Share：
Share this content in WeChat
X
[Chinese] [PDF] 20 1
• Special Focus •
Editorial on precise body composition imaging evaluation and clinical application
CHENG Xiaoguang  YAN Dong  WANG Ling  ZHAO Jian 

Cite this article as: CHENG X G, YAN D, WANG L, et al. Editorial on precise body composition imaging evaluation and clinical application[J]. Chin J Magn Reson Imaging, 2026, 17(9): 1-4, 15. DOI:10.12015/issn.1674-8034.2026.09.001.


[Abstract] Abnormal body composition, including skeletal muscle attenuation, visceral fat accumulation, intramuscular fat infiltration, and bone mass decline, is closely associated with metabolic diseases, adverse tumor prognosis, frailty in the elderly, osteoarticular injuries, and osteoporotic fractures. BMI, waist circumference, and bioelectrical impedance analysis can only provide a rough assessment of body shape but fail to differentiate fat distribution, muscle mass, and bone microstructure, making them inadequate for precise diagnosis and treatment. Imaging techniques centered on dual-energy X-ray absorptiometry, quantitative computed tomography, conventional CT body composition analysis, and magnetic resonance water-fat separation imaging enable layered quantitative evaluation of bones, skeletal muscles, subcutaneous, and visceral fat, serving as the mainstream approach for precise body composition assessment today. With advancements in artificial intelligence and opportunistic imaging analysis, body composition evaluation has transitioned from a research tool to routine clinical application. This article systematically reviews the principles, advantages and disadvantages, quantitative indicators, and multidisciplinary clinical value of various body composition imaging techniques, summarizes current technical limitations, and explores the prospects for standardized system construction and intelligent applications in China, providing a reference for promoting precision medicine in body composition.
[Keywords] body composition；sarcopenia；visceral fat；Dual-energy X-ray absorptiometry；computed tomography；magnetic resonance imaging；precision medicine

CHENG Xiaoguang1, 2*   YAN Dong1   WANG Ling1   ZHAO Jian2  

1 Department of Radiology, Beijing Jishuitan Hospital, Capital Medical University, Beijing 100035, China

2 Department of Medical Imaging, The Third Hospital of Hebei Medical University, Shijiazhuang 050051, China

Corresponding author: CHENG X G, E-mail: xiao65@263.net

Conflicts of interest   None.

Received  2026-07-30
Accepted  2026-09-14
DOI: 10.12015/issn.1674-8034.2026.09.001
Cite this article as: CHENG X G, YAN D, WANG L, et al. Editorial on precise body composition imaging evaluation and clinical application[J]. Chin J Magn Reson Imaging, 2026, 17(9): 1-4, 15. DOI:10.12015/issn.1674-8034.2026.09.001.

[1]
WANG Z, PIERSON R, HEYMSFIELD S. The five-level model: a new approach to organizing body-composition research[J]. Am J Clin Nutr, 1992, 56(1): 19-28. DOI: 10.1093/ajcn/56.1.19.
[2]
National Health and Family Planning Commission of the People's Republic of China. Criteria of weight for adults: WS/T 428—2013[S]. Beijing: China Standards Press, 2013.
[3]
General Office of the National Health Commission of the People's Republic of China. Guidelines for Diagnosis and Treatment of Obesity (2024 Edition)[R/OL]. (2024-10-12) [2026-07-29]. https://www.nhc.gov.cn/yzygj/c100068/202410/18966b78087d44429f934a2ef028b027/files/1732873189749_61795.pdf.
[4]
Chinese Society of Osteoporosis and Bone Mineral Research of Chinese Medical Association. Guidelines for the Diagnosis and Treatment of Primary Osteoporosis (2022)[J]. Chin J Osteoporos Bone Miner Res, 2022, 15 (6): 573-611. DOI: 10.3969/j.issn.1674-2591.2022.06.001.
[5]
National Center for Orthopaedics (Beijing Jishuitan Hospital), Radiological Imaging Equipment Branch of China Medical Equipment Association, Musculoskeletal Radiology Group of Chinese Society of Radiology of Chinese Medical Association, et al. Expert consensus on the equipment quality control and clinical application of bone densitometry[J]. Chin J Radiol, 2024, 58(4): 381-387. DOI: 10.3760/cma.j.cn112149-20231025-00317.
[6]
SHEPHERD J A, NG B K, SOMMER M J, et al. Body composition by DXA[J]. Bone, 2017, 104: 101-105. DOI: 10.1016/j.bone.2017.06.010.
[7]
BAZZOCCHI A, PONTI F, ALBISINNI U, et al. DXA: Technical aspects and application[J]. Eur J Radiol, 2016, 85(8): 1481-1492. DOI: 10.1016/j.ejrad.2016.04.004.
[8]
CARDENAS C E, YANG J Z, ANDERSON B M, et al. Advances in auto-segmentation[J]. Semin Radiat Oncol, 2019, 29(3): 185-197. DOI: 10.1016/j.semradonc.2019.02.001.
[9]
CHANG Y, YOON S H, KWON R, et al. Automated comprehensive CT assessment of the risk of diabetes and associated cardiometabolic conditions[J/OL]. Radiology, 2024, 312(2): e233410 [2026-07-29]. https://pubmed.ncbi.nlm.nih.gov/39105639/. DOI: 10.1148/radiol.233410.
[10]
CHENG X, ZHANG Y, WANG C, et al. The optimal anatomic site for a single slice to estimate the total volume of visceral adipose tissue by using the quantitative computed tomography (QCT) in Chinese population[J]. Eur J Clin Nutr, 2018, 72(11): 1567-1575. DOI: 10.1038/s41430-018-0122-1.
[11]
XU L, DUANMU Y Y, BLAKE G M, et al. Validation of goose liver fat measurement by QCT and CSE-MRI with biochemical extraction and pathology as reference[J]. Eur Radiol, 2018, 28(5): 2003-2012. DOI: 10.1007/s00330-017-5189-x.
[12]
ZHANG X P, CHENG X G, CHENG J L, et al. Quantitative analysis of hepatic steatosis in goose using quantitative CT[J]. Chin J Radiol, 2021, 55(4): 431-435. DOI: 10.3760/cma.j.cn112149-20201228-01342.
[13]
PICKHARDT P J, LUBNER M G. Noninvasive quantitative CT for diffuse liver diseases: steatosis, iron overload, and fibrosis[J/OL]. Radiographics, 2025, 45(1): e240176 [2026-07-29]. https://pubmed.ncbi.nlm.nih.gov/39700040/. DOI: 10.1148/rg.240176.
[14]
GUO Z, ZHOU F Y, YUAN Y, et al. An evaluation of dual-layer detector spectral CT for the estimation of MRI proton density fat fraction of the liver[J/OL]. Quant Imaging Med Surg, 2026, 16(6): 448 [2026-07-29]. https://pubmed.ncbi.nlm.nih.gov/42273094/. DOI: 10.21037/qims-2025-1-2600.
[15]
XU X X, YUAN J, CAI X X, et al. Robust liver steatosis quantification using photon-counting CT in comparison to PDFF: validation across protocols and cohorts[J]. Acad Radiol, 2026 [2026-07-29]. https://pubmed.ncbi.nlm.nih.gov/42705927/. DOI: 10.1016/j.acra.2026.08.094.
[16]
STAREKOVA J, HERNANDO D, PICKHARDT P J, et al. Quantification of liver fat content with CT and MRI: state of the art[J]. Radiology, 2021, 301(2): 250-262. DOI: 10.1148/radiol.2021204288.
[17]
FERRAIOLI G, SOARES MONTEIRO L B. Ultrasound-based techniques for the diagnosis of liver steatosis[J]. World J Gastroenterol, 2019, 25(40): 6053-6062. DOI: 10.3748/wjg.v25.i40.6053.
[18]
ATZORI S, PASHA Y, MAURICE J B, et al. The accuracy of ultrasound controlled attenuation parameter in diagnosing hepatic fat content[J]. Hepatic Med Evid Res, 2023, 15: 51-61. DOI: 10.2147/HMER.S411619.
[19]
WIJNTJES J, VAN ALFEN N. Muscle ultrasound: Present state and future opportunities[J]. Muscle Nerve, 2021, 63(4): 455-466. DOI: 10.1002/mus.27081.
[20]
NEELAND I J, ROSS R, DESPRÉS J P, et al. Visceral and ectopic fat, atherosclerosis, and cardiometabolic disease: a position statement[J]. Lancet Diabetes Endocrinol, 2019, 7(9): 715-725. DOI: 10.1016/S2213-8587(19)30084-1.
[21]
BUSETTO L, DICKER D, FRÜHBECK G, et al. A new framework for the diagnosis, staging and management of obesity in adults[J]. Nat Med, 2024, 30(9): 2395-2399. DOI: 10.1038/s41591-024-03095-3.
[22]
HOLMES C J, RACETTE S B. The utility of body composition assessment in nutrition and clinical practice: an overview of current methodology[J/OL]. Nutrients, 2021, 13(8): 2493 [2026-07-29]. https://pubmed.ncbi.nlm.nih.gov/34444653/. DOI: 10.3390/nu13082493.
[23]
Fatty Liver and Alcoholic Liver Disease Group of Chinese Society of Hepatology of Chinese Medical Association, Fatty Liver Expert Committee of Chinese Medical Doctor Association. Guidelines of prevention and treatment for nonalcoholic fatty liver disease: a 2018 update[J]. Chin J Hepatol, 2018, 26(3): 195-203. DOI: 10.3760/cma.j.issn.1007-3418.2018.03.008.
[24]
YOUNOSSI Z M, ZELBER-SAGI S, LAZARUS J V, et al. Global consensus recommendations for metabolic dysfunction-associated steatotic liver disease and steatohepatitis[J]. Gastroenterology, 2025, 169(5): 1017-1032. DOI: 10.1053/j.gastro.2025.02.044.
[25]
GUO X Y. Clinical application of ultrasound attenuation coefficient in quantitative detection of hepatic steatosis[D]. Dalian: Dalian Medical University, 2022. DOI: 10.26994/d.cnki.gdlyu.2022.000866.
[26]
GUO Z, BLAKE G M, LI K, et al. Liver fat content measurement with quantitative CT validated against MRI proton density fat fraction: a prospective study of 400 healthy volunteers[J]. Radiology, 2020, 294(1): 89-97. DOI: 10.1148/radiol.2019190467.
[27]
GUO Z, BLAKE G M, GRAFFY P M, et al. Hepatic steatosis: CT-based prevalence in adults in China and the United States and associations with age, sex, and body mass index[J]. AJR Am J Roentgenol, 2022, 218(5): 846-857. DOI: 10.2214/AJR.21.26728.
[28]
Chinese Society of Health Management of Chinese Medical Association, National Center for Orthopaedics (Beijing Jishuitan Hospital, Capital Medical University), National Quality Control Center for Radiological Imaging, et al. Guidelines for quantitative CT in health management (2024)[J]. Chin J Health Manag, 2024, 18(9): 645-654. DOI: 10.3760/cma.j.cn115624-20240607-00474.
[29]
LEI L B, WANG B, ZHAO L S, et al. Berberine and adiposity in diabetes-free individuals with obesity and MASLD: A randomized clinical trial[J/OL]. JAMA Netw Open, 2026, 9(1): e2554152 [2026-07-29]. https://pubmed.ncbi.nlm.nih.gov/41543854/. DOI: 10.1001/jamanetworkopen.2025.54152.
[30]
MA C J, ZHANG Q H, HUANG Y P, et al. Value of body composition analysis based on chest CT combined with amide proton transfer weighted imaging in predicting functional prognosis of patients with acute ischemic stroke[J/OL]. Chin J Magn Reson Imaging, (2026-06-26) [2026-07-29]. https://link.cnki.net/urlid/11.5902.R.20260626.0935.012.
[31]
MIAO M, HAN X T, GAO L, et al. Effect of fat deposition on plasma atherosclerosis index in postmenopausal women[J/OL]. Chin J Magn Reson Imaging, 2026, (2025-12-19) [2026-07-29]. https://link.cnki.net/urlid/11.5902.R.20251218.1634.002.
[32]
BATES D D B, PICKHARDT P J. CT-derived body composition assessment as a prognostic tool in oncologic patients: from opportunistic research to artificial intelligence-based clinical implementation[J]. AJR Am J Roentgenol, 2022, 219(4): 671-680. DOI: 10.2214/AJR.22.27749.
[33]
WANG S Q, ZHANG Q H, WANG X L, et al. The value of predicting nerve invasion based on chest CT body composition index combined with multi-parameter MRI of prostate cancer focus and fat around prostate[J/OL]. Chin J Magn Reson Imaging, 2026, (2026-04-01) [2026-07-29]. https://link.cnki.net/urlid/11.5902.R.20260330.1801.004.
[34]
CHEN A L, ZHANG Q H, LI W H, et al. Value of preoperative prediction of budding grade of T3 ~ T4 rectal cancer based on abdominal CT body composition analysis combined with diffusion kurtosis imaging[J/OL]. Chin J Magn Reson Imaging, 2026, (2026-04-01) [2026-07-29]. https://link.cnki.net/urlid/11.5902.R.20260331.0844.002.

PREV Research progress on AI-based multimodal MRI in the diagnosis and treatment decision-making of cervical spondylotic myelopathy
NEXT The value of chest CT-derived body composition analysis combined with amide proton transfer-weighted imaging in predicting functional outcomes in patients with acute ischemic stroke
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn