title: Performance of computer-aided detection of pulmonary nodules in low-dose CT: comparison with double reading by nodule volume.
abstract: OBJECTIVE
  To evaluate performance of computer-aided detection (CAD) beyond double reading for pulmonary nodules on low-dose computed tomography (CT) by nodule volume.
  
  
  METHODS
  A total of 400 low-dose chest CT examinations were randomly selected from the NELSON lung cancer screening trial. CTs were evaluated by two independent readers and processed by CAD. A total of 1,667 findings marked by readers and/or CAD were evaluated by a consensus panel of expert chest radiologists. Performance was evaluated by calculating sensitivity of pulmonary nodule detection and number of false positives, by nodule characteristics and volume.
  
  
  RESULTS
  According to the screening protocol, 90.9 % of the findings could be excluded from further evaluation, 49.2 % being small nodules (less than 50 mm(3)). Excluding small nodules reduced false-positive detections by CAD from 3.7 to 1.9 per examination. Of 151 findings that needed further evaluation, 33 (21.9 %) were detected by CAD only, one of them being diagnosed as lung cancer the following year. The sensitivity of nodule detection was 78.1 % for double reading and 96.7 % for CAD. A total of 69.7 % of nodules undetected by readers were attached nodules of which 78.3 % were vessel-attached.
  
  
  CONCLUSIONS
  CAD is valuable in lung cancer screening to improve sensitivity of pulmonary nodule detection beyond double reading, at a low false-positive rate when excluding small nodules.
  
  
  CONCLUSIONS
  • Computer-aided detection (CAD) has known advantages for computed tomography (CT).                                                                              • Combined CAD/nodule size cut-off parameters assist CT lung cancer screening.                                                                              • This combination improves the sensitivity of pulmonary nodule detection by CT.                                                                              • It increases the positive predictive value for cancer detection.
authors: Center for Medical Imaging - North East Netherlands, Department of Radiology, University of Groningen/University Medical Center Groningen, P.O. Box 30.001, 9700RB, Groningen, the Netherlands.
authors: Yingru
authors: Y
authors: Zhao
authors: Geertruida H
authors: GH
authors: de Bock
authors: Rozemarijn
authors: R
authors: Vliegenthart
authors: Rob J
authors: RJ
authors: van Klaveren
authors: Ying
authors: Y
authors: Wang
authors: Luca
authors: L
authors: Bogoni
authors: Pim A
authors: PA
authors: de Jong
authors: Willem P
authors: WP
authors: Mali
authors: Peter M A
authors: PM
authors: van Ooijen
authors: Matthijs
authors: M
authors: Oudkerk
mesh_terms: D000368:Aged; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D008875:Middle Aged; D055613:Multiple Pulmonary Nodules / Q000000981:diagnostic imaging* / Q000473:pathology; D011829:Radiation Dosage; D014057:Tomography, X-Ray Computed / Q000379:methods*