Chronic diffuse infiltrative lung disease: determination of the diagnostic value of clinical data, chest radiography, and CT and Bayesian analysis.

Abstract

PURPOSE: To assess the value of clinical, chest radiographic, and computed tomographic (CT) findings in classifying chronic diffuse infiltrative lung disease (CDILD) MATERIALS AND METHODS: Two samples from the same population were consecutively studied: the training set (group A, n = 208) for the development of the decision aid and the test set (group B, n = 100) for validation. Computer-aided diagnoses were made with a Bayesian model that assigned to each patient diagnostic probabilities based on clinical, radiographic, or CT variables. RESULTS: In group A, a correct diagnosis based on clinical data was obtained in 29% of cases; radiography, 9%; and CT, 36%. This increased to 54% when clinical and radiographic variables were combined (P < .0001) and to 80% when data from all three were analyzed together (P < .0001). With prior and conditional probabilities determined from group A, the frequency of correct diagnosis in group B was 27% with clinical data, which increased to 53% (P < .0001) with radiographic findings and 61% after including CT data (P = .07). CONCLUSION: CT can help determine the specific diagnosis in patients with CDILD.