title: Benefit of computer-aided detection analysis for the detection of subsolid and solid lung nodules on thin- and thick-section CT.
abstract: OBJECTIVE
  The objective of our study was to evaluate the impact of computer-aided detection (CAD) on the identification of subsolid and solid lung nodules on thin- and thick-section CT.
  
  
  METHODS
  For 46 chest CT examinations with ground-glass opacity (GGO) nodules, CAD marks computed using thin data were evaluated in two phases. First, four chest radiologists reviewed thin sections (reader(thin)) for nodules and subsequently CAD marks (reader(thin) + CAD(thin)). After 4 months, the same cases were reviewed on thick sections (reader(thick)) and subsequently with CAD marks (reader(thick) + CAD(thick)). Sensitivities were evaluated. Additionally, reader(thick) sensitivity with assessment of CAD marks on thin sections was estimated (reader(thick) + CAD(thin)).
  
  
  RESULTS
  For 155 nodules (mean, 5.5 mm; range, 4.0-27.5 mm)-74 solid nodules, 22 part-solid (part-solid nodules), and 59 GGO nodules-CAD stand-alone sensitivity was 80%, 95%, and 71%, respectively, with three false-positives on average (0-12) per CT study. Reader(thin) + CAD(thin) sensitivities were higher than reader(thin) for solid nodules (82% vs 57%, p < 0.001), part-solid nodules (97% vs 81%, p = 0.0027), and GGO nodules (82% vs 69%, p < 0.001) for all readers (p < 0.001). Respective sensitivities for reader(thick), reader(thick) + CAD(thick), reader(thick) + CAD(thin) were 40%, 58% (p < 0.001), and 77% (p < 0.001) for solid nodules; 72%, 73% (p = 0.322), and 94% (p < 0.001) for part-solid nodules; and 53%, 58% (p = 0.008), and 79% (p < 0.001) for GGO nodules. For reader(thin), false-positives increased from 0.64 per case to 0.90 with CAD(thin) (p < 0.001) but not for reader(thick); false-positive rates were 1.17, 1.19, and 1.26 per case for reader(thick), reader(thick) + CAD(thick), and reader(thick) + CAD(thin), respectively.
  
  
  CONCLUSIONS
  Detection of GGO nodules and solid nodules is significantly improved with CAD. When interpretation is performed on thick sections, the benefit is greater when CAD marks are reviewed on thin rather than thick sections.
authors.affiliation: Department of Radiology, New York University Langone Medical Center, 560 First Ave, IRM 236, New York, NY 10016, USA.
authors.forename: Myrna C B
authors.initials: MC
authors.lastname: Godoy
authors.forename: Tae Jung
authors.initials: TJ
authors.lastname: Kim
authors.forename: Charles S
authors.initials: CS
authors.lastname: White
authors.forename: Luca
authors.initials: L
authors.lastname: Bogoni
authors.forename: Patricia
authors.initials: P
authors.lastname: de Groot
authors.forename: Charles
authors.initials: C
authors.lastname: Florin
authors.forename: Nancy
authors.initials: N
authors.lastname: Obuchowski
authors.forename: James S
authors.initials: JS
authors.lastname: Babb
authors.forename: Marcos
authors.initials: M
authors.lastname: Salganicoff
authors.forename: David P
authors.initials: DP
authors.lastname: Naidich
authors.forename: Vikram
authors.initials: V
authors.lastname: Anand
authors.forename: Sangmin
authors.initials: S
authors.lastname: Park
authors.forename: Ioannis
authors.initials: I
authors.lastname: Vlahos
authors.forename: Jane P
authors.initials: JP
authors.lastname: Ko
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D055613:Multiple Pulmonary Nodules / Q000000981:diagnostic imaging* / Q000473:pathology; D011857:Radiographic Image Interpretation, Computer-Assisted; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging* / Q000473:pathology; D014057:Tomography, X-Ray Computed