Evaluation of e-Lung automated quantitative computed tomography biomarkers in idiopathic pulmonary fibrosis

• Published in: ERJ OPEN RESEARCH
• Main Authors: George, Peter M.; Rennison-Jones, Christian; Benvenuti, Giacomo; Sifostratoudaki, Aliki; Ottink, Finja A.; Bou-Zeid, Wassim; Ledda, Roberta E.; Kadir, Roqia F. Abul; Johari, Bushra; Fernandez, Claire; Harston, George W. J.; Joly, Olivier; Gerry, Stephen; Devaraj, Anand
• Format: Article
• Language: English
• Published: EUROPEAN RESPIRATORY SOC JOURNALS LTD 2024
• Subjects: Respiratory System
• Online Access: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001390389900024

Background In patients with idiopathic pulmonary fibrosis (IPF) there is a need to identify biomarkers that 1) are associated with increased risk of adverse outcome and 2) can be used to monitor treatment response or identify disease progression over time. Methods Two consecutive cohorts of patients with IPF were accessed from the Open Source Imaging Consortium database. Automated computed tomography (CT) biomarkers of disease severity incorporating fibrotic and pulmonary vascular features (the reticulovascular score and weighted reticulovascular score (WRVS)) were studied. Relationships between imaging biomarkers, lung function and survival were analysed. Results In separate test and validation cohorts, 168 and 176 patients with IPF respectively (median survival 2.6 years) were studied. A threshold of WRVS >= 15% at baseline CT was most strongly associated with transplant-free survival (HR 3.00, 95% CI 1.47-6.10, p=0.002) when adjusted for baseline forced vital capacity (FVC) and age. In patients with 12-month follow-up CT and lung function tests (n=89) an increase in 3% of WRVS (the minimal clinically important difference) was also significantly associated with reduced survival independent of FVC, and outperformed visual evaluation of progressive fibrosis. Conclusions WRVS is an automated CT biomarker which can identify patients with IPF at increased risk of progression and is able to reliably capture disease progression over time.