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Open Access 07-04-2022 | Sarcopenia | Computed Tomography

Two-dimensional CT measurements enable assessment of body composition on head and neck CT

Authors: David Zopfs, Daniel Pinto dos Santos, Jonathan Kottlors, Robert P. Reimer, Simon Lennartz, Roman Kloeckner, Max Schlaak, Sebastian Theurich, Christoph Kabbasch, Marc Schlamann, Nils Große Hokamp

Published in: European Radiology | Issue 9/2022

Abstract

Objectives

The aim of this study was to evaluate whether simple 2D measurements in axial slices of head and neck CT examinations correlate with generally established measurements of body composition in abdominal CT at the height of the third lumbar vertebra and thus allow for an estimation of muscle and fat masses.

Methods

One hundred twenty-two patients who underwent concurrent CT of the head and neck and the abdomen between July 2016 and July 2020 were retrospectively included. For a subset of 30 patients, additional bioelectrical impedance analysis (BIA) was available. Areas of paraspinal muscles at the height of the third (C3) and fifth cervical vertebrae (C5) as well as the total cross-sectional area at the height of C3 and at the submandibular level were correlated with the results of abdominal measurements and BIA. Furthermore, intra- and interreader variabilities of all measurements were assessed.

Results

Regarding adipose tissue, good correlations were found between the total cross-sectional area of the patient’s body at the submandibular level and at the height of C3 between both abdominal measurements and BIA results (r = 0.8–0.92; all p < 0.001). Regarding muscle, the total paraspinal muscle area at the height of C3 and C5 showed strong correlations with abdominal measurements and moderate to strong correlations with BIA results (r = 0.44–0.80; all p < 0.001), with the muscle area on C5 yielding slightly higher correlations.

Conclusions

Body composition information can be obtained with comparable reliability from head and neck CT using simple biplanar measurements as from abdominal CT.

Key Points

• The total paraspinal muscle area at the height of C3 and C5 correlates strongly with abdominal muscle mass.

• The total cross-sectional area at the submandibular level and at the height of C3 shows good correlations with abdominal fat mass.

• The described measurements facilitate a rapid, opportunistic assessment of relevant body composition parameters.

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Lee K, Shin Y, Huh J et al (2019) Recent issues on body composition imaging for sarcopenia evaluation. Korean J Radiol 20(2):205–217CrossRef

Kottlors J, Zopfs D, Fervers P et al (2020) Body composition on low dose chest CT is a significant predictor of poor clinical outcome in COVID-19 disease - a multicenter feasibility study. Eur J Radiol 132:109274CrossRef

Pinto Dos Santos D, Kloeckner R, Koch S et al (2020) Sarcopenia as prognostic factor for survival after orthotopic liver transplantation. Eur J Gastroenterol Hepatol 32(5):626–634CrossRef

Xia L, Zhao R, Wan Q et al (2020) Sarcopenia and adverse health-related outcomes: an umbrella review of meta-analyses of observational studies. Cancer Med 9(21):7964–7978CrossRef

Shachar SS, Williams GR, Muss HB, Nishijima TF (2016) Prognostic value of sarcopenia in adults with solid tumours: a meta-analysis and systematic review. Eur J Cancer 57:58–67CrossRef

Zopfs D, Theurich S, Große Hokamp N et al (2020) Single-slice CT measurements allow for accurate assessment of sarcopenia and body composition. Eur Radiol 30(3):1701–1708CrossRef

Koitka S, Kroll L, Malamutmann E, Oezcelik A, Nensa F (2021) Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks. Eur Radiol 31(4):1795–1804CrossRef

Zopfs D, Bousabarah K, Lennartz S et al (2020) Evaluating body composition by combining quantitative spectral detector computed tomography and deep learning-based image segmentation. Eur J Radiol 130:109153CrossRef

Burns JE, Yao J, Chalhoub D, Chen JJ, Summers RM (2020) A machine learning algorithm to estimate sarcopenia on abdominal CT. Acad Radiol 27(3):311–320CrossRef

10.

Fuchs G, Chretien YR, Mario J et al (2018) Quantifying the effect of slice thickness, intravenous contrast and tube current on muscle segmentation: implications for body composition analysis. Eur Radiol 28(6):2455–2463CrossRef

11.

Kim J-H, Choi K-H, Kang K-W et al (2019) Impact of visceral adipose tissue on clinical outcomes after acute ischemic stroke. Stroke 50(2):448–454CrossRef

12.

Derstine BA, Holcombe SA, Goulson RL et al (2017) Quantifying sarcopenia reference values using lumbar and thoracic muscle areas in a healthy population. J Nutr Health Aging 21(10):180–185PubMed

13.

Derstine BA, Holcombe SA, Ross BE, Wang NC, Su GL, Wang SC (2018) Skeletal muscle cutoff values for sarcopenia diagnosis using T10 to L5 measurements in a healthy US population. Sci Rep 8(1):11369CrossRef

14.

Nemec U, Heidinger B, Sokas C, Chu L, Eisenberg RL (2017) Diagnosing sarcopenia on thoracic computed tomography: quantitative assessment of skeletal muscle mass in patients undergoing transcatheter Aortic Valve Replacement. Acad Radiol 24(9):1154–1161CrossRef

15.

Jung AR, Roh J-L, Kim JS, Choi S-H, Nam SY, Kim SY (2019) Efficacy of head and neck computed tomography for skeletal muscle mass estimation in patients with head and neck cancer. Oral Oncol 95:95–99CrossRef

16.

Swartz JE, Pothen AJ, Wegner I et al (2016) Feasibility of using head and neck CT imaging to assess skeletal muscle mass in head and neck cancer patients. Oral Oncol 62:28–33CrossRef

17.

Zwart AT, van der Hoorn A, van Ooijen PMA, Steenbakkers RJHM, de Bock GH, Halmos GB (2019) CT-measured skeletal muscle mass used to assess frailty in patients with head and neck cancer. J Cachexia Sarcopenia Muscle 10(5):1060–1069CrossRef

18.

Cruz-Jentoft AJ, Sayer AA (2019) Sarcopenia. Lancet 393(10191):2636–2646CrossRef

19.

Ubachs J, Ziemons J, Minis-Rutten IJG et al (2019) Sarcopenia and ovarian cancer survival: a systematic review and meta-analysis. J Cachexia Sarcopenia Muscle 10(6):1165–1174CrossRef

20.

Hsu C-S, Kao J-H (2018) Sarcopenia and chronic liver diseases. Expert Rev Gastroenterol Hepatol 12(12):1229–1244CrossRef

21.

Hwang F, McGreevy CM, Pentakota SR et al (2019) Sarcopenia is predictive of functional outcomes in older trauma patients. Cureus 11(11):e6154PubMedPubMedCentral

22.

Xia Y, Ergun DL, Wacker WK, Wang X, Davis CE, Kaul S (2014) Relationship between dual-energy X-ray absorptiometry volumetric assessment and X-ray computed tomography-derived single-slice measurement of visceral fat. J Clin Densitom 17(1):78–83CrossRef

23.

Geraghty EM, Boone JM (2003) Determination of height, weight, body mass index, and body surface area with a single abdominal CT image. Radiology 228(3):857–863CrossRef

24.

Ward LC (2019) Bioelectrical impedance analysis for body composition assessment: reflections on accuracy, clinical utility, and standardisation. Eur J Clin Nutr 73(2):194–199CrossRef

25.

Coppini LZ, Waitzberg DL, Campos ACL (2005) Limitations and validation of bioelectrical impedance analysis in morbidly obese patients. Curr Opin Clin Nutr Metab Care 8(3):329–332CrossRef

26.

Morsbach F, Zhang Y-H, Martin L, Lindqvist C, Brismar T (2019) Body composition evaluation with computed tomography: contrast media and slice thickness cause methodological errors. Nutrition 59:50–55CrossRef

27.

Troschel AS, Troschel FM, Fuchs G et al (2021) Significance of acquisition parameters for adipose tissue segmentation on CT images. AJR Am J Roentgenol 217(1):177–185CrossRef

Title: Two-dimensional CT measurements enable assessment of body composition on head and neck CT
Authors: David Zopfs
Daniel Pinto dos Santos
Jonathan Kottlors
Robert P. Reimer
Simon Lennartz
Roman Kloeckner
Max Schlaak
Sebastian Theurich
Christoph Kabbasch
Marc Schlamann
Nils Große Hokamp
Publication date: 07-04-2022
Publisher: Springer Berlin Heidelberg
Keywords: Sarcopenia
CT of the Neck
Published in: European Radiology / Issue 9/2022
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-022-08773-9

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Two-dimensional CT measurements enable assessment of body composition on head and neck CT

Abstract

Objectives

Methods

Results

Conclusions

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Key Points

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