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Published in: European Radiology 3/2020

01-03-2020 | Computed Tomography | Computed Tomography

Single-slice CT measurements allow for accurate assessment of sarcopenia and body composition

Authors: David Zopfs, Sebastian Theurich, Nils Große Hokamp, Jana Knuever, Lukas Gerecht, Jan Borggrefe, Max Schlaak, Daniel Pinto dos Santos

Published in: European Radiology | Issue 3/2020

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Abstract

Objectives

To evaluate the correlation between simple planimetric measurements in axial computed tomography (CT) slices and measurements of patient body composition and anthropometric data performed with bioelectrical impedance analysis (BIA) and metric clinical assessments.

Methods

In this prospective cross-sectional study, we analyzed data of a cohort of 62 consecutive, untreated adult patients with advanced malignant melanoma who underwent concurrent BIA assessments at their radiologic baseline staging by CT between July 2016 and October 2017. To assess muscle and adipose tissue mass, we analyzed the areas of the paraspinal muscles as well as the cross-sectional total patient area in a single CT slice at the height of the third lumbar vertebra. These measurements were subsequently correlated with anthropometric (body weight) and body composition parameters derived from BIA (muscle mass, fat mass, fat-free mass, and visceral fat mass). Linear regression models were built to allow for estimation of each parameter based on CT measurements.

Results

Linear regression models allowed for accurate prediction of patient body weight (adjusted R2 = 0.886), absolute muscle mass (adjusted R2 = 0.866), fat-free mass (adjusted R2 = 0.855), and total as well as visceral fat mass (adjusted R2 = 0.887 and 0.839, respectively).

Conclusions

Our data suggest that patient body composition can accurately and quantitatively be determined by using simple measurements in a single axial CT slice. This could be useful in various medical and scientific settings, where the knowledge of the patient’s anthropometric parameters is not immediately or easily available.

Key Points

Easy to perform measurements on a single CT slice highly correlate with clinically valuable parameters of body composition.
Body composition data were acquired using bioelectrical impedance analysis to correlate CT measurements with a non-imaging-based method, which is frequently lacking in previous studies.
The obtained equations facilitate a quick, opportunistic assessment of relevant parameters of body composition.
Literature
1.
go back to reference Bae KT, Tao C, Gürel S et al (2007) Effect of patient weight and scanning duration on contrast enhancement during pulmonary multidetector CT angiography. Radiology 242(2):582–589CrossRef Bae KT, Tao C, Gürel S et al (2007) Effect of patient weight and scanning duration on contrast enhancement during pulmonary multidetector CT angiography. Radiology 242(2):582–589CrossRef
2.
go back to reference Fernandes CM, Clark S, Price A, Innes G (1999) How accurately do we estimate patients’ weight in emergency departments? Can Fam Physician 45:2373–2376PubMedPubMedCentral Fernandes CM, Clark S, Price A, Innes G (1999) How accurately do we estimate patients’ weight in emergency departments? Can Fam Physician 45:2373–2376PubMedPubMedCentral
3.
go back to reference Lorenz MW, Graf M, Henke C et al (2007) Anthropometric approximation of body weight in unresponsive stroke patients. J Neurol Neurosurg Psychiatry 78(12):1331–1336CrossRef Lorenz MW, Graf M, Henke C et al (2007) Anthropometric approximation of body weight in unresponsive stroke patients. J Neurol Neurosurg Psychiatry 78(12):1331–1336CrossRef
4.
go back to reference Gascho D, Ganzoni L, Kolly P et al (2017) A new method for estimating patient body weight using CT dose modulation data. Eur Radiol Exp 1(1):23CrossRef Gascho D, Ganzoni L, Kolly P et al (2017) A new method for estimating patient body weight using CT dose modulation data. Eur Radiol Exp 1(1):23CrossRef
5.
go back to reference Cubison TC, Gilbert PM (2005) So much for percentage, but what about the weight? Emerg Med J 22(9):643–645 Cubison TC, Gilbert PM (2005) So much for percentage, but what about the weight? Emerg Med J 22(9):643–645
6.
go back to reference Leary TS, Milner QJ, Niblett DJ (2000) The accuracy of the estimation of body weight and height in the intensive care unit. Eur J Anaesthesiol 17(11):698–703CrossRef Leary TS, Milner QJ, Niblett DJ (2000) The accuracy of the estimation of body weight and height in the intensive care unit. Eur J Anaesthesiol 17(11):698–703CrossRef
7.
go back to reference Gallagher D, Kelley DE, Yim JE et al (2009) Adipose tissue distribution is different in type 2 diabetes. Am J Clin Nutr 89(3):807–814 Gallagher D, Kelley DE, Yim JE et al (2009) Adipose tissue distribution is different in type 2 diabetes. Am J Clin Nutr 89(3):807–814
8.
go back to reference Scherzer R, Shen W, Bacchetti P et al (2008) Comparison of dual-energy X-ray absorptiometry and magnetic resonance imaging-measured adipose tissue depots in HIV-infected and control subjects. Am J Clin Nutr 88(4):1088–1096CrossRef Scherzer R, Shen W, Bacchetti P et al (2008) Comparison of dual-energy X-ray absorptiometry and magnetic resonance imaging-measured adipose tissue depots in HIV-infected and control subjects. Am J Clin Nutr 88(4):1088–1096CrossRef
9.
go back to reference Buckley RG, Stehman CR, Dos Santos FL et al (2012) Bedside method to estimate actual body weight in the emergency department. J Emerg Med 42(1):100–104CrossRef Buckley RG, Stehman CR, Dos Santos FL et al (2012) Bedside method to estimate actual body weight in the emergency department. J Emerg Med 42(1):100–104CrossRef
12.
go back to reference Lenchik L, Boutin RD (2018) Sarcopenia: beyond muscle atrophy and into the new frontiers of opportunistic imaging, precision medicine, and machine learning. Semin Musculoskelet Radiol 22(3):307–322CrossRef Lenchik L, Boutin RD (2018) Sarcopenia: beyond muscle atrophy and into the new frontiers of opportunistic imaging, precision medicine, and machine learning. Semin Musculoskelet Radiol 22(3):307–322CrossRef
13.
go back to reference 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 57:58–67 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 57:58–67
14.
go back to reference Chang KV, Chen JD, Wu WT, Huang KC, Hsu CT, Han DS (2018) Association between loss of skeletal muscle mass and mortality and tumor recurrence in hepatocellular carcinoma: a systematic review and meta-analysis. Liver Cancer 7(1):90–103 Chang KV, Chen JD, Wu WT, Huang KC, Hsu CT, Han DS (2018) Association between loss of skeletal muscle mass and mortality and tumor recurrence in hepatocellular carcinoma: a systematic review and meta-analysis. Liver Cancer 7(1):90–103
15.
go back to reference Sconfienza LM (2019) Sarcopenia: ultrasound today, smartphones tomorrow? Eur Radiol 29(1):1–2CrossRef Sconfienza LM (2019) Sarcopenia: ultrasound today, smartphones tomorrow? Eur Radiol 29(1):1–2CrossRef
16.
go back to reference Sergi G, de Rui M, Stubbs B, Veronese N, Manzato E (2017) Measurement of lean body mass using bioelectrical impedance analysis: a consideration of the pros and cons. Aging Clin Exp Res 29(4):591–597CrossRef Sergi G, de Rui M, Stubbs B, Veronese N, Manzato E (2017) Measurement of lean body mass using bioelectrical impedance analysis: a consideration of the pros and cons. Aging Clin Exp Res 29(4):591–597CrossRef
17.
go back to reference Lemos T, Gallagher D (2017) Current body composition measurement techniques. Curr Opin Endocrinol Diabetes Obes 24(5):310–314CrossRef Lemos T, Gallagher D (2017) Current body composition measurement techniques. Curr Opin Endocrinol Diabetes Obes 24(5):310–314CrossRef
18.
go back to reference 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 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
19.
go back to reference Kuriyan R (2018) Body composition techniques. Indian J Med Res 148(5):648–658CrossRef Kuriyan R (2018) Body composition techniques. Indian J Med Res 148(5):648–658CrossRef
20.
go back to reference Gonzalez MC, Heymsfield SB (2017) Bioelectrical impedance analysis for diagnosing sarcopenia and cachexia: what are we really estimating? J Cachexia Sarcopenia Muscle 8(2):187–189CrossRef Gonzalez MC, Heymsfield SB (2017) Bioelectrical impedance analysis for diagnosing sarcopenia and cachexia: what are we really estimating? J Cachexia Sarcopenia Muscle 8(2):187–189CrossRef
21.
go back to reference Chula de Castro JA, Lima TR, Silva DAS (2018) Body composition estimation in children and adolescents by bioelectrical impedance analysis: a systematic review. J Bodyw Mov Ther 22(1):134–146CrossRef Chula de Castro JA, Lima TR, Silva DAS (2018) Body composition estimation in children and adolescents by bioelectrical impedance analysis: a systematic review. J Bodyw Mov Ther 22(1):134–146CrossRef
22.
go back to reference Chien MY, Huang TY, Wu YT (2008) Prevalence of sarcopenia estimated using a bioelectrical impedance analysis prediction equation in community-dwelling elderly people in Taiwan. J Am Geriatr Soc 56(9):1710–1715 Chien MY, Huang TY, Wu YT (2008) Prevalence of sarcopenia estimated using a bioelectrical impedance analysis prediction equation in community-dwelling elderly people in Taiwan. J Am Geriatr Soc 56(9):1710–1715
23.
go back to reference Prado CM, Lieffers JR, McCargar LJ et al (2008) Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: a population-based study. Lancet Oncol 9(7):629–635 Prado CM, Lieffers JR, McCargar LJ et al (2008) Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: a population-based study. Lancet Oncol 9(7):629–635
24.
go back to reference 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 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
25.
go back to reference Thurlow S, Taylor-Covill G, Sahota P, Oldroyd B, Hind K (2018) Effects of procedure, upright equilibrium time, sex and BMI on the precision of body fluid measurements using bioelectrical impedance analysis. Eur J Clin Nutr 72(1):148–153CrossRef Thurlow S, Taylor-Covill G, Sahota P, Oldroyd B, Hind K (2018) Effects of procedure, upright equilibrium time, sex and BMI on the precision of body fluid measurements using bioelectrical impedance analysis. Eur J Clin Nutr 72(1):148–153CrossRef
26.
go back to reference Bosy-Westphal A, Jensen B, Braun W, Pourhassan M, Gallagher D, Müller MJ (2017) Quantification of whole-body and segmental skeletal muscle mass using phase-sensitive 8-electrode medical bioelectrical impedance devices. Eur J Clin Nutr 71(9):1061–1067CrossRef Bosy-Westphal A, Jensen B, Braun W, Pourhassan M, Gallagher D, Müller MJ (2017) Quantification of whole-body and segmental skeletal muscle mass using phase-sensitive 8-electrode medical bioelectrical impedance devices. Eur J Clin Nutr 71(9):1061–1067CrossRef
29.
go back to reference Faron A, Luetkens JA, Schmeel FC, Kuetting DLR, Thomas D, Sprinkart AM (2019) Quantification of fat and skeletal muscle tissue at abdominal computed tomography: associations between single-slice measurements and total compartment volumes. Abdom Radiol (NY) https://doi.org/10.1007/s00261-019-01912-9 Faron A, Luetkens JA, Schmeel FC, Kuetting DLR, Thomas D, Sprinkart AM (2019) Quantification of fat and skeletal muscle tissue at abdominal computed tomography: associations between single-slice measurements and total compartment volumes. Abdom Radiol (NY) https://​doi.​org/​10.​1007/​s00261-019-01912-9
30.
go back to reference Kamiya N, Li J, Kume M, Fujita H, Shen D, Zheng G (2018) Fully automatic segmentation of paraspinal muscles from 3D torso CT images via multi-scale iterative random forest classifications. Int J Comput Assist Radiol Surg 13(11):1697–1706CrossRef Kamiya N, Li J, Kume M, Fujita H, Shen D, Zheng G (2018) Fully automatic segmentation of paraspinal muscles from 3D torso CT images via multi-scale iterative random forest classifications. Int J Comput Assist Radiol Surg 13(11):1697–1706CrossRef
31.
go back to reference Kazemi-Bajestani SM, Mazurak VC, Baracos V (2016) Computed tomography-defined muscle and fat wasting are associated with cancer clinical outcomes. Semin Cell Dev Biol 54:2–10 Kazemi-Bajestani SM, Mazurak VC, Baracos V (2016) Computed tomography-defined muscle and fat wasting are associated with cancer clinical outcomes. Semin Cell Dev Biol 54:2–10
32.
go back to reference Shen W, Punyanitya M, Wang Z et al (2004) Total body skeletal muscle and adipose tissue volumes: estimation from a single abdominal cross-sectional image. J Appl Physiol (Bethesda 1985) 97(6):2333–2338 Shen W, Punyanitya M, Wang Z et al (2004) Total body skeletal muscle and adipose tissue volumes: estimation from a single abdominal cross-sectional image. J Appl Physiol (Bethesda 1985) 97(6):2333–2338
33.
go back to reference 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 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
34.
go back to reference Irlbeck T, Massaro JM, Bamberg F, O’Donnell CJ, Hoffmann U, Fox CS (2010) Association between single-slice measurements of visceral and abdominal subcutaneous adipose tissue with volumetric measurements: the Framingham Heart Study. Int J Obes (Lond) 34(4):781–787CrossRef Irlbeck T, Massaro JM, Bamberg F, O’Donnell CJ, Hoffmann U, Fox CS (2010) Association between single-slice measurements of visceral and abdominal subcutaneous adipose tissue with volumetric measurements: the Framingham Heart Study. Int J Obes (Lond) 34(4):781–787CrossRef
35.
36.
37.
go back to reference Mei KL, Batsis JA, Mills JB, Holubar SD (2016) Sarcopenia and sarcopenic obesity: do they predict inferior oncologic outcomes after gastrointestinal cancer surgery? Perioper Med (Lond) 5:30 Mei KL, Batsis JA, Mills JB, Holubar SD (2016) Sarcopenia and sarcopenic obesity: do they predict inferior oncologic outcomes after gastrointestinal cancer surgery? Perioper Med (Lond) 5:30
38.
go back to reference Deluche E, Leobon S, Desport JC, Venat-Bouvet L, Usseglio J, Tubiana-Mathieu N (2018) Impact of body composition on outcome in patients with early breast cancer. Support Care Cancer 26(3):861–868CrossRef Deluche E, Leobon S, Desport JC, Venat-Bouvet L, Usseglio J, Tubiana-Mathieu N (2018) Impact of body composition on outcome in patients with early breast cancer. Support Care Cancer 26(3):861–868CrossRef
39.
go back to reference Baum T, Lorenz C, Buerger C et al (2018) Automated assessment of paraspinal muscle fat composition based on the segmentation of chemical shift encoding-based water/fat-separated images. Eur Radiol Exp 2(1):32CrossRef Baum T, Lorenz C, Buerger C et al (2018) Automated assessment of paraspinal muscle fat composition based on the segmentation of chemical shift encoding-based water/fat-separated images. Eur Radiol Exp 2(1):32CrossRef
40.
go back to reference Hashimoto F, Kakimoto A, Ota N, Ito S, Nishizawa S (2019) Automated segmentation of 2D low-dose CT images of the psoas-major muscle using deep convolutional neural networks. Radiol Phys Technol 12(2):210–215CrossRef Hashimoto F, Kakimoto A, Ota N, Ito S, Nishizawa S (2019) Automated segmentation of 2D low-dose CT images of the psoas-major muscle using deep convolutional neural networks. Radiol Phys Technol 12(2):210–215CrossRef
42.
43.
go back to reference Looijaard WGPM, Molinger J, Weijs PJM (2018) Measuring and monitoring lean body mass in critical illness. Curr Opin Crit Care 24(4):241–247CrossRef Looijaard WGPM, Molinger J, Weijs PJM (2018) Measuring and monitoring lean body mass in critical illness. Curr Opin Crit Care 24(4):241–247CrossRef
Metadata
Title
Single-slice CT measurements allow for accurate assessment of sarcopenia and body composition
Authors
David Zopfs
Sebastian Theurich
Nils Große Hokamp
Jana Knuever
Lukas Gerecht
Jan Borggrefe
Max Schlaak
Daniel Pinto dos Santos
Publication date
01-03-2020
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 3/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-019-06526-9

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