title: Is the computer-aided detection scheme for lung nodule also useful in detecting lung cancer?
abstract: OBJECTIVE
  To evaluate the impact of a computer-aided diagnosis (CAD) system on the performance of observers for the detection of both lung nodules and lung cancers.
  
  
  METHODS
  One hundred fifty computed tomographic scans were evaluated. Database included 23 lung cancers (long diameter <20 mm), nodules stable for at least 2 years, and normal cases. Five chest radiologists and 5 radiology residents each independently recorded the locus of each nodule candidate and assigned a confidence score for the likelihood of nodule and malignancy without CAD; then, the interpretation was repeated with the use of CAD. A consensus panel of 2 chest radiologists served as a reference standard for the nodules. Histological confirmation was a reference standard for the cancers. The performances of the observers for the detection of nodules and cancer with and without CAD were compared using jackknife free-response receiver operating characteristic analysis.
  
  
  RESULTS
  The performance of detecting lung nodules was increased significantly with CAD for all radiologists and subgroups (P < 0.01). Although the overall performance of detecting lung cancers was not affected significantly with the use of CAD (P > 0.05), 4 lung cancers missed by 3 residents on their initial observation were additionally detected with CAD. Eighteen of 23 lung cancers were detected by CAD itself.
  
  
  CONCLUSIONS
  The overall radiologists' performance of detecting lung nodules was improved significantly with the use of CAD, whereas no statistical significance was observed for the detection of lung cancers. The use of CAD, however, contributed to the detection of additional lung cancers for less experienced readers.
authors.affiliation: Department of Radiology and Institute of Radiation Medicine, Seoul National University College of Medicine, Seoul, Korea. jmgoo@plaza.snu.ac.kr
authors.forename: Jin Mo
authors.initials: JM
authors.lastname: Goo
authors.forename: Hyae Young
authors.initials: HY
authors.lastname: Kim
authors.forename: Jeong Won
authors.initials: JW
authors.lastname: Lee
authors.forename: Hyun Ju
authors.initials: HJ
authors.lastname: Lee
authors.forename: Chang Hyun
authors.initials: CH
authors.lastname: Lee
authors.forename: Kyung Won
authors.initials: KW
authors.lastname: Lee
authors.forename: Tae Jung
authors.initials: TJ
authors.lastname: Kim
authors.forename: Kun Young
authors.initials: KY
authors.lastname: Lim
authors.forename: Seong Ho
authors.initials: SH
authors.lastname: Park
authors.forename: Kyongtae T
authors.initials: KT
authors.lastname: Bae
mesh_terms: D000230:Adenocarcinoma / Q000175:diagnosis*; D002282:Adenocarcinoma, Bronchiolo-Alveolar / Q000175:diagnosis*; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002289:Carcinoma, Non-Small-Cell Lung / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008168:Lung / Q000000981:diagnostic imaging; D008175:Lung Neoplasms / Q000175:diagnosis*; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012015:Reference Standards; D015203:Reproducibility of Results; D012189:Retrospective Studies; D014057:Tomography, X-Ray Computed / Q000379:methods*