LAUSR

The rising prevalence of pulmonary fibrosis and its attendant morbidity and mortality burden have spurred a renewed interest in the search for radiographical biomarkers predictive of early and clinically inconspicuous disease.1 The pressing need to identify such biomarkers becomes increasingly important as these abnormalities may herald the onset of pulmonary fibrosis, which could be rapidly progressive, necessitating clinical intervention. Antifibrotic therapies have been recently demonstrated to slow the progression of pulmonary fibrosis across diverse forms of interstitial lung disease (ILD), providing some hope in those individuals shown to be at risk.2 In this regard, high-resolution CT (HRCT) scans of the chest hold a broad appeal as a non-invasive radiological tool that is clinically accessible and rapidly performed with near-instantaneous results. Thus, a great deal of emphasis is currently placed on the recognition of radiological abnormalities that possibly signify pulmonary fibrosis in its early stages.3 These subclinical bilateral interstitial densities, often termed interstitial lung abnormalities (ILAs) or early ILD, are frequently observed on chest HRCTs and are associated with increased risk of hospitalisation and death.3 4 However, substantial inter-reader variability in radiologist interpretations of these fibrotic indices has led to increased reliance on deep learning algorithms and artificial intelligence to identify and accurately quantify the extent of lung parenchymal fibrosis in a more objective manner.5 In tandem with the rapid pace of radiological biomarker advancements is the increasing recognition of the predictive and prognostic value of genomic biomarkers associated with pulmonary fibrosis.6 Of prime importance among the genomic biomarkers that predict risk of pulmonary fibrosis are the polymorphisms in the promoter region of the gene encoding mucin 5B ( MUC5B ) ( rs35705950 ) and gene variants in the …