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Updated: Jul 17, 2026

Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease
Published on: June 16, 2020
Quantitative Assessment of Systemic Sclerosis-Related Interstitial Lung Disease via 3D-Imaging
Hiromasa Nakayasu1, Yuzo Suzuki1, Masato Kono2
1Second Division, Department of Internal Medicine, Hamamatsu University School of Medicine, Hamamatsu, Japan.
Background And Objective:
Interstitial lung disease (ILD) is a leading cause of morbidity and mortality in patients with systemic sclerosis (SSc). The disease course of SSc-related ILD (SSc-ILD) is heterogeneous, and several risk-based models have been developed. This study aimed to quantitatively measure volume loss and disease extent and subsequently evaluate their associations with the development of end-stage lung disease (ESLD).
Methods:
Lung volume (LV) and disease extent were retrospectively and quantitatively evaluated in two cohorts (exploratory: n = 70; validation: n = 42) using high-resolution computed tomography at the time of SSc-ILD diagnosis, compared to controls (n = 70). LV was quantitatively measured using three-dimensional imaging (3D-image) and standardised by predicted forced vital capacity (standardised LV). The ratio of the normally attenuated LV (range, -950 to -750 Hounsfield units) to the whole-LV (NL%) was also measured using 3D-image. The associations of these variables with ESLD were evaluated.
Results:
Volume loss and normal lung area loss were noted in patients with SSc-ILD compared with controls, especially in the lower lobes. Meanwhile, extended ILD lesions without volume reduction were observed in the upper lobes. Both decreased standardised LV and NL% were associated with ESLD development, and age and NL% were significant risk factors for ESLD independent of pulmonary function test parameters and standardised LV. A composite model consisting of age and NL% successfully stratified patients with SSc-ILD based on the risk of ESLD.
Conclusion:
3D-image may be a useful technique for assessing disease severity and predicting the risk for ESLD in patients with SSc-ILD.
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