Fibrotic and vascular abnormalities quantified by an AI-based model are associated with outcomes in patients with idiopathic pulmonary fibrosis.

Background: Idiopathic pulmonary fibrosis (IPF) is a progressive fibrosing interstitial lung disease associated with high morbidity and mortality despite specific anti-fibrotic therapies. Management of IPF is complex and relies on pulmonary function tests (PFT) to evaluate severity and monitor progression. CT provides non-invasive morphologic assessment and emerging software techniques enable quantitative analysis.

Methods: We included 319 individuals with IPF from the OSIC dataset. A cross-sectional analysis was made for all patients, with a longitudinal evaluation for 143 of them. We used LungQ software (Thirona, The Netherlands) to quantify lung and pulmonary vessel volumes, as well as the extent of interstitial lung disease and to assess correlation with PFT and mortality.

Results: Quantitative extent of fibrotic abnormalities was correlated with baseline FVC and DLCO (r -0.47, p < 0.0001 and r -0.55, p < 0.0001 respectively) and longitudinal modifications over time (r -0.48, p < 0.0001 and r-0.43 p < 0.0001, respectively). Median baseline extent of ILD, expressed as a percentage of lung volume, was 16.5% (10.8-25.5) and increased to 17.3% (11.6-29) on follow-up (p < 0.001). The median ILD progression was of 9.8% (-9.5-40.0). Vascular enlargement quantification as well as ILD quantification were predictive marker of death (p < 0.0001). However, vascular abnormalities' independent predictive value could not be assessed in multivariate models due to multicollinearity with other variables.

Conclusions: LungQ allows to quantify interstitial and vascular lung features and their changes over time in a large cohort of patients with IPF. Imaging markers were negatively correlated with PFT at baseline and follow-ups were predictive of mortality confirming their potential as disease quantifiers. Further clinical validation is needed to specify the potential clinical use.