Software‐based quantitative analysis of lung parenchyma in patients with systemic sclerosis may provide new generation data for pulmonary fibrosis. Issue 4 (20th December 2020) - Temiz Karadag, Duygu; Cakir, Ozgur; Komac, Andac; Yazici, Ayten; Cefle, Ayse
Abstract: Objectives: To investigate lung volume and density in patients with SSc and changes in these parameters because of 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 analysed using Myrian XP Lung 3D software. Right, and left lung densities and volumes were calculated separately by a blinded operator. Results were analysed 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 < .001 and P < .001 ; P = .006 and P = .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 = .002 and P < .001 ; P = .045 and P = .044, respectively). Patients who developed PF during follow‐up had significantly lower baseline right and left lung densities than those who did not ( P = .018 ; P = .014, respectively). Forced vital capacity and carbon monoxide diffusing capacity showed a 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.