimation MDCT: impact on radiologists' performance].
abstract: OBJECTIVE
  Evaluate the improvement in detecting lung nodules when using multidetector CT (MDCT) computer-assisted diagnosis (CAD).
  
  
  METHODS
  Three radiologists (R1, R2, R3) with different levels of experience independently interpreted 30 MDCT examinations of the thorax taken for screening purposes, first without and then with CAD. The diagnosis was established by two of the three radiologists interpreting the images together, assisted by the CAD.
  
  
  RESULTS
  The consensus reading identified 133 nodules, 61 (46%) of which were 4 mm or larger. The sensitivity values in the detection of nodules before and after using the CAD were 54% and 80% (R1), 38% and 71% (R2), and 70% and 88% (R3), respectively. When considering only the nodules that were 4 mm or larger, the sensitivity values varied before and after using the CAD, from 62% to 95% (R1), from 41% to 84% (R2), and from 74% to 92% (R3). By combining two by two the three radiologists' results obtained without the CAD, the sensitivity values were 65%, 83%, and 77%, respectively, for all the nodules, and 70%, 85%, and 77% for the nodules that were 4 mm or larger. The CAD induced a total of 105 false-positive results, with a mean of 3.5 per examination.
  
  
  CONCLUSIONS
  The lung nodules missed by the radiologist can be detected if the CAD is used as a second reader. The CAD can be at least as beneficial as the use of a second independent reader.
authors.affiliation: Service de Radiologie, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Université Pierre et Marie Curie, Paris Cedex 13, France.
authors.forename: B
authors.initials: B
authors.lastname: Brochu
authors.forename: C
authors.initials: C
authors.lastname: Beigelman-Aubry
authors.forename: J-L
authors.initials: JL
authors.lastname: Goldmard
authors.forename: P
authors.initials: P
authors.lastname: Raffy
authors.forename: P A
authors.initials: PA
authors.lastname: Grenier
authors.forename: O
authors.initials: O
authors.lastname: Lucidarme
mesh_terms: D000328:Adult; D000368:Aged; D003936:Diagnosis, Computer-Assisted; D005189:False Positive Reactions; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008403:Mass Screening; D008875:Middle Aged; D015588:Observer Variation; D010865:Pilot Projects; D011446:Prospective Studies; D013902:Radiography, Thoracic; D011871:Radiology / Q000592:standards*; D012680:Sensitivity and Specificity; D012907:Smoking; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging; D036542:Tomography, Spiral Computed; D014057:Tomography, X-Ray Computed / Q000379:methods*