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Published in: Abdominal Radiology 3/2024

29-12-2023 | Computed Tomography | Technical

Comparing fully automated AI body composition measures derived from thin and thick slice CT image data

Authors: Matthew H. Lee, Daniel Liu, John W. Garrett, Alberto Perez, Ryan Zea, Ronald M. Summers, Perry J. Pickhardt

Published in: Abdominal Radiology | Issue 3/2024

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Abstract

Purpose

To compare fully automated artificial intelligence body composition measures derived from thin (1.25 mm) and thick (5 mm) slice abdominal CT data.

Methods

In this retrospective study, fully automated CT-based body composition algorithms for quantifying bone attenuation, muscle attenuation, muscle area, liver attenuation, liver volume, spleen volume, visceral-to-subcutaneous fat ratio (VSR) and aortic calcium were applied to both thin (1.25 × 0.625 mm) and thick (5 × 3 mm) abdominal CT series from two patient cohorts: unenhanced scans in asymptomatic adults undergoing colorectal cancer screening, and post-contrast scans in patients with colorectal cancer. Body composition measures derived from thin and thick slice data were compared, including correlation coefficients and Bland–Altman analysis.

Results

A total of 9882 CT scans (mean age, 57.0 years; 4527 women, 5355 men) were evaluated, including 8947 non-contrast and 935 contrast-enhanced CT exams. Very strong positive correlation was observed for all soft tissue measures: muscle attenuation (r2 = 0.97), muscle area (r2 = 0.98), liver attenuation (r2 = 0.99), liver volume (r2 = 0.98) and spleen volume (r2 = 0.99), VSR (r2 = 0.98), and aortic calcium (r2 = 0.92); (p < 0.001 for all). Moderate positive correlation was observed for bone attenuation (r2 = 0.35). Bland–Altman analysis showed strong agreement for muscle attenuation, muscle area, liver attenuation, liver volume and spleen volume. Mean percentage differences amongst body composition measures were less than 5% for VSR (4.6%), muscle area (− 0.5%), liver attenuation (0.4%) and liver volume (2.7%) and less than 10% for muscle attenuation (− 5.5%) and spleen volume (5.1%). For aortic calcium, thick slice overestimated for Agatston scores between 0 and 100 and > 400 burden in 3.1% and 0.3% relative to thin slice, respectively, but underestimated scores between 100 and 400.

Conclusion

Automated body composition measures derived from thin and thick abdominal CT data are strongly correlated and show agreement, particularly for soft tissue applications, making it feasible to use either series for these CT-based body composition algorithms.

Graphical abstract

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Metadata
Title
Comparing fully automated AI body composition measures derived from thin and thick slice CT image data
Authors
Matthew H. Lee
Daniel Liu
John W. Garrett
Alberto Perez
Ryan Zea
Ronald M. Summers
Perry J. Pickhardt
Publication date
29-12-2023
Publisher
Springer US
Published in
Abdominal Radiology / Issue 3/2024
Print ISSN: 2366-004X
Electronic ISSN: 2366-0058
DOI
https://doi.org/10.1007/s00261-023-04135-1

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