Quantitative interstitial lung disease scores in idiopathic inflammatory myopathies: longitudinal changes and clinical implications

Abstract

Objectives To investigate computer-aided quantitative scores from high-resolution CT (HRCT) images and determine their longitudinal changes and clinical significance in patients with idiopathic inflammatory myopathies (IIMs)-related interstitial lung disease (IIMs-ILD). Methods The clinical data and HRCT images of 80 patients with IIMs who underwent serial HRCT scans at least twice were retrospectively analysed. Quantitative ILD (QILD) scores (%) were calculated as the sum of the extent of lung fibrosis, ground-glass opacity, and honeycombing. The individual time-estimated ΔQILD between two consecutive scans was derived using a linear approximation of yearly changes. Results The baseline median QILD (interquartile range) scores in the whole lung were 28.1% (19.1–43.8). The QILD was significantly correlated with forced vital capacity (r=-0.349, P = 0.002) and diffusing capacity for carbon monoxide (r=-0.381, P = 0.001). For ΔQILD between the first two scans, according to the visual ILD subtype, QILD aggravation was more frequent in patients with usual interstitial pneumonia (UIP) than non-UIP (80.0% vs. 44.4%, P = 0.013). Multivariable logistic regression analyses identified UIP was significantly related to radiographic ILD progression (ΔQILD >2%, P = 0.015). Patients with higher baseline QILD scores (>28.1%) had a higher risk of lung transplantation or death (P = 0.015). In the analysis of three serial HRCT scans (n = 41), dynamic ΔQILD with four distinct patterns (improving, worsening, convex, and concave) was observed. Conclusion QILD changes in IIMs-ILD were dynamic, and baseline UIP patterns seemed to be related to a longitudinal progression in QILD. These may be a potential imaging biomarkers for lung function, changes in ILD severity, and prognosis in IIMs-ILD.

Towards the adoption of quantitative computed tomography in the management of interstitial lung disease

“…Computerised applications can also detect changes in lung volume [31,32]. QCT parameters are predictive of measures of ILD progression, including FVC decline and mortality [18,31,[33][34][35][36][37][38]. Importantly, QCT tools are able to identify prognostic biomarkers that are not detectable to human eyes, such as the vessel-related structure measured by CALIPER, which is a strong predictor of mortality in patients with IPF [34].…”