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Updated: Apr 5, 2026

Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease
Published on: June 16, 2020
Perfusion- and pattern-based quantitative CT indexes using contrast-enhanced dual-energy computed tomography in
Jung Won Moon1, Jang Pyo Bae2, Ho Yun Lee3
1Department of Radiology (J.W.M.), Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea.
Objectives:
To evaluate automated texture-based segmentation of dual-energy CT (DECT) images in diffuse interstitial lung disease (DILD) patients and prognostic stratification by overlapping morphologic and perfusion information of total lung.
Methods:
Suspected DILD patients scheduled for surgical biopsy were prospectively included. Texture patterns included ground-glass opacity (GGO), reticulation and consolidation. Pattern- and perfusion-based CT measurements were assessed to extract quantitative parameters. Accuracy of texture-based segmentation was analysed. Correlations between CT measurements and pulmonary function test or 6-minute walk test (6MWT) were calculated. Parameters of idiopathic pulmonary fibrosis/usual interstitial pneumonia (IPF/UIP) and non-IPF/UIP were compared. Survival analysis was performed.
Results:
Overall accuracy was 90.47% for whole lung segmentation. Correlations between mean iodine values of total lung, 50-97.5th (%) attenuation and forced vital capacity or 6MWT were significant. Volume of GGO, reticulation and consolidation had significant correlation with DLco or SpO2 on 6MWT. Significant differences were noted between IPF/UIP and non-IPF/UIP in 6MWT distance, mean iodine value of total lung, 25-75th (%) attenuation and entropy. IPF/UIP diagnosis, GGO ratio, DILD extent, 25-75th (%) attenuation and SpO2 on 6MWT showed significant correlations with survival.
Conclusion:
DECT combined with pattern analysis is useful for analysing DILD and predicting survival by provision of morphology and enhancement.
Key Points:
• Dual-energy CT (DECT) produces morphologic and parenchymal enhancement information. • Automated lung segmentation enables analysis of disease extent and severity. • This prospective study showed value of DECT in DILD patients. • Parameters on DECT enable characterization and survival prediction of DILD.
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