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.
Conclusion: 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.
Keywords: Computed tomography; Interstitial lung disease; Medical informatics.