Software-Based Quantitative Analysis of Lung Parenchyma in Patients with
Systemic Sclerosis May Provide New Generation Data for Pulmonary
Fibrosis
Abstract
Objectives: To investigate lung volume and density in patients with SSc
and changes in these parameters due to PF, using a software-aided image
quantification method, and compare this with a matched healthy control
group. Methods: Thoracic high-resolution computed tomography (HRCT)
images of patients and controls were analyzed using Myrian XP Lung 3D
software. Right, and left lung densities and volumes were calculated
separately by a blinded operator. Results were analyzed between
subgroups to investigate associations with the clinical features.
Results: A total of 135 patients with SSc and 38 healthy controls (HC)
were included. Characteristics of the SSc patients were 94 (69.6%)
without PF, 85.4% female, mean age 49.8 (15.4) years; 41 (30.4%) with
PF, 88.3% female, mean age 50.2 (11.5) years and HC group were 89.5%
Female, mean age 52.2 (5.8) years. The right and left lung densities
were significantly higher, and right and left lung volumes were
significantly lower in the SSc patients with signs of fibrosis than
those without and HC (p<0.001 and p<0.001; p=0.006
and p=0.002, respectively). After excluding patients with PF, right and
left lung densities and volumes differed significantly between diffuse
cutaneous SSc, limited cutaneous SSc, and HC (p=0.002 and
p<0.001; p=0.045 and p=0.044, respectively). Patients who
developed PF during follow-up had significantly lower baseline right and
left lung densities than those who did not (p=0.018; p=0.014,
respectively). Forced vital capacity and carbon monoxide diffusing
capacity showed weak correlation with lung densities and volumes in
patients without PF and moderate to high correlation in PF patients.
Conclusion: Lung density and volume in SSc patients changed
significantly in those with PF and those without. Quantitative
information extracted by soft-ware aided methods may contribute more to
the detection, screening, and risk prediction in SSc related PF.