title: High-resolution computed tomography to differentiate chronic diffuse infiltrative lung diseases with chronic multifocal consolidation patterns using logical analysis of data.
abstract: BACKGROUND
  Chronic lung consolidation has a limited number of differential diagnoses requiring distinct managements. The aim of the study was to investigate how logical analysis of data (LAD) can support their diagnosis at HRCT (high-resolution computed tomography).
  
  
  METHODS
  One hundred twenty-four patients were retrospectively included and classified into 8 diagnosis categories: sarcoidosis (n=35), connective tissue disease (n=21), adenocarcinoma (n=17), lymphoma (n=13), cryptogenic organizing pneumonia (n=11), drug-induced lung disease (n=9), chronic eosinophilic pneumonia (n =7) and miscellaneous (n=11). First, we investigated the patterns and models (association of patterns characterizing a disease) built-up by the LAD from combinations of HRCT attributes (n=51). Second, data were recomputed by adding simple clinical attributes (n=14) to the analysis. Third, cluster analysis was performed to explain LAD failures.
  
  
  RESULTS
  HRCT models reached a sensitivity >80% and a specificity >90% for adenocarcinoma and chronic eosinophilic pneumonia. The same thresholds were obtained for sarcoidosis, connective tissue disease, and drug-induced lung diseases when clinical attributes were added to HRCT. LAD failed to provide a satisfactory model for lymphoma and cryptogenic organizing pneumonia, with overlap between both diseases shown on cluster analysis.
  
  
  CONCLUSIONS
  LAD provides relevant models that can be used as a diagnosis support for the radiologist. It highlights the need to add clinical data in the analysis due to frequent overlap between diseases at HRCT.
authors.affiliation: Department of Radiology, Hôpital Saint-Louis, Assistance Publique-Hôpitaux de Paris, Université Paris 7 Denis Diderot, Sorbonne Paris-Cité, Paris, France. constancedemm@gmail.com.
authors.forename: Constance
authors.initials: C
authors.lastname: De Margerie-Mellon
authors.forename: Geneviève
authors.initials: G
authors.lastname: Dion
authors.forename: Julien
authors.initials: J
authors.lastname: Darlay
authors.forename: Imene
authors.initials: I
authors.lastname: Ridene
authors.forename: Marianne
authors.initials: M
authors.lastname: Kambouchner
authors.forename: Nadia
authors.initials: N
authors.lastname: Brauner
authors.forename: Michel
authors.initials: M
authors.lastname: Brauner
authors.forename: Dominique
authors.initials: D
authors.lastname: Valeyre
authors.forename: Pierre-Yves
authors.initials: PY
authors.lastname: Brillet
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D002908:Chronic Disease; D057225:Data Mining / Q000379:methods*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging*; D008171:Lung Diseases / Q000000981:diagnostic imaging* / Q000209:etiology; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated; D011237:Predictive Value of Tests; D011857:Radiographic Image Interpretation, Computer-Assisted; D012189:Retrospective Studies; D012307:Risk Factors; D014057:Tomography, X-Ray Computed
keywords: Computed tomography; Interstitial lung disease; Medical informatics