Interstitial lung diseases (ILDs) encompass over 200 distinct pulmonary disorders characterized by inflammation or fibrosis of the lung interstitium, the area surrounding the alveoli. These conditions compromise gas exchange, causing symptoms such as progressively worsening shortness of breath on exertion, persistent dry cough, and eventual respiratory failure in advanced stages [1].
Idiopathic pulmonary fibrosis (IPF) represents the most prevalent and extensively studied fibrotic ILD. IPF is a chronic, progressive fibrosing lung disease of unknown origin, typically resulting in declining respiratory function. The disease course varies among patients, with a median survival of approximately 2 to 5 years post-diagnosis. Early diagnosis and antifibrotic therapies can, however, slow disease progression and improve patient outcomes [2,3,7].
Accurate diagnosis of ILDs often requires multidisciplinary discussions (MDD) involving pulmonologists, radiologists, pathologists, rheumatologists, and other specialists. The diagnostic process integrates clinical assessment, high-resolution computed tomography (HRCT), pulmonary function tests, and occasionally bronchoalveolar lavage or lung biopsies [4].
Although fibrotic ILDs currently have no cure, antifibrotic therapies such as pirfenidone and nintedanib have significantly improved disease management. These treatments, initially approved for IPF, slow lung function decline and are being increasingly investigated or authorized for other progressive fibrotic ILDs [5,6].
Real-world evidence indicates variability in individual treatment responses to antifibrotic therapies. Current research is focused on discovering biomarkers, improving patient stratification, and personalizing treatment approaches to maximize patient benefit [7].
Additionally, innovative technologies such as artificial intelligence (AI)-assisted imaging tools are being developed to facilitate earlier disease detection, enhance diagnostic accuracy, and improve prognosis assessment, potentially transforming patient care strategies in ILD management [8].
References
- Cottin V, Hirani NA, Hotchkin DL, et al. Presentation, diagnosis and clinical course of the spectrum of progressive-fibrosing interstitial lung diseases. Eur Respir Rev. 2018;27(150):180076. doi: 10.1183/16000617.0076-2018
- Raghu G, Collard HR, Egan JJ, et al. An official ATS/ERS/JRS/ALAT statement: idiopathic pulmonary fibrosis: evidence-based guidelines for diagnosis and management. Am J Respir Crit Care Med. 2011;183(6):788–824. doi: 10.1164/rccm.2009-040GL
- Ley B, Collard HR, King TE Jr. Clinical course and prediction of survival in idiopathic pulmonary fibrosis. Am J Respir Crit Care Med. 2011;183(4):431–40. doi: 10.1164/rccm.201006-0894OC
- Walsh SL, Wells AU, Desai SR, et al. Multicentre evaluation of multidisciplinary team meeting agreement on diagnosis in diffuse parenchymal lung disease: a case-cohort study. Lancet Respir Med. 2016;4(7):557–65. doi: 10.1016/S2213-2600(16)30033-9
- Richeldi L, du Bois RM, Raghu G, et al. Efficacy and safety of nintedanib in idiopathic pulmonary fibrosis. N Engl J Med. 2014;370(22):2071–82. doi: 10.1056/NEJMoa1402584
- King TE Jr, Bradford WZ, Castro-Bernardini S, et al. A phase 3 trial of pirfenidone in patients with idiopathic pulmonary fibrosis. N Engl J Med. 2014;370(22):2083–92. doi: 10.1056/NEJMoa1402582
- Kreuter M, Wijsenbeek M, Vasakova M, et al. Real-world effectiveness and safety of nintedanib in patients with idiopathic pulmonary fibrosis: Results from the European multicentre EMPIRE registry. Respir Res. 2021;22:305. doi: 10.1186/s12931-021-01884-0
- Walsh SLF, Calandriello L, Silva M, Sverzellati N. Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: a case–cohort study. Lancet Respir Med. 2018;6(11):837–845. doi: 10.1016/S2213-2600(18)30256-1