title: Using the CT features to differentiate invasive pulmonary adenocarcinoma from pre-invasive lesion appearing as pure or mixed ground-glass nodules.
abstract: OBJECTIVE
  To differentiate pre-invasive lesion from invasive pulmonary adenocarcinoma (IPA) appearing as ground-glass nodules (GGNs) using CT features.
  
  
  METHODS
  149 GGNs were enrolled in this study, with 74 pure GGNs (p-GGNs) and 75 mixed GGNs (m-GGNs). Firstly, univariate analysis was used to analyse the difference of CT features between pre-invasive lesion and IPA. Then, multivariate analysis was conducted to identify variables that could independently differentiate pre-invasive lesion from IPA. Receiver operating characteristic curve analysis was performed to evaluate the differentiating value of identified variables.
  
  
  RESULTS
  In the p-GGNs, multivariate analysis showed that the amount of blood vessels was an independent risk factor. Using the amount of blood vessels "≥1" as the diagnostic criterion, we could diagnose IPA with a sensitivity of 100%. Using the amount of blood vessels "=0" as the diagnostic criterion, we could diagnose pre-invasive lesions with a specificity of 100%. In the m-GGNs, multivariate analysis showed that the volume of solid portion (VSolid) and pleural indentation were two independent risk factors. One further model was constructed using these two variables: model = 2.508 × (VSolid + 1.407) × (pleural indentation - 1.016). Using the new model, improved diagnostic ability was achieved compared with using VSolid or pleural indentation alone.
  
  
  CONCLUSIONS
  The amount of blood vessels through the p-GGNs would be an important criterion during clinical management, while VSolid and pleural indentation seemed important for m-GGNs. Moreover, the new model could further improve the differentiating value for m-GGNs.
  
  
  CONCLUSIONS
  CT features are useful in differentiating pre-invasive lesion from IPA appearing as GGNs.
authors.affiliation: Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
authors.forename: J
authors.initials: J
authors.lastname: Liang
authors.affiliation: Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
authors.forename: X-Q
authors.initials: XQ
authors.lastname: Xu
authors.affiliation: Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
authors.forename: H
authors.initials: H
authors.lastname: Xu
authors.affiliation: Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
authors.forename: M
authors.initials: M
authors.lastname: Yuan
authors.affiliation: Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
authors.forename: W
authors.initials: W
authors.lastname: Zhang
authors.affiliation: Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
authors.forename: Z-F
authors.initials: ZF
authors.lastname: Shi
authors.affiliation: Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
authors.forename: T-F
authors.initials: TF
authors.lastname: Yu
mesh_terms: D000230:Adenocarcinoma / Q000000981:diagnostic imaging*; D000328:Adult; D000368:Aged; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D012189:Retrospective Studies; D012307:Risk Factors; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*