title: High-resolution computed tomography to differentiate chronic diffuse interstitial lung diseases with predominant ground-glass pattern using logical analysis of data.
abstract: OBJECTIVE
  We evaluated the performance of high-resolution computed tomography (HRCT) to differentiate chronic diffuse interstitial lung diseases (CDILD) with predominant ground-glass pattern by using logical analysis of data (LAD).
  
  
  METHODS
  A total of 162 patients were classified into seven categories: sarcoidosis (n = 38), connective tissue disease (n = 32), hypersensitivity pneumonitis (n = 18), drug-induced lung disease (n = 15), alveolar proteinosis (n = 12), idiopathic non-specific interstitial pneumonia (n = 10) and miscellaneous (n = 37). First, 40 CT attributes were investigated by the LAD to build up patterns characterising a category. From the association of patterns, LAD determined models specific to each CDILD. Second, data were recomputed by adding eight clinical attributes to the analysis. The 20 x 5 cross-folding method was used for validation.
  
  
  RESULTS
  Models could be individualised for sarcoidosis, hypersensitivity pneumonitis, connective tissue disease and alveolar proteinosis. An additional model was individualised for drug-induced lung disease by adding clinical data. No model was demonstrated for idiopathic non-specific interstitial pneumonia and the miscellaneous category. The results showed that HRCT had a good sensitivity (>or=64%) and specificity (>or=78%) and a high negative predictive value (>or=93%) for diseases with a model. Higher sensitivity (>or=78%) and specificity (>or=89%) were achieved by adding clinical data.
  
  
  CONCLUSIONS
  The diagnostic performance of HRCT is high and can be increased by adding clinical data.
authors.affiliation: UPRES EA 2363, Department of Radiology, Université Paris 13, Assistance Publique-Hôpitaux de Paris, Hôpital Avicenne, 125 route de Stalingrad, 93000 Bobigny, France.
authors.forename: Sophie Grivaud
authors.initials: SG
authors.lastname: Martin
authors.forename: Louis-Philippe
authors.initials: LP
authors.lastname: Kronek
authors.forename: Dominique
authors.initials: D
authors.lastname: Valeyre
authors.forename: Nadia
authors.initials: N
authors.lastname: Brauner
authors.forename: Pierre-Yves
authors.initials: PY
authors.lastname: Brillet
authors.forename: Hilario
authors.initials: H
authors.lastname: Nunes
authors.forename: Michel W
authors.initials: MW
authors.lastname: Brauner
authors.forename: Frédérique
authors.initials: F
authors.lastname: Réty
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D016015:Logistic Models; D017563:Lung Diseases, Interstitial / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*