Automated Quantification of High-Resolution CT Scan Findings in Individuals at Risk for Pulmonary Fibrosis

Abstract

Automated methods to quantify interstitial lung disease (ILD) on high-resolution CT (HRCT) scans in people at risk for pulmonary fibrosis have not been developed and validated. Cohorts with familial pulmonary fibrosis (n = 126) or rheumatoid arthritis with and without ILD (n = 86) were used to develop and validate a computer program capable of quantifying ILD on HRCT scans, which imaged the lungs semicontinuously from the apices to the lung bases during end-inspiration in the prone position. This method uses segmentation, texture analysis, training, classification, and grading to score ILD. Quantification of HRCT scan findings of ILD using an automated computer program correlated with radiologist readings and detected disease of varying severity in a derivation cohort with familial pulmonary fibrosis or their first-degree relatives. This algorithm was validated in an independent cohort of subjects with rheumatoid arthritis with and without ILD. Automated classification of HRCT scans as normal or ILD was significant in the derivation and validation cohorts (P < .001 and P < .001, respectively). Areas under receiver operating characteristic curves performed independently for each group were 0.888 for the derivation cohort and 0.885 for the validation cohort. Pulmonary function test results, including FVC and diffusion capacity, correlated with computer-generated HRCT scan scores for ILD (r = -0.483 and r = -0.532, respectively). Automated computer scoring of HRCT scans can objectively identify ILD and potentially quantify radiographic severity of lung disease in populations at risk for pulmonary fibrosis.