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  • Title: High-resolution computed tomography to differentiate chronic diffuse interstitial lung diseases with predominant ground-glass pattern using logical analysis of data.
    Author: Martin SG, Kronek LP, Valeyre D, Brauner N, Brillet PY, Nunes H, Brauner MW, Réty F.
    Journal: Eur Radiol; 2010 Jun; 20(6):1297-310. PubMed ID: 19997848.
    Abstract:
    OBJECTIVES: 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. CONCLUSION: The diagnostic performance of HRCT is high and can be increased by adding clinical data.
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