Deep learning-based quantification of CT changes after 1 year of pirfenidone treatment as a prognostic marker in idiopathic pulmonary fibrosis.
Authors
Affiliations (11)
Affiliations (11)
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.
- Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University Biomedical Research Institute of Jeonbuk National University Hospital, Jeonbuk National University and Medical School, Jeonju, Korea.
- Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Korea.
- Department of Radiology, The University of Chicago, Chicago, IL, USA.
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea.
- Cancer Research Institute, Seoul National University, Seoul, Korea.
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea.
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea. [email protected].
- Department of Radiology, Seoul National University Hospital, Seoul, Korea. [email protected].
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea. [email protected].
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). Changes in deep learning-based CT quantification results over 1 year of pirfenidone treatment demonstrated significant prognostic value in patients with IPF. Question Disease progression and mortality in patients with idiopathic pulmonary fibrosis (IPF) continue to occur unpredictably beyond 1 year of pirfenidone treatment. Findings Changes in deep learning-based quantification of normal lung and fibrosis extent after 1 year of pirfenidone treatment were significant prognostic predictors. Critical relevance statement Incorporating 1-year changes in deep learning-based CT quantification improves prognostication of mortality, lung transplantation, and lung function decline, supporting more individualized risk stratification in idiopathic pulmonary fibrosis.