Deep learning-based quantification of CT changes after 1 year of pirfenidone treatment as a prognostic marker in idiopathic pulmonary fibrosis.

Published in Insights into imaging. Abstract: To evaluate the prognostic value of changes in deep learning-based CT quantification following 1 year of pirfenidone treatment in patients with idiopathic pulmonary fibrosis (IPF). This study included patients with IPF who received at least 1 year of pirfenidone between September 2009 and March 2022 at two hospitals. The extent of normal lung and fibrosis (sum of reticular opacity and honeycombing) was quantified on baseline and 1-year follow-up chest CT scans using a deep learning-based tool, and the changes were calculated. The primary outcome was a composite of all-cause mortality or lung transplantation; the secondary outcome was a ≥ 10% absolute decline in forced vital capacity (FVC) occurring after the 1-year follow-up. Associations between changes in CT quantification and these outcomes were assessed using Cox proportional hazards regression analyses. A total of 436 patients (341 men; mean age, 69.8 years) were included. Greater preservation of normal lung extent over 1 year was associated with a lower risk of the composite outcome (adjusted hazard ratio (HR) per 5% increase: 0.90; 95% confidence interval (CI): 0.85, 0.96; p < 0.001), whereas increases in fibrosis extent were associated with a higher risk (adjusted HR per 5% increase: 1.16, 95% CI: 1.06, 1.27, p = 0.002). Regarding the secondary outcome of ≥ 10% absolute FVC decline, only the change in fibrosis extent remained a significant predictor (adjusted HR per 5% increase: 1.26, 95% CI: 1.09, 1.45, p = 0.001). C...