title: Limited value of shape, margin and CT density in the discrimination between benign and malignant screen detected solid pulmonary nodules of the NELSON trial.
abstract: OBJECTIVE
  To evaluate prospectively the value of size, shape, margin and density in discriminating between benign and malignant CT screen detected solid non-calcified pulmonary nodules.
  
  
  METHODS
  This study was institutional review board approved. For this study 405 participants of the NELSON lung cancer screening trial with 469 indeterminate or potentially malignant solid pulmonary nodules (>50mm3) were selected. The nodules were classified based on size, shape (round, polygonal, irregular) and margin (smooth, lobulated, spiculated). Mean nodule density and nodule volume were automatically generated by software. Analyses were performed by univariate and multivariate logistic regression. Results were presented as likelihood ratios (LR) with 95% confidence intervals (CI). Receiver operating characteristic analysis was performed for mean density as predictor for lung cancer.
  
  
  RESULTS
  Of the 469 nodules, 387 (83%) were between 50 and 500mm3, 82 (17%) >500mm3, 59 (13%) malignant, 410 (87%) benign. The median size of the nodules was 103mm3 (range 50-5486mm3). In multivariate analysis lobulated nodules had LR of 11 compared to smooth; spiculated nodules a LR of 7 compared to smooth; irregular nodules a LR of 6 compared to round and polygonal; volume a LR of 3. The mean nodule CT density did not predict the presence of lung cancer (AUC 0.37, 95% CI 0.32-0.43).
  
  
  CONCLUSIONS
  In solid non-calcified nodules larger than 50mm3, size and to a lesser extent a lobulated or spiculated margin and irregular shape increased the likelihood that a nodule was malignant. Nodule density had no discriminative power.
authors.affiliation: Department of Radiology, University Medical Center Groningen, University of Groningen, The Netherlands. dongming1996@hotmail.com
authors.forename: Dong Ming
authors.initials: DM
authors.lastname: Xu
authors.forename: Rob J
authors.initials: RJ
authors.lastname: van Klaveren
authors.forename: Geertruida H
authors.initials: GH
authors.lastname: de Bock
authors.forename: Anne
authors.initials: A
authors.lastname: Leusveld
authors.forename: Yingru
authors.initials: Y
authors.lastname: Zhao
authors.forename: Ying
authors.initials: Y
authors.lastname: Wang
authors.forename: Rozemarijn
authors.initials: R
authors.lastname: Vliegenthart
authors.forename: Harry J
authors.initials: HJ
authors.lastname: de Koning
authors.forename: Ernst T
authors.initials: ET
authors.lastname: Scholten
authors.forename: Johny
authors.initials: J
authors.lastname: Verschakelen
authors.forename: Mathias
authors.initials: M
authors.lastname: Prokop
authors.forename: Matthijs
authors.initials: M
authors.lastname: Oudkerk
mesh_terms: D000368:Aged; D016009:Chi-Square Distribution; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted; D016032:Randomized Controlled Trials as Topic; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging* / Q000473:pathology; D014057:Tomography, X-Ray Computed / Q000379:methods*