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Fully automated CT imaging biomarkers of bone, muscle, and fat: correcting for the effect of intravenous contrast

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Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

Fully automated CT-based algorithms for quantifying bone, muscle, and fat have been validated for unenhanced abdominal scans. The purpose of this study was to determine and correct for the effect of intravenous (IV) contrast on these automated body composition measures.

Materials and methods

Initial study cohort consisted of 1211 healthy adults (mean age, 45.2 years; 733 women) undergoing abdominal CT for potential renal donation. Multiphasic CT protocol consisted of pre-contrast, arterial, and parenchymal phases. Fully automated CT-based algorithms for quantifying bone mineral density (BMD, L1 trabecular HU), muscle area and density (L3-level MA and M-HU), and fat (visceral/subcutaneous (V/S) fat ratio) were applied to pre-contrast and parenchymal phases. Effect of IV contrast upon these body composition measures was analyzed. Square of the Pearson correlation coefficient (r2) was generated for each comparison.

Results

Mean changes (± SD) in L1 BMD, L3-level MA and M-HU, and V/S fat ratio were 26.7 ± 27.2 HU, 2.9 ± 10.2 cm2, 18.8 ± 6.0 HU, − 0.1 ± 0.2, respectively. Good linear correlation between pre- and post-contrast values was observed for all automated measures: BMD (pre = 0.87 × post; r2 = 0.72), MA (pre = 0.98 × post; r2 = 0.92), M-HU (pre = 0.75 × post  + 5.7; r2 = 0.75), and V/S (pre = 1.11 × post; r2 = 0.94); p < 0.001 for all r2 values. There were no significant trends according to patient age or gender that required further correction.

Conclusion

Fully automated quantitative tissue measures of bone, muscle, and fat at contrast-enhanced abdominal CT can be correlated with non-contrast equivalents using simple, linear relationships. These findings will facilitate evaluation of mixed CT cohorts involving larger patient populations and could greatly expand the potential for opportunistic screening.

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Acknowledgements

This research was supported in part by the Intramural Research Program of the National Institutes of Health Clinical Center, and utilized the high-performance computing capabilities of the NIH Biowulf cluster. We thank NVIDIA for GPU card donation.

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Authors and Affiliations

  1. The University of Wisconsin School of Medicine & Public Health, Madison, WI, USA

    Alberto A. Perez & Perry J. Pickhardt

  2. Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA

    Daniel C. Elton, Veit Sandfort & Ronald M. Summers

  3. Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave., Madison, WI, 53792-3252, USA

    Perry J. Pickhardt

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  1. Alberto A. Perez
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  2. Perry J. Pickhardt
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Correspondence to Perry J. Pickhardt.

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Conflict of interest

Dr. Pickhardt serves is an advisor to Bracco and Zebra and is a shareholder in SHINE, Elucent, and Cellectar; Dr. Summers receives royalties from iCAD, PingAn, Philips, ScanMed and Translation Holdings, and research support from PingAn (CRADA) and NVIDIA (GPU card donations).

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Perez, A.A., Pickhardt, P.J., Elton, D.C. et al. Fully automated CT imaging biomarkers of bone, muscle, and fat: correcting for the effect of intravenous contrast. Abdom Radiol 46, 1229–1235 (2021). https://doi.org/10.1007/s00261-020-02755-5

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  • DOI: https://doi.org/10.1007/s00261-020-02755-5

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