INTRODUCTION
Systemic sclerosis (SSc) is a life-threatening, immune-mediated
rheumatic disease characterized by fibrosis of the skin and internal
organs and vasculopathy. SSc may affect almost every organ, but usually
the skin, lung, heart, gastrointestinal tract, and peripheral
circulation.
Lung involvement is one of the leading causes of morbidity and mortality
[1]. Postmortem examination reveals signs of pulmonary fibrosis (PF)
in approximately 80% of SSc patients [2]. Major risk factors for
the development of PF include diffuse cutaneous SSc (dcSSc), African
American race, older age at disease onset, shorter disease duration,
presence of anti-Scl-70 antibodies, and absence of anticentromere
antibodies [3]. It has been reported that there is a close
association between thoracic computed tomography (CT) patterns and
histopathological findings in surgical lung biopsy specimens which has
resulted in the widespread use of high-resolution computed tomography
(HRCT) to identify the lung pathology due to fibrosis [4-5].
Thoracic HRCT scanning plays a central role in detecting lung fibrosis
in SSc. Compared to HRCT, pulmonary symptoms, chest radiography,
pulmonary function tests, and bronchoalveolar lavage (BAL) have limited
diagnostic accuracy and availability, especially when lung disease is
less advanced. HRCT may identify abnormalities in up to 44% of the
patients with apparently normal chest radiographs [6].
Despite major advances in diagnosis, SSc-related PF is challenging for
clinical management because treatment options are still limited.
However, it has been suggested that there is a ”window of opportunity”
when patients are identified at a very early phase of the disease,
enabling physicians to prevent or at least slow disease progression with
effective medications [7]. This view has highlighted the importance
of identification of organ involvement before irreversible fibrosis and
organ damage occur. Recent technical advances in radiology and
informatics have enabled the extraction of quantitative information,
such as volumetric data, morphometric data, or textural patterns,
concerning an organ of interest or a lesion within the organ [8].
Many algorithms and software platforms provide image segmentation
routines for quantification of lung abnormalities. Lung segmentation is
the method used to identify the boundaries of the lung from the
surrounding tissue and provide quantitative data to assess the lung
parenchyma [9].
The aim of this study was 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.