Imaging of Body Composition

Silvia Gazzotti; Rebecca Sassi; Maria Pilar Aparisi Gómez; Riccardo Guglielmi; Violeta Vasilevska Nikodinovska; Carmelo Messina; Giuseppe Guglielmi; Alberto Bazzocchi

doi:10.1055/s-0044-1788887

Seminars in Musculoskeletal Radiology, Inhaltsverzeichnis

Semin Musculoskelet Radiol 2024; 28(05): 594-609
DOI: 10.1055/s-0044-1788887

Review Article

Imaging of Body Composition

Silvia Gazzotti

¹Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy

,

Rebecca Sassi

¹Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy

,

Maria Pilar Aparisi Gómez

²Department of Radiology, Te Toka Tumai Auckland (Auckland District Health Board), Auckland, New Zealand

³Department of Anatomy and Medical Imaging, Faculty of Medical and Health Sciences, Waipapa Taumata Rau, University of Auckland, Auckland, New Zealand

⁴Department of Radiology, IMSKE, Valencia, Spain

,

Riccardo Guglielmi

⁵Department of Radiology and Nuclear Medicine, Luzerner Kantonsspital, Lucerne, Switzerland

,

Violeta Vasilevska Nikodinovska

⁶Faculty of Medicine, Ss. Cyril and Methodius University, Skopje, North Macedonia

⁷Radiology Department, University Surgical Clinic St. Naum Ohridski, Skopje, Macedonia

,

Carmelo Messina

⁸IRCCS Istituto Ortopedico Galeazzi, Milan, Italy

⁹Department of Biomedical Sciences for Health, Università Degli Studi di Milano, Milan, Italy

,

Giuseppe Guglielmi

¹⁰Department of Clinical and Experimental Medicine, Foggia University School of Medicine, Foggia, Italy

¹¹Radiology Unit, “Dimiccoli” Hospital, Barletta, Italy

¹²Radiology Unit, IRCCS “Casa Sollievo della Sofferenza” Hospital, San Giovanni Rotondo, Italy

,

Alberto Bazzocchi

¹Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy

› Institutsangaben

Abstract

Body composition is now recognized to have a major impact on health and disease. Imaging enables its analysis in an objective and quantitative way through diverse techniques such as dual-energy X-ray absorptiometry, computed tomography, magnetic resonance imaging, and ultrasonography. This review article first surveys the methodological aspects underpinning the use of these modalities to assess body composition, highlighting their strengths and limitations as well as the set of parameters that they measure and their clinical relevance. It then provides an update on the main applications of body composition imaging in current practice, with a focus on sarcopenia, obesity, lipodystrophies, cancer, and critical care. We conclude by considering the emerging role of artificial intelligence in the analysis of body composition, enabling the extraction of numerous metrics with the potential to refine prognostication and management across a number of pathologies, paving the way toward personalized medicine.

Keywords

body composition - sarcopenia - obesity - artificial intelligence

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Referenzen

References
1 Wang ZM, Pierson Jr RN, Heymsfield SB. The five-level model: a new approach to organizing body-composition research. Am J Clin Nutr 1992; 56 (01) 19-28
2 Shepherd JA, Ng BK, Sommer MJ, Heymsfield SB. Body composition by DXA. Bone 2017; 104: 101-105
3 Messina C, Albano D, Gitto S. et al. Body composition with dual energy X-ray absorptiometry: from basics to new tools. Quant Imaging Med Surg 2020; 10 (08) 1687-1698
4 Bazzocchi A, Ponti F, Albisinni U, Battista G, Guglielmi G. DXA: Technical aspects and application. Eur J Radiol 2016; 85 (08) 1481-1492
5 Simoni P, Guglielmi R, Aparisi Gómez MP. Imaging of body composition in children. Quant Imaging Med Surg 2020; 10 (08) 1661-1671
6 International Society for Clinical Densitometry. ISCD Adult Official Positions 2023. Available from: https://iscd.org/learn/official-positions/adult-positions/ . Accessed July 31, 2024
7 Powers C, Fan B, Borrud LG, Looker AC, Shepherd JA. Long-term precision of dual-energy X-ray absorptiometry body composition measurements and association with their covariates. J Clin Densitom 2015; 18 (01) 76-85
8 Maïmoun L, Alonso S, Mahadea KK, Boudousq V, Mura T, Mariano-Goulart D. Cross-calibration study of the stratos and hologic QDR 4500A dual-energy X-ray absorptiometers to assess bone mineral density and body composition. J Clin Densitom 2023; 26 (04) 101434
9 Kaul S, Rothney MP, Peters DM. et al. Dual-energy X-ray absorptiometry for quantification of visceral fat. Obesity (Silver Spring) 2012; 20 (06) 1313-1318
10 Neeland IJ, Grundy SM, Li X, Adams-Huet B, Vega GL. Comparison of visceral fat mass measurement by dual-X-ray absorptiometry and magnetic resonance imaging in a multiethnic cohort: the Dallas Heart Study. Nutr Diabetes 2016; 6 (07) e221
11 Neeland IJ, Ross R, Després JP. et al; International Atherosclerosis Society, International Chair on Cardiometabolic Risk Working Group on Visceral Obesity. Visceral and ectopic fat, atherosclerosis, and cardiometabolic disease: a position statement. Lancet Diabetes Endocrinol 2019; 7 (09) 715-725
12 Guerri S, Mercatelli D, Aparisi Gómez MP. et al. Quantitative imaging techniques for the assessment of osteoporosis and sarcopenia. Quant Imaging Med Surg 2018; 8 (01) 60-85
13 Erlandson MC, Lorbergs AL, Mathur S, Cheung AM. Muscle analysis using pQCT, DXA and MRI. Eur J Radiol 2016; 85 (08) 1505-1511
14 Tolonen A, Pakarinen T, Sassi A. et al. Methodology, clinical applications, and future directions of body composition analysis using computed tomography (CT) images: a review. Eur J Radiol 2021; 145: 109943
15 Amini B, Boyle SP, Boutin RD, Lenchik L. Approaches to assessment of muscle mass and myosteatosis on computed tomography: a systematic review. J Gerontol A Biol Sci Med Sci 2019; 74 (10) 1671-1678
16 Mourtzakis M, Prado CMM, Lieffers JR, Reiman T, McCargar LJ, Baracos VE. A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care. Appl Physiol Nutr Metab 2008; 33 (05) 997-1006
17 van Heusden HC, Swartz JE, Chargi N. et al. Feasibility of assessment of skeletal muscle mass on a single cross-sectional image at the level of the fourth thoracic vertebra. Eur J Radiol 2021; 142: 109879
18 Ahn H, Kim DW, Ko Y. et al. Updated systematic review and meta-analysis on diagnostic issues and the prognostic impact of myosteatosis: a new paradigm beyond sarcopenia. Ageing Res Rev 2021; 70: 101398
19 Engelke K, Museyko O, Wang L, Laredo JD. Quantitative analysis of skeletal muscle by computed tomography imaging—state of the art. J Orthop Translat 2018; 15: 91-103
20 Morsbach F, Zhang YH, Martin L, Lindqvist C, Brismar T. Body composition evaluation with computed tomography: contrast media and slice thickness cause methodological errors. Nutrition 2019; 59: 50-55
21 Huber FA, Del Grande F, Rizzo S, Guglielmi G, Guggenberger R. MRI in the assessment of adipose tissues and muscle composition: how to use it. Quant Imaging Med Surg 2020; 10 (08) 1636-1649
22 Borga M. MRI adipose tissue and muscle composition analysis—a review of automation techniques. Br J Radiol 2018; 91 (1089) 20180252
23 Chianca V, Albano D, Messina C. et al. Sarcopenia: imaging assessment and clinical application. Abdom Radiol (NY) 2022; 47 (09) 3205-3216
24 Borga M, Ahlgren A, Romu T, Widholm P, Dahlqvist Leinhard O, West J. Reproducibility and repeatability of MRI-based body composition analysis. Magn Reson Med 2020; 84 (06) 3146-3156
25 Schweitzer L, Geisler C, Pourhassan M. et al. What is the best reference site for a single MRI slice to assess whole-body skeletal muscle and adipose tissue volumes in healthy adults?. Am J Clin Nutr 2015; 102 (01) 58-65
26 Ponti F, De Cinque A, Fazio N, Napoli A, Guglielmi G, Bazzocchi A. Ultrasound imaging, a stethoscope for body composition assessment. Quant Imaging Med Surg 2020; 10 (08) 1699-1722
27 Stock MS, Thompson BJ. Echo intensity as an indicator of skeletal muscle quality: applications, methodology, and future directions. Eur J Appl Physiol 2021; 121 (02) 369-380
28 Tang X, Wang L, Guo R, Huang S, Tang Y, Qiu L. Application of ultrasound elastography in the evaluation of muscle strength in a healthy population. Quant Imaging Med Surg 2020; 10 (10) 1961-1972
29 Vasilevska Nikodinovska V, Ivanoski S. Sarcopenia, more than just muscle atrophy: imaging methods for the assessment of muscle quantity and quality. Röfo Fortschr Geb Röntgenstr Neuen Bildgeb Verfahr 2023; 195 (09) 777-789
30 Bazzocchi A, Filonzi G, Ponti F. et al. Accuracy, reproducibility and repeatability of ultrasonography in the assessment of abdominal adiposity. Acad Radiol 2011; 18 (09) 1133-1143
31 Schlecht I, Wiggermann P, Behrens G. et al. Reproducibility and validity of ultrasound for the measurement of visceral and subcutaneous adipose tissues. Metabolism 2014; 63 (12) 1512-1519
32 Santos R, Armada-da-Silva PAS. Reproducibility of ultrasound-derived muscle thickness and echo-intensity for the entire quadriceps femoris muscle. Radiography 2017; 23 (03) e51-e61
33 Reeves ND, Maganaris CN, Narici MV. Ultrasonographic assessment of human skeletal muscle size. Eur J Appl Physiol 2004; 91 (01) 116-118
34 Lima J, Foletto E, Cardoso RCB. et al. Ultrasound for measurement of skeletal muscle mass quantity and muscle composition/architecture in critically ill patients: a scoping review on studies' aims, methods, and findings. Clin Nutr 2024; 43 (01) 95-110
35 Zieff G, Cornwall J, Blue MN, Smith-Ryan AE, Stoner L. Ultrasound-based measurement of central adiposity: key considerations and guidelines. Obes Rev 2024; 25 (05) e13716
36 Cruz-Jentoft AJ, Bahat G, Bauer J. et al; Writing Group for the European Working Group on Sarcopenia in Older People 2 (EWGSOP2), and the Extended Group for EWGSOP2. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing 2019; 48 (01) 16-31
37 Liu B, Du Y, Wu Y, Snetselaar LG, Wallace RB, Bao W. Trends in obesity and adiposity measures by race or ethnicity among adults in the United States 2011–18: population based study. BMJ 2021; 372 (365) n365
38 Wallengren O, Bosaeus I, Frändin K. et al. Comparison of the 2010 and 2019 diagnostic criteria for sarcopenia by the European Working Group on Sarcopenia in Older People (EWGSOP) in two cohorts of Swedish older adults. BMC Geriatr 2021; 21 (01) 600
39 Ivanoski S, Vasilevska Nikodinovska V. Future ultrasound biomarkers for sarcopenia: elastography, contrast-enhanced ultrasound, and speed of sound ultrasound imaging. Semin Musculoskelet Radiol 2020; 24 (02) 194-200
40 Perkisas S, Bastijns S, Baudry S. et al. Application of ultrasound for muscle assessment in sarcopenia: 2020 SARCUS update. Eur Geriatr Med 2021; 12 (01) 45-59
41 Tagliafico AS, Bignotti B, Torri L, Rossi F. Sarcopenia: how to measure, when and why. Radiol Med (Torino) 2022; 127 (03) 228-237
42 Gallagher D, Visser M, Sepúlveda D, Pierson RN, Harris T, Heymsfield SB. How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups?. Am J Epidemiol 1996; 143 (03) 228-239
43 Abraham TM, Pedley A, Massaro JM, Hoffmann U, Fox CS. Association between visceral and subcutaneous adipose depots and incident cardiovascular disease risk factors. Circulation 2015; 132 (17) 1639-1647
44 Agrawal S, Klarqvist MDR, Diamant N. et al. BMI-adjusted adipose tissue volumes exhibit depot-specific and divergent associations with cardiometabolic diseases. Nat Commun 2023; 14 (01) 266
45 Bray GA. Beyond BMI. Nutrients 2023; 15 (10) 2254
46 Donini LM, Busetto L, Bischoff SC. et al. Definition and diagnostic criteria for sarcopenic obesity: ESPEN and EASO consensus statement. Obes Facts 2022; 15 (03) 321-335
47 Srikanthan P, Hevener AL, Karlamangla AS. Sarcopenia exacerbates obesity-associated insulin resistance and dysglycemia: findings from the National Health and Nutrition Examination Survey III. PLoS One 2010; 5 (05) e10805
48 Ponti F, Plazzi A, Guglielmi G, Marchesini G, Bazzocchi A. Body composition, dual-energy X-ray absorptiometry and obesity: the paradigm of fat (re)distribution. BJR Case Rep 2019; 5 (03) 20170078
49 Dalili D, Bazzocchi A, Dalili DE, Guglielmi G, Isaac A. The role of body composition assessment in obesity and eating disorders. Eur J Radiol 2020; 131: 109227
50 Sylivris A, Mesinovic J, Scott D, Jansons P. Body composition changes at 12 months following different surgical weight loss interventions in adults with obesity: a systematic review and meta-analysis of randomized control trials. Obes Rev 2022; 23 (07) e13442
51 Yamamoto A, Kikuchi Y, Kusakabe T. et al. Imaging spectrum of abnormal subcutaneous and visceral fat distribution. Insights Imaging 2020; 11 (01) 24
52 Fiorenza CG, Chou SH, Mantzoros CS. Lipodystrophy: pathophysiology and advances in treatment. Nat Rev Endocrinol 2011; 7 (03) 137-150
53 Viskovic K, Richman I, Klasnic K. et al. Assessment of ultrasound for use in detecting lipoatrophy in HIV-infected patients taking combination antiretroviral therapy. AIDS Patient Care STDS 2009; 23 (02) 79-84
54 Brown JC, Cespedes Feliciano EM, Caan BJ. The evolution of body composition in oncology—epidemiology, clinical trials, and the future of patient care: facts and numbers. J Cachexia Sarcopenia Muscle 2018; 9 (07) 1200-1208
55 Hanna L, Nguo K, Furness K, Porter J, Huggins CE. Association between skeletal muscle mass and quality of life in adults with cancer: a systematic review and meta-analysis. J Cachexia Sarcopenia Muscle 2022; 13 (02) 839-857
56 Gonzalez MC, Pastore CA, Orlandi SP, Heymsfield SB. Obesity paradox in cancer: new insights provided by body composition. Am J Clin Nutr 2014; 99 (05) 999-1005
57 Lauby-Secretan B, Scoccianti C, Loomis D, Grosse Y, Bianchini F, Straif K. International Agency for Research on Cancer Handbook Working Group. Body fatness and cancer—viewpoint of the IARC Working Group. N Engl J Med 2016; 375 (08) 794-798
58 Cheng E, Kirley J, Cespedes Feliciano EM, Caan BJ. Adiposity and cancer survival: a systematic review and meta-analysis. Cancer Causes Control 2022; 33 (10) 1219-1246
59 Shachar SS, Williams GR, Muss HB, Nishijima TF. Prognostic value of sarcopenia in adults with solid tumours: a meta-analysis and systematic review. Eur J Cancer 2016; 57: 58-67
60 Aleixo GFP, Shachar SS, Nyrop KA, Muss HB, Malpica L, Williams GR. Myosteatosis and prognosis in cancer: systematic review and meta-analysis. Crit Rev Oncol Hematol 2020; 145: 102839
61 Bradshaw PT. Body composition and cancer survival: a narrative review. Br J Cancer 2024; 130 (02) 176-183
62 Surov A, Pech M, Gessner D. et al. Low skeletal muscle mass is a predictor of treatment related toxicity in oncologic patients. A meta-analysis. Clin Nutr 2021; 40 (10) 5298-5310
63 Medici F, Bazzocchi A, Buwenge M. et al. Impact and treatment of sarcopenia in patients undergoing radiotherapy: a multidisciplinary, AMSTAR-2 compliant review of systematic reviews and metanalyses. Front Oncol 2022; 12: 887156
64 Zhang XM, Chen D, Xie XH, Zhang JE, Zeng Y, Cheng AS. Sarcopenia as a predictor of mortality among the critically ill in an intensive care unit: a systematic review and meta-analysis. BMC Geriatr 2021; 21 (01) 339
65 Mundi MS, Patel JJ, Martindale R. Body Composition technology: implications for the ICU. Nutr Clin Pract 2019; 34 (01) 48-58
66 Erstad BL, Barletta JF. Drug dosing in the critically ill obese patient: a focus on medications for hemodynamic support and prophylaxis. Crit Care 2021; 25 (01) 77
67 De Rosa S, Umbrello M, Pelosi P, Battaglini D. Update on lean body mass diagnostic assessment in critical illness. Diagnostics (Basel) 2023; 13 (05) 888
68 da Silva Passos LB, Macedo TAA, De-Souza DA. Nutritional state assessed by ultrasonography, but not by bioelectric impedance, predicts 28-day mortality in critically ill patients. Prospective cohort study. Clin Nutr 2021; 40 (12) 5742-5750
69 Wang B, Torriani M. Artificial intelligence in the evaluation of body composition. Semin Musculoskelet Radiol 2020; 24 (01) 30-37
70 Elhakim T, Trinh K, Mansur A, Bridge C, Daye D. Role of machine learning-based CT body composition in risk prediction and prognostication: current state and future directions. Diagnostics (Basel) 2023; 13 (05) 968
71 Bedrikovetski S, Seow W, Kroon HM, Traeger L, Moore JW, Sammour T. Artificial intelligence for body composition and sarcopenia evaluation on computed tomography: a systematic review and meta-analysis. Eur J Radiol 2022; 149: 110218
72 Magudia K, Bridge CP, Bay CP. et al. Population-scale CT-based body composition analysis of a large outpatient population using deep learning to derive age-, sex-, and race-specific reference curves. Radiology 2021; 298 (02) 319-329
73 Magudia K, Bridge CP, Bay CP. et al. Utility of normalized body composition areas, derived from outpatient abdominal CT using a fully automated deep learning method, for predicting subsequent cardiovascular events. AJR Am J Roentgenol 2023; 220 (02) 236-244
74 Mai DVC, Drami I, Pring ET. et al; BiCyCLE Research Group. A systematic review of automated segmentation of 3D computed-tomography scans for volumetric body composition analysis. J Cachexia Sarcopenia Muscle 2023; 14 (05) 1973-1986
75 Geis JR, Brady AP, Wu CC. et al. Ethics of artificial intelligence in radiology: summary of the Joint European and North American Multisociety Statement. Radiology 2019; 293 (02) 436-440
76 Santoro A, Guidarelli G, Ostan R. et al. Gender-specific association of body composition with inflammatory and adipose-related markers in healthy elderly Europeans from the NU-AGE study. Eur Radiol 2019; 29 (09) 4968-4979
77 Ma W, Zhu H, Yu X. et al. Association between android fat mass, gynoid fat mass and cardiovascular and all-cause mortality in adults: NHANES 2003–2007. Front Cardiovasc Med 2023; 10: 1055223
78 Liu P, Ma F, Lou H, Liu Y. The utility of fat mass index vs. body mass index and percentage of body fat in the screening of metabolic syndrome. BMC Public Health 2013; 13: 629
79 Mongraw-Chaffin M, Allison MA, Burke GL. et al. CT-derived body fat distribution and incident cardiovascular disease: the multi-ethnic study of atherosclerosis. J Clin Endocrinol Metab 2017; 102 (11) 4173-4183
80 Baggerman MR, Dekker IM, Winkens B. et al. Visceral obesity measured using computed tomography scans: no significant association with mortality in critically ill patients. J Crit Care 2023; 77: 154316
81 Pickhardt PJ, Graffy PM, Zea R. et al. Automated CT biomarkers for opportunistic prediction of future cardiovascular events and mortality in an asymptomatic screening population: a retrospective cohort study. Lancet Digit Health 2020; 2 (04) e192-e200
82 den Os OE, Nielen R, Bidar E. Myosteatosis as a prognostic marker for postoperative mortality in adult patients undergoing surgery in general—a systematic review. Surgeries (Basel) 2023; 4 (04) 647-664
83 Loosen SH, Schulze-Hagen M, Püngel T. et al. Skeletal muscle composition predicts outcome in critically ill patients. Crit Care Explor 2020; 2 (08) e0171
84 Ebadi M, Tsien C, Bhanji RA. et al. Myosteatosis in cirrhosis: a review of diagnosis, pathophysiological mechanisms and potential interventions. Cells 2022; 11 (07) 1216
85 Nachit M, Horsmans Y, Summers RM, Leclercq IA, Pickhardt PJ. AI-based CT body composition identifies myosteatosis as key mortality predictor in asymptomatic adults. Radiology 2023; 307 (05) e222008
86 Neeland IJ, Ayers CR, Rohatgi AK. et al. Associations of visceral and abdominal subcutaneous adipose tissue with markers of cardiac and metabolic risk in obese adults. Obesity (Silver Spring) 2013; 21 (09) E439-E447
87 Linge J, Widholm P, Nilsson D, Kugelberg A, Olbers T, Dahlqvist Leinhard O. Risk stratification using magnetic resonance imaging-derived, personalized z-scores of visceral adipose tissue, subcutaneous adipose tissue, and liver fat in persons with obesity. Surg Obes Relat Dis 2024; 20 (05) 419-424
88 Engelke K, Chaudry O, Gast L. et al. Magnetic resonance imaging techniques for the quantitative analysis of skeletal muscle: state of the art. J Orthop Translat 2023; 42: 57-72
89 Linge J, Petersson M, Forsgren MF, Sanyal AJ, Dahlqvist Leinhard O. Adverse muscle composition predicts all-cause mortality in the UK Biobank imaging study. J Cachexia Sarcopenia Muscle 2021; 12 (06) 1513-1526
90 Kim SK, Kim HJ, Hur KY. et al. Visceral fat thickness measured by ultrasonography can estimate not only visceral obesity but also risks of cardiovascular and metabolic diseases. Am J Clin Nutr 2004; 79 (04) 593-599
91 Vlachos IS, Hatziioannou A, Perelas A, Perrea DN. Sonographic assessment of regional adiposity. AJR Am J Roentgenol 2007; 189 (06) 1545-1553
92 Bertoli S, Leone A, Vignati L. et al. Metabolic correlates of subcutaneous and visceral abdominal fat measured by ultrasonography: a comparison with waist circumference. Nutr J 2016; 15: 2
93 Perkisas S, Baudry S, Bauer J. et al. Application of ultrasound for muscle assessment in sarcopenia: towards standardized measurements. Eur Geriatr Med 2018; 9 (06) 739-757
94 Prell T, Grimm A, Axer H. Uncovering sarcopenia and frailty in older adults by using muscle ultrasound—a narrative review. Front Med (Lausanne) 2024; 11: 1333205
95 Casey P, Alasmar M, McLaughlin J. et al. The current use of ultrasound to measure skeletal muscle and its ability to predict clinical outcomes: a systematic review. J Cachexia Sarcopenia Muscle 2022; 13 (05) 2298-2309
96 Strasser EM, Draskovits T, Praschak M, Quittan M, Graf A. Association between ultrasound measurements of muscle thickness, pennation angle, echogenicity and skeletal muscle strength in the elderly. Age (Dordr) 2013; 35 (06) 2377-2388
97 Bunout D, Gonzalez S, Canales M, Barrera G, Hirsch S. Ultrasound assessment of rectus femoris pennation angle and echogenicity. Their association with muscle functional measures and fat infiltration measured by CT scan. Clin Nutr ESPEN 2023; 55: 420-424
98 Lenchik L, Mazzoli V, Cawthon PM, Hepple RT, Boutin RD. Muscle steatosis and fibrosis in older adults, from the AJR special series on imaging of fibrosis. AJR Am J Roentgenol 2024; 222 (05) e2329742