title: Persistent Pure Ground-Glass Nodules Larger Than 5 mm: Differentiation of Invasive Pulmonary Adenocarcinomas From Preinvasive Lesions or Minimally Invasive Adenocarcinomas Using Texture Analysis.
abstract: OBJECTIVE
  To evaluate the differentiating potentials of computed tomography texture analysis for invasive pulmonary adenocarcinomas (IPAs) from their preinvasive lesions or minimally invasive adenocarcinomas (MIAs) manifesting as persistent pure ground-glass nodules (PGGNs) larger than 5 mm.
  
  
  METHODS
  This institutional review board-approved retrospective study included 63 patients (23 men and 40 women) with 66 PGGNs larger than 5 mm on unenhanced computed tomography from 2005 to 2013. All PGGNs were pathologically confirmed and categorized into 2 groups [IPAs (n = 11) vs preinvasive lesions (n = 41)/MIAs (n = 14)]. Each PGGN was segmented manually, and their texture features were quantitatively extracted. To identify significant differentiating factors of IPAs from preinvasive lesions/MIAs, multivariate logistic regression and C-statistic analyses were performed.
  
  
  RESULTS
  Between IPAs and preinvasive lesions/MIAs, nodule size, volume, mass, entropy, effective diameter, and surface area were significantly different (P < 0.05), and homogeneity and gray level co-occurrence matrix inverse difference moment showed marginal significance (P < 0.10). Subsequent multivariate analysis revealed larger nodule mass [adjusted odds ratio (OR), 11.92], higher entropy (adjusted OR, 35.12), and lower homogeneity (adjusted OR, 0.278 × 10) as independent differentiating factors of IPAs. Subgroup analysis showed that larger nodule mass, higher entropy, and lower homogeneity were also significant differentiating variables of IPAs in nodules of diameter 10 mm or larger. A multiple logistic regression model using these features showed excellent [area under the curve (AUC), 0.962] and significantly higher differentiating performance compared to nodule size (AUC, 0.712) or mass (AUC, 0.788) alone.
  
  
  CONCLUSIONS
  Computed tomography texture features such as higher entropy and lower homogeneity were significant differentiating factors of IPAs presenting as PGGNs larger than 5 mm and have potentials to enhance the differentiating performance.
authors.affiliation: From the*Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea; †Cancer Research Institute, Seoul National University, Seoul, Korea; ‡Department of Radiology, Duke University Medical Center, Durham, NC; and §Department of Pathology, Seoul National University College of Medicine, Seoul, Korea.
authors.forename: In-Pyeong
authors.initials: IP
authors.lastname: Hwang
authors.forename: Chang Min
authors.initials: CM
authors.lastname: Park
authors.forename: Sang Joon
authors.initials: SJ
authors.lastname: Park
authors.forename: Sang Min
authors.initials: SM
authors.lastname: Lee
authors.forename: Holman Page
authors.initials: HP
authors.lastname: McAdams
authors.forename: Yoon Kyung
authors.initials: YK
authors.lastname: Jeon
authors.forename: Jin Mo
authors.initials: JM
authors.lastname: Goo
mesh_terms: D000230:Adenocarcinoma / Q000000981:diagnostic imaging / Q000473:pathology*; D000328:Adult; D000368:Aged; D000465:Algorithms; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008175:Lung Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology*; D008297:Male; D008875:Middle Aged; D009361:Neoplasm Invasiveness; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging / Q000473:pathology*; D014057:Tomography, X-Ray Computed / Q000379:methods*