High-resolution computed tomography to differentiate chronic diffuse interstitial lung diseases with predominant ground-glass pattern using logical analysis of data.
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.
Martin
Sophie Grivaud
SG
UPRES EA 2363, Department of Radiology, Université Paris 13, Assistance Publique-Hôpitaux de Paris, Hôpital Avicenne, 125 route de Stalingrad, 93000 Bobigny, France.
Kronek
Louis-Philippe
LP
Valeyre
Dominique
D
Brauner
Nadia
N
Brillet
Pierre-Yves
PY
Nunes
Hilario
H
Brauner
Michel W
MW
Réty
Frédérique
F
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*