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European Radiology

, Volume 26, Issue 5, pp 1368–1377 | Cite as

Perfusion- and pattern-based quantitative CT indexes using contrast-enhanced dual-energy computed tomography in diffuse interstitial lung disease: relationships with physiologic impairment and prediction of prognosis

  • Jung Won Moon
  • Jang Pyo Bae
  • Ho Yun LeeEmail author
  • Namkug KimEmail author
  • Man Pyo Chung
  • Hye Yun Park
  • Yongjun Chang
  • Joon Beom Seo
  • Kyung Soo Lee
Computed Tomography

Abstract

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.

Keywords

DECT DILD Perfusion- or pattern-based CT quantification parameters IPF/UIP Survival 

Abbreviations

DECT

dual-energy computed tomography

DILD

diffuse interstitial lung disease

GGO

ground-glass opacity

HRCT

high-resolution computed tomography

IPF/UIPs

idiopathic pulmonary fibrosis/usual interstitial pneumonias

PFT

pulmonary function test

6MWT

6-minute walk test

VNC

virtual non-contrast

Notes

Acknowledgments

The scientific guarantor of this publication is Ho Yun Lee. The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article. This research was supported by the Research Fund of the GE Healthcare (OMS-09-07). One of the authors has significant statistical expertise. Institutional review board approval was obtained. Written informed consent was obtained from all subjects (patients) in this study. Some study subjects or cohorts have not been previously reported. Methodology: prospective, diagnostic or prognostic study, performed at one institution.

Supplementary material

330_2015_3946_MOESM1_ESM.docx (18 kb)
ESM 1 (DOCX 18 kb)

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Copyright information

© European Society of Radiology 2015

Authors and Affiliations

  • Jung Won Moon
    • 1
  • Jang Pyo Bae
    • 2
  • Ho Yun Lee
    • 3
    Email author
  • Namkug Kim
    • 2
    Email author
  • Man Pyo Chung
    • 4
  • Hye Yun Park
    • 4
  • Yongjun Chang
    • 2
  • Joon Beom Seo
    • 2
  • Kyung Soo Lee
    • 3
  1. 1.Department of Radiology (J.W.M.), Kangbuk Samsung HospitalSungkyunkwan University School of MedicineSeoulKorea
  2. 2.Department of Radiology (J.P.B., N.K., Y.C., J.B.S.), Asan Medical CenterUniversity of Ulsan College of MedicineSeoulKorea
  3. 3.Department of Radiology and Center for Imaging Science (H.Y.L., K.S.L.), Samsung Medical CenterSungkyunkwan University School of MedicineSeoulKorea
  4. 4.Department of Pulmonology (M.P.C, H.Y.P), Samsung Medical CenterSungkyunkwan University School of MedicineSeoulKorea

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