Download PDF
Rofo
DOI: 10.1055/a-2263-1501
Review

Body composition analysis by radiological imaging – methods, applications, and prospects

Article in several languages: English | deutsch
Nicolas Linder
1   Department of Diagnostic and Interventional Radiology, University of Leipzig Medical Center, Leipzig, Germany
2   Division of Radiology and Nuclear Medicine, Kantonsspital St. Gallen, Sankt Gallen, Switzerland
,
Timm Denecke
1   Department of Diagnostic and Interventional Radiology, University of Leipzig Medical Center, Leipzig, Germany
,
Harald Busse
1   Department of Diagnostic and Interventional Radiology, University of Leipzig Medical Center, Leipzig, Germany
› Author Affiliations
› Further Information
Also available at
   Permissions and Reprints

Abstract

Background This review discusses the quantitative assessment of tissue composition in the human body (body composition, BC) using radiological methods. Such analyses are gaining importance, in particular, for oncological and metabolic problems. The aim is to present the different methods and definitions in this field to a radiological readership in order to facilitate application and dissemination of BC methods. The main focus is on radiological cross-sectional imaging.

Methods The review is based on a recent literature search in the US National Library of Medicine catalog (pubmed.gov) using appropriate search terms (body composition, obesity, sarcopenia, osteopenia in conjunction with imaging and radiology, respectively), as well as our own work and experience, particularly with MRI- and CT-based analyses of abdominal fat compartments and muscle groups.

Results and Conclusion Key post-processing methods such as segmentation of tomographic datasets are now well established and used in numerous clinical disciplines, including bariatric surgery. Validated reference values are required for a reliable assessment of radiological measures, such as fatty liver or muscle. Artificial intelligence approaches (deep learning) already enable the automated segmentation of different tissues and compartments so that the extensive datasets can be processed in a time-efficient manner – in the case of so-called opportunistic screening, even retrospectively from diagnostic examinations. The availability of analysis tools and suitable datasets for AI training is considered a limitation.

Key Points 

  • Radiological imaging methods are increasingly used to determine body composition (BC).

  • BC parameters are usually quantitative and well reproducible.

  • CT image data from routine clinical examinations can be used retrospectively for BC analysis.

  • Prospectively, MRI examinations can be used to determine organ-specific BC parameters.

  • Automated and in-depth analysis methods (deep learning or radiomics) appear to become important in the future.

Citation Format

  • Linder N, Denecke T, Busse H. Body composition analysis by radiological imaging – methods, applications, and prospects. Fortschr Röntgenstr 2024; DOI: 10.1055/a-2263-1501

Keywords

CT - MR-imaging - physiological studies - treatment effects - CT-quantitative - metabolic Disorders


Publication History

Received: 24 June 2023

Accepted after revision: 24 December 2023

Article published online:
03 April 2024

© 2024. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Borga M, West J, Bell JD. et al. Advanced body composition assessment: from body mass index to body composition profiling. Journal of Investigative Medicine 2018; 66: 1-9
    CrossrefPubMedGoogle Scholar
  • 2 Borga M. MRI adipose tissue and muscle composition analysis-a review of automation techniques. Br J Radiol 2018; 91: 20180252 DOI: 10.1259/bjr.20180252.
    CrossrefPubMedGoogle Scholar
  • 3 Guglielmi G, Ponti F, Agostini M. et al. The role of DXA in sarcopenia. Aging Clin Exp Res 2016; 28: 1047-1060
    CrossrefPubMedGoogle Scholar
  • 4 Tosato M, Marzetti E, Cesari M. et al. Measurement of muscle mass in sarcopenia: from imaging to biochemical markers. Aging Clin Exp Res 2017; 29: 19-27
    CrossrefPubMedGoogle Scholar
  • 5 Albano D, Messina C, Vitale J. et al. Imaging of sarcopenia: old evidence and new insights. Eur Radiol 2020; 30: 2199-2208
    CrossrefPubMedGoogle Scholar
  • 6 Ponti F, De Cinque A, Fazio N. et al. Ultrasound imaging, a stethoscope for body composition assessment. Quant Imaging Med Surg 2020; 10: 1699-1722
    CrossrefPubMedGoogle Scholar
  • 7 Hemke R, Buckless C, Torriani M. Quantitative Imaging of Body Composition. Semin Musculoskelet Radiol 2020; 24: 375-385
    Article in Thieme ConnectPubMedGoogle Scholar
  • 8 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: 1687-1698
    CrossrefPubMedGoogle Scholar
  • 9 Chianca V, Albano D, Messina C. et al. Sarcopenia: imaging assessment and clinical application. Abdom Radiol (NY) 2021; DOI: 10.1007/s00261-021-03294-3.
    CrossrefPubMedGoogle Scholar
  • 10 Ng M, Fleming T, Robinson M. et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 2014; 384: 766-781
    CrossrefPubMedGoogle Scholar
  • 11 Chooi YC, Ding C, Magkos F. The epidemiology of obesity. Metabolism 2019; 92: 6-10
    CrossrefPubMedGoogle Scholar
  • 12 Thomas EL, Saeed N, Hajnal JV. et al. Magnetic resonance imaging of total body fat. Journal of Applied Physiology 1998; 85: 1778-1785
    CrossrefPubMedGoogle Scholar
  • 13 Wu Z, Gao Z, Qiao Y. et al. Long-Term Results of Bariatric Surgery in Adolescents with at Least 5 Years of Follow-up: a Systematic Review and Meta-Analysis. Obes Surg 2023; 33: 1730-1745
    CrossrefPubMedGoogle Scholar
  • 14 Heymsfield SB. Development of imaging methods to assess adiposity and metabolism. Int J Obes (Lond) 2008; 32 (Suppl. 07) S76-S82
    CrossrefPubMedGoogle Scholar
  • 15 Heymsfield SB, Hu HH, Shen W. et al. Emerging Technologies and their Applications in Lipid Compartment Measurement. Trends Endocrinol Metab 2015; 26: 688-698
    CrossrefPubMedGoogle Scholar
  • 16 Hu HH, Chen J, Shen W. Segmentation and quantification of adipose tissue by magnetic resonance imaging. MAGMA 2016; 29: 259-276
    CrossrefPubMedGoogle Scholar
  • 17 Bray GA. Beyond BMI. Nutrients 2023; 15: 2254 DOI: 10.3390/nu15102254.
    CrossrefPubMedGoogle Scholar
  • 18 Whitcher B, Thanaj M, Cule M. et al. Precision MRI phenotyping enables detection of small changes in body composition for longitudinal cohorts. Sci Rep 2022; 12: 3748 DOI: 10.1038/s41598-022-07556-y.
    CrossrefPubMedGoogle Scholar
  • 19 Schwenzer NF, Machann J, Schraml C. et al. Quantitative analysis of adipose tissue in single transverse slices for estimation of volumes of relevant fat tissue compartments: a study in a large cohort of subjects at risk for type 2 diabetes by MRI with comparison to anthropometric data. Investigative radiology 2010; 45: 788-794
    CrossrefPubMedGoogle Scholar
  • 20 Springer F, Ehehalt S, Sommer J. et al. Predicting volumes of metabolically important whole-body adipose tissue compartments in overweight and obese adolescents by different MRI approaches and anthropometry. European Journal of Radiology 2012; 81: 1488-1494
    CrossrefPubMedGoogle Scholar
  • 21 Schaudinn A, Linder N, Garnov N. et al. Predictive accuracy of single- and multi-slice MRI for the estimation of total visceral adipose tissue in overweight to severely obese patients: MRI prediction of visceral fat volumes. NMR in Biomedicine 2015; 28: 583-590
    CrossrefPubMedGoogle Scholar
  • 22 Linder N, Schaudinn A, Garnov N. et al. Age and gender specific estimation of visceral adipose tissue amounts from radiological images in morbidly obese patients. Scientific Reports 2016; 6: 22261 DOI: 10.1038/srep22261.
    CrossrefPubMedGoogle Scholar
  • 23 Fearon K, Strasser F, Anker SD. et al. Definition and classification of cancer cachexia: an international consensus. Lancet Oncol 2011; 12: 489-495
    CrossrefPubMedGoogle Scholar
  • 24 Zopfs D, Theurich S, Große HokampN. et al. Single-slice CT measurements allow for accurate assessment of sarcopenia and body composition. Eur Radiol 2020; 30: 1701-1708
    CrossrefPubMedGoogle Scholar
  • 25 Sizoo D, de Heide LJM, Emous M. et al. Measuring Muscle Mass and Strength in Obesity: a Review of Various Methods. Obes Surg 2021; 31: 384-393
    CrossrefPubMedGoogle Scholar
  • 26 Lacoste JeansonA, Santos F, Villa C. et al. Architecture of the femoral and tibial diaphyses in relation to body mass and composition: Research from whole-body CT scans of adult humans. Am J Phys Anthropol 2018; 167: 813-826
    CrossrefPubMedGoogle Scholar
  • 27 Sollmann N, Löffler MT, Kronthaler S. et al. MRI-Based Quantitative Osteoporosis Imaging at the Spine and Femur. J Magn Reson Imaging 2021; 54: 12-35
    CrossrefPubMedGoogle Scholar
  • 28 Low G, Ferguson C, Locas S. et al. Multiparametric MR assessment of liver fat, iron, and fibrosis: a concise overview of the liver “Triple Screen”. Abdom Radiol (NY) 2023; 48: 2060-2073
    CrossrefPubMedGoogle Scholar
  • 29 Hayashi D, Roemer FW, Tol JL. et al. Emerging Quantitative Imaging Techniques in Sports Medicine. Radiology 2023; 308: e221531 DOI: 10.1148/radiol.221531.
    CrossrefPubMedGoogle Scholar
  • 30 Yin M, Ehman RL. MR Elastography: Practical Questions, From the Am J Roentgenol Special Series on Imaging of Fibrosis. Am J Roentgenol 2023; DOI: 10.2214/Am J Roentgenol.23.29437.
    CrossrefPubMedGoogle Scholar
  • 31 Barr RG, Wilson SR, Rubens D. et al. Update to the Society of Radiologists in Ultrasound Liver Elastography Consensus Statement. Radiology 2020; 296: 263-274
    CrossrefPubMedGoogle Scholar
  • 32 Herrmann J, Krstin N, Schoennagel BP. et al. Age-related distribution of vertebral bone-marrow diffusivity. Eur J Radiol 2012; 81: 4046-4049
    CrossrefPubMedGoogle Scholar
  • 33 Raya JG, Duarte A, Wang N. et al. Applications of Diffusion-Weighted MRI to the Musculoskeletal System. J Magn Reson Imaging 2023; DOI: 10.1002/jmri.2887034.
    CrossrefPubMedGoogle Scholar
  • 34 JafariNasabian P, Inglis JE, Reilly W. et al. Aging human body: changes in bone, muscle and body fat with consequent changes in nutrient intake. Journal of Endocrinology 2017; 234: R37-R51
    CrossrefPubMedGoogle Scholar
  • 35 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: 319-329
    CrossrefPubMedGoogle Scholar
  • 36 Schorr M, Dichtel LE, Gerweck AV. et al. Sex differences in body composition and association with cardiometabolic risk. Biol Sex Differ 2018; 9: 28
    CrossrefPubMedGoogle Scholar
  • 37 Wulan SN, Westerterp KR, Plasqui G. Ethnic differences in body composition and the associated metabolic profile: a comparative study between Asians and Caucasians. Maturitas 2010; 65: 315-319
    CrossrefPubMedGoogle Scholar
  • 38 Nixon DW. Cancer, cancer cachexia, and diet: lessons from clinical research. Nutrition 1996; 12: S52-S56
    CrossrefPubMedGoogle Scholar
  • 39 Evans WJ, Campbell WW. Sarcopenia and age-related changes in body composition and functional capacity. J Nutr 1993; 123: 465-468
    CrossrefPubMedGoogle Scholar
  • 40 Han J, Harrison L, Patzelt L. et al. Imaging modalities for diagnosis and monitoring of cancer cachexia. EJNMMI Res 2021; 11: 94
    CrossrefPubMedGoogle Scholar
  • 41 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
    CrossrefPubMedGoogle Scholar
  • 42 Vogele D, Otto S, Sollmann N. et al. Sarcopenia – Definition, Radiological Diagnosis, Clinical Significance. Zeitschrift Fortschr Röntgenstr 2023; 195: 393-405 DOI: 10.1055/a-1990-0201.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 43 Mahajan A, Spracklen CN, Zhang W. et al. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Nat Genet 2022; 54: 560-572
    CrossrefPubMedGoogle Scholar
  • 44 Haselmann V, Schoenberg SO, Neumaier M. et al. Integrated diagnostics. Radiologie (Heidelb) 2022; 62: 11-16
    CrossrefPubMedGoogle Scholar
  • 45 Stange R, Linder N, Schaudinn A. et al. Dicomflex: A novel framework for efficient deployment of image analysis tools in radiological research. PLoS ONE 2018; 13: e0202974
    CrossrefPubMedGoogle Scholar
  • 46 Karlsson A, Rosander J, Romu T. et al. Automatic and quantitative assessment of regional muscle volume by multi-atlas segmentation using whole-body water-fat MRI. J Magn Reson Imaging 2015; 41: 1558-1569
    CrossrefPubMedGoogle Scholar
  • 47 Borga M, Thomas EL, Romu T. et al. Validation of a fast method for quantification of intra-abdominal and subcutaneous adipose tissue for large-scale human studies: Quantification of IAAT and ASAT. NMR in Biomedicine 2015; 28: 1747-1753
    CrossrefPubMedGoogle Scholar
  • 48 West J, Dahlqvist LeinhardO, Romu T. et al. Feasibility of MR-Based Body Composition Analysis in Large Scale Population Studies. PLoS One 2016; 11: e0163332
    CrossrefPubMedGoogle Scholar
  • 49 Schlett CL, Hendel T, Weckbach S. et al. Population-Based Imaging and Radiomics: Rationale and Perspective of the German National Cohort MRI Study. Zeitschrift Fortschr Röntgenstr 2016; 188: 652-661
    Article in Thieme ConnectPubMedGoogle Scholar
  • 50 Scherer J, Nolden M, Kleesiek J. et al. Joint Imaging Platform for Federated Clinical Data Analytics. JCO Clin Cancer Inform 2020; 4: 1027-1038
    CrossrefPubMedGoogle Scholar
  • 51 Salg GA, Ganten MK, Bucher AM. et al. A reporting and analysis framework for structured evaluation of COVID-19 clinical and imaging data. NPJ Digit Med 2021; 4: 69
    CrossrefPubMedGoogle Scholar
  • 52 Starekova J, Hernando D, Pickhardt PJ. et al. Quantification of Liver Fat Content with CT and MRI: State of the Art. Radiology 2021; 301: 250-262
    CrossrefPubMedGoogle Scholar
  • 53 Pickhardt PJ, Graffy PM, Perez AA. et al. Opportunistic Screening at Abdominal CT: Use of Automated Body Composition Biomarkers for Added Cardiometabolic Value. Radiographics 2021; 41: 524-542
    CrossrefPubMedGoogle Scholar
  • 54 Nowak S, Theis M, Wichtmann BD. et al. End-to-end automated body composition analyses with integrated quality control for opportunistic assessment of sarcopenia in CT. Eur Radiol 2022; 32: 3142-3151
    CrossrefPubMedGoogle Scholar
  • 55 Flint A, Andersen G, Hockings P. et al. Randomised clinical trial: semaglutide versus placebo reduced liver steatosis but not liver stiffness in subjects with non‐alcoholic fatty liver disease assessed by magnetic resonance imaging. Aliment Pharmacol Ther 2021; 54: 1150-1161
    CrossrefPubMedGoogle Scholar
  • 56 Alkhouri N, Herring R, Kabler H. et al. Safety and efficacy of combination therapy with semaglutide, cilofexor and firsocostat in patients with non-alcoholic steatohepatitis: A randomised, open-label phase II trial. Journal of Hepatology 2022; 77: 607-618
    CrossrefPubMedGoogle Scholar
  • 57 Baum T, Cordes C, Dieckmeyer M. et al. MR-based assessment of body fat distribution and characteristics. European Journal of Radiology 2016; 85: 1512-1518
    CrossrefPubMedGoogle Scholar
  • 58 Lee SB, Cho YJ, Yoon SH. et al. Automated segmentation of whole-body CT images for body composition analysis in pediatric patients using a deep neural network. Eur Radiol 2022; 32: 8463-8472 DOI: 10.1007/s00330-022-08829-w.
    CrossrefPubMedGoogle Scholar
  • 59 Tamaki N, Kotani T, Nishimura M. et al. Dynamic whole-body FDG-PET imaging for oncology studies. Clin Transl Imaging 2022; 10: 249-258
    CrossrefPubMedGoogle Scholar
  • 60 Schütz UHW, Billich C, König K. et al. Characteristics, changes and influence of body composition during a 4486 km transcontinental ultramarathon: results from the TransEurope FootRace mobile whole body MRI-project. BMC Med 2013; 11: 122
    CrossrefPubMedGoogle Scholar
  • 61 Barbalho ER, Rocha IMG da, Medeiros GOC de. et al. Agreement between software programmes of body composition analyses on abdominal computed tomography scans of obese adults. Arch Endocrinol Metab 2020; 64: 24-29
    PubMedGoogle Scholar
  • 62 Borrelli P, Kaboteh R, Enqvist O. et al. Artificial intelligence-aided CT segmentation for body composition analysis: a validation study. Eur Radiol Exp 2021; 5: 11
    CrossrefPubMedGoogle Scholar
  • 63 Borga M, Ahlgren A, Romu T. et al. Reproducibility and repeatability of MRI-based body composition analysis. Magn Reson Med 2020; 84: 3146-3156
    CrossrefPubMedGoogle Scholar
  • 64 Lee K, Shin Y, Huh J. et al. Recent Issues on Body Composition Imaging for Sarcopenia Evaluation. Korean J Radiol 2019; 20: 205-217
    CrossrefPubMedGoogle Scholar
  • 65 Tagliafico AS, Bignotti B, Torri L. et al. Sarcopenia: how to measure, when and why. Radiol Med 2022; 127: 228-237
    CrossrefPubMedGoogle Scholar
  • 66 Surov A, Strobel A, Borggrefe J. et al. Low skeletal muscle mass predicts treatment response in oncology: a meta-analysis. Eur Radiol 2023; 33: 6426-6437
    CrossrefPubMedGoogle Scholar
  • 67 Lee MH, Zea R, Garrett JW. et al. Abdominal CT Body Composition Thresholds Using Automated AI Tools for Predicting 10-year Adverse Outcomes. Radiology 2023; 306: e220574
    CrossrefPubMedGoogle Scholar
  • 68 Mateos-Valenzuela AG, González-Macías ME, Ahumada-Valdez S. et al. Risk factors and association of body composition components for lumbar disc herniation in Northwest, Mexico. Sci Rep 2020; 10: 18479
    CrossrefPubMedGoogle Scholar
  • 69 Higgins MI, Martini DJ, Patil DH. et al. Quantification of body composition in renal cell carcinoma patients: Comparing computed tomography and magnetic resonance imaging measurements. Eur J Radiol 2020; 132: 109307
    CrossrefPubMedGoogle Scholar
  • 70 Ying P, Jin W, Wu X. et al. Association between CT-Quantified Body Composition and Recurrence, Survival in Nonmetastasis Colorectal Cancer Patients Underwent Regular Chemotherapy after Surgery. Biomed Res Int 2021; 2021: 6657566
    CrossrefPubMedGoogle Scholar
  • 71 Bamba S, Inatomi O, Takahashi K. et al. Assessment of Body Composition From CT Images at the Level of the Third Lumbar Vertebra in Inflammatory Bowel Disease. Inflamm Bowel Dis 2021; 27: 1435-1442
    CrossrefPubMedGoogle Scholar
  • 72 Chandarana H, Pisuchpen N, Krieger R. et al. Association of body composition parameters measured on CT with risk of hospitalization in patients with Covid-19. Eur J Radiol 2021; 145: 110031
    CrossrefPubMedGoogle Scholar
  • 73 Muresan BT, Sánchez JuanC, Artero A. et al. Measurement of body composition in cancer patients using CT planning scan at the third lumbar vertebra. Nutr Hosp 2019; 36: 1307-1314
    PubMedGoogle Scholar
  • 74 Dodds RM, Granic A, Robinson SM. et al. Sarcopenia, long-term conditions, and multimorbidity: findings from UK Biobank participants. J Cachexia Sarcopenia Muscle 2020; 11: 62-68
    CrossrefPubMedGoogle Scholar
  • 75 Yokoo T, Serai SD, Pirasteh A. et al. Linearity, Bias, and Precision of Hepatic Proton Density Fat Fraction Measurements by Using MR Imaging: A Meta-Analysis. Radiology 2018; 286: 486-498
    CrossrefPubMedGoogle Scholar
  • 76 Pickhardt PJ. Value-added Opportunistic CT Screening: State of the Art. Radiology 2022; 303: 241-254
    CrossrefPubMedGoogle Scholar
  • 77 Pickhardt PJ, Summers RM, Garrett JW. et al. Opportunistic Screening: Radiology Scientific Expert Panel. Radiology 2023; 307: e222044
    CrossrefPubMedGoogle Scholar
  • 78 Stoker J, van Randen A, Laméris W. et al. Imaging patients with acute abdominal pain. Radiology 2009; 253: 31-46
    CrossrefPubMedGoogle Scholar
  • 79 van Randen A, Laméris W, van Es HW. et al. A comparison of the accuracy of ultrasound and computed tomography in common diagnoses causing acute abdominal pain. Eur Radiol 2011; 21: 1535-1545
    CrossrefPubMedGoogle Scholar
  • 80 Lau HT, Liu W, Lam V. et al. Early routine (erCT) versus selective computed tomography (sCT) for acute abdominal pain: A systematic review and meta-analysis of randomised trials. Int J Surg 2022; 101: 106622
    CrossrefPubMedGoogle Scholar