Abstract
Rationale: Idiopathic pulmonary fibrosis (IPF) is a complex and heterogeneous disease. Given this, we reasoned that differences in genetic profiles may be associated with unique clinical and radiologic features. Computational image analysis, sometimes referred to as radiomics, provides objective, quantitative assessments of radiologic features in subjects with pulmonary fibrosis. Objectives: To determine if the genetic risk profile of patients with IPF identifies unique computational imaging phenotypes. Methods: Participants with IPF were included in this study if they had genotype data and computed tomography (CT) scans of the chest available for computational image analysis. The extent of lung fibrosis and the likelihood of a usual interstitial pneumonia (UIP) pattern were scored automatically using two separate, previously validated deep learning techniques for CT analysis. UIP pattern was also classified visually by radiologists according to established criteria. Results: Among 329 participants with IPF, MUC5B and ZKSCAN1 were independently associated with the deep learning-based UIP score. None of the common variants were associated with fibrosis extent by computational imaging. We did not find an association between MUC5B or ZKSCAN1 and visually assessed UIP pattern. Conclusions: Select genetic variants are associated with computer-based classification of UIP on CT in this IPF cohort. Analysis of radiologic features using deep learning may enhance our ability to identify important genotype-phenotype associations in fibrotic lung diseases.