Quantitative Computed Tomography in Idiopathic Pulmonary Fibrosis: Is It Time to Act?

Quantification of lung abnormalities on high-resolution computed tomography (HRCT) images in patients with idiopathic pulmonary fibrosis (IPF) has been a focus of research for more than 20 years. During this time, we have moved from visual scoring to computerized tools of increasing complexity (also known as quantitative CT [qCT]), with the most recent studies using deep learning technology. The progress made has improved the prognostic value of the HRCT features quantified, while at the same time overcoming the well-documented limits of visual scoring, such as interobserver variability and low reproducibility. These features also correlate with pulmonary function tests both at a single time point and during followup. Another advantage of these computerized tools is their theoretical ability to identify and quantify prognostic imaging biomarkers, which are inaccessible to human eyes