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Clinical application progress of imaging-based body composition analysis
ZHANG Qinhe  LI Wenhao  LIU Ailian 

Cite this article as: ZHANG Q H, LI W H, LIU A L. Clinical application progress of imaging-based body composition analysis[J]. Chin J Magn Reson Imaging, 2026, 17(9): 224-234. DOI:10.12015/issn.1674-8034.2026.09.029.


[Abstract] Body composition analysis (BCA) quantifies the distribution and function of human tissues such as fat, muscle, and bone using non-invasive imaging techniques, providing a new perspective for research in metabolic diseases, chronic diseases, geriatrics, and oncology. It can be used to assess obesity, sarcopenia, and osteoporosis, and to monitor nutritional status, disease severity, and the effectiveness of interventions. Multimodal imaging-based BCA reveals the complex interactions between tumors and the host. BCA is closely related to patients' overall nutritional status, inflammatory response, and immune function, and has gradually become an important tool in the management of cancer patients. Through interdisciplinary collaboration, the combination of nutrition, sports medicine, and oncology provides patients with more precise and personalized treatment plans. With the development of AI technology, deep learning algorithms can automatically identify, segment, and quantify muscle and adipose tissue, improving the efficiency and accuracy of BCA and enhancing its feasibility for clinical application. However, the field still faces challenges such as inconsistent measurement standards, insufficient prospective validation, poor generalization ability of AI models, and difficulties in clinical integration. Therefore, this article reviews the progress of BCA imaging technology and its applications in metabolic syndrome, tumors, acute inflammation, geriatrics, chronic disease risk assessment, treatment response and tolerability, and prognostic prediction. It systematically analyzes the limitations of current research and proposes future directions such as establishing a standardized system, conducting multi-center prospective studies, promoting the clinical application of interpretable AI, and integrating multi-omics data, aiming to provide a reference for the clinical translation and standardized application of BCA.
[Keywords] body composition；sarcopenia；obesity；CT；magnetic resonance imaging；radiomics, machine learning, deep learning

ZHANG Qinhe1, 2   LI Wenhao1   LIU Ailian1, 2*  

1 Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian 116011, China

2 Technology Innovation Center of Hyperpolarized MRI, Liaoning Province, Dalian 116011, China

Corresponding author: LIU A L, E-mail: cjr.liuailian@vip.163.com

Conflicts of interest   None.

Received  2026-03-30
Accepted  2026-06-09
DOI: 10.12015/issn.1674-8034.2026.09.029
Cite this article as: ZHANG Q H, LI W H, LIU A L. Clinical application progress of imaging-based body composition analysis[J]. Chin J Magn Reson Imaging, 2026, 17(9): 224-234. DOI:10.12015/issn.1674-8034.2026.09.029.

[1]
DI VINCENZO O, PICIOCCHI C, MUZZIOLI L, et al. Osteosarcopenic obesity: a systematic review and a call for consensus on definitions and diagnostic criteria[J/Ol]. Clin Nutr, 2025, 51: 146-160 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/40570669/. DOI: 10.1016/j.clnu.2025.06.009.
[2]
2021 ADULT BMI COLLABORATORS GBD. Global, regional, and national prevalence of adult overweight and obesity, 1990-2021, with forecasts to 2050: a forecasting study for the Global Burden of Disease Study 2021[J]. Lancet, 2025, 405(10481): 813-838. DOI: 10.1016/S0140-6736(25)00355-1.
[3]
GAZZOTTI S, SASSI R, APARISI GÓMEZ M P, et al. Imaging of body composition[J]. Semin Musculoskelet Radiol, 2024, 28(5): 594-609. DOI: 10.1055/s-0044-1788887.
[4]
WANG D Y, MORTON J I, SALIM A, et al. Comparison of DXA, BIA, and anthropometry for assessing subcutaneous, visceral, liver, and pancreas fat measured by MRI[J]. Diabetes Obes Metab, 2026, 28(4): 2795-2805. DOI: 10.1111/dom.70456.
[5]
ZHANG Q H, XIE L H, ZHANG H N, et al. Magnetic resonance imaging assessment of abdominal ectopic fat deposition in correlation with cardiometabolic risk factors[J/OL]. Front Endocrinol, 2022, 13: 820023 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/35432188/. DOI: 10.3389/fendo.2022.820023.
[6]
SALEHIN M, YANG CHOW V T, LEE H, et al. Validation of automated computed tomography segmentation software to assess body composition among cancer patients[J/OL]. Clin Nutr ESPEN, 2025, 69: 686-695 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/40865606/. DOI: 10.1016/j.clnesp.2025.08.024.
[7]
SUPE L, RIZZO S. The correlation of computed tomography (CT)-based body composition and survival in pancreatic cancer patients: a systematic review[J/OL]. Tomography, 2026, 12(1): 8 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/41591140/. DOI: 10.3390/tomography12010008.
[8]
CAO X R, SHEN G L, SUN T. Association between body composition and risk of type 2 diabetes mellitus: a systematic review and meta-analysis[J/OL]. Diabetes, Metabolic Syndrome and Obesity, 2026, 19: 598731 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/42038528/. DOI: 10.2147/DMSO.S598731.
[9]
MAURYA A K, AGGARWAL L M, CHOUDHARY S. Body composition analysis techniques and its application in oncology: a review[J]. Nutr Cancer, 2024, 76(8): 666-675. DOI: 10.1080/01635581.2024.2353942.
[10]
SON J W, HAN B D, BENNETT J P, et al. Development and clinical application of bioelectrical impedance analysis method for body composition assessment[J/OL]. Obes Rev, 2025, 26: e13844 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/39350475/. DOI: 10.1111/obr.13844.
[11]
ABDULSALAM A J, KARA M, ÖZÇAKAR L. Ultrasound in body composition analysis: Au courant[J]. Rofo, 2025, 197(3): 308-309. DOI: 10.1055/a-2328-6110.
[12]
Chinese Medical Association Health Management Branch, National Center for Orthopaedics (Beijing Jishuitan Hospital Capital Medical University), National Quality Control Center for Radiographic 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.
[13]
ZAFFINA C, WYTTENBACH R, PAGNAMENTA A, et al. Body composition assessment: comparison of quantitative values between magnetic resonance imaging and computed tomography[J]. Quant Imaging Med Surg, 2022, 12(2): 1450-1466. DOI: 10.21037/qims-21-619.
[14]
TAKAHASHI M, TAKENAGA T, NOMURA Y, et al. Automated volume measurement of abdominal adipose tissue from entire abdominal cavity in Dixon MR images using deep learning[J]. Radiol Phys Technol, 2023, 16(1): 28-38. DOI: 10.1007/s12194-022-00687-x.
[15]
THOMAS D M, CROFFORD I, SCUDDER J, et al. Updates on methods for body composition analysis: implications for clinical practice[J/OL]. Curr Obes Rep, 2025, 14(1): 8 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/39798028/. DOI: 10.1007/s13679-024-00593-w.
[16]
LYU C X, QU W N, WANG Z B, et al. An explainable AI workflow integrating automated volumetric body composition analysis for predicting pathological grading of gastroenteropancreatic neuroendocrine neoplasms: a multicenter cohort study[J/OL]. Int J Surg, 202 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/41609392/. DOI: 10.1097/JS9.0000000000004879.
[17]
VOGELE D, OTTO S, SOLLMANN N, et al. Sarcopenia–definition, radiological diagnosis, clinical significance[J]. Rofo, 2023, 195(5): 393-405. DOI: 10.1055/a-1990-0201.
[18]
KIM S, KIM T H, JEONG C W, et al. Development of quantification software for evaluating body composition contents and its clinical application in sarcopenic obesity[J/OL]. Sci Rep, 2020, 10(1): 10452 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/32591563/. DOI: 10.1038/s41598-020-67461-0.
[19]
HAUBOLD J, BALDINI G, PARMAR V, et al. BOA: a CT-based body and organ analysis for radiologists at the point of care[J]. Invest Radiol, 2024, 59(6): 433-441. DOI: 10.1097/rli.0000000000001040.
[20]
CHARRIÈRE K, RAGUSA A, GENOUX B, et al. ODIASP: an open-source software for automated SMI determination-application to an inpatient population[J/OL]. J Cachexia Sarcopenia Muscle, 2025, 16(4): e70023 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/40716112/. DOI: 10.1002/jcsm.70023.
[21]
GARRETT J W, PICKHARDT P J, SUMMERS R M. Methodology for a fully automated pipeline of AI-based body composition tools for abdominal CT[J]. Abdom Radiol, 2025, 50(11): 5567-5580. DOI: 10.1007/s00261-025-04951-7.
[22]
NOWAK S, THEIS M, WICHTMANN B D, et al. End-to-end automated body composition analyses with integrated quality control for opportunistic assessment of sarcopenia in CT[J]. Eur Radiol, 2022, 32(5): 3142-3151. DOI: 10.1007/s00330-021-08313-x.
[23]
HAUBOLD J, POLLOK O B, HOLTKAMP M, et al. Moving beyond CT body composition analysis: using style transfer for bringing CT-based fully-automated body composition analysis to T2-weighted MRI sequences[J]. Invest Radiol, 2025, 60(8): 552-559. DOI: 10.1097/RLI.0000000000001162.
[24]
SCHNEIDER D, EGGEBRECHT T, LINDER A, et al. Abdominal fat quantification using convolutional networks[J]. Eur Radiol, 2023, 33(12): 8957-8964. DOI: 10.1007/s00330-023-09865-w.
[25]
WEISMAN A J, HUFF D T, GOVINDAN R M, et al. Multi-organ segmentation of CT via convolutional neural network: impact of training setting and scanner manufacturer[J/OL]. Biomed Phys Eng Express, 2023, 9(6): 065021 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/37725928/. DOI: 10.1088/2057-1976/acfb06.
[26]
VAN DIJK D P J, et al. External validation of a deep learning model for automatic segmentation of skeletal muscle and adipose tissue on abdominal CT images[J]. Br J Radiol, 2024, 97(1164): 2015-2023. DOI: 10.1093/bjr/tqae191.
[27]
RIDLEY B J, HAMAMOTO Y, CORNELISSEN P L, et al. Perceptual body image tasks require ethnically appropriate stimuli[J/OL]. Body Image, 2025, 53: 101899 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/40344951/. DOI: 10.1016/j.bodyim.2025.101899.
[28]
PÉREZ-SANPABLO A I, QUINZAÑOS-FRESNEDO J, GUTIÉRREZ-MARTÍNEZ J, et al. Transforming medical imaging: the role of artificial intelligence integration in PACS for enhanced diagnostic accuracy and workflow efficiency[J/OL]. Curr Med Imaging Former Curr Med Imaging Rev, 2025, 21: e15734056370620 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/40265427/. DOI: 10.2174/0115734056370620250403030638.
[29]
NOWAK S, FARON A, LUETKENS J A, et al. Fully automated segmentation of connective tissue compartments for CT-based body composition analysis: a deep learning approach[J]. Invest Radiol, 2020, 55(6): 357-366. DOI: 10.1097/RLI.0000000000000647.
[30]
ZHANG L, SHEN B X, SHI H Z, et al. Assessment and application of skeletal muscle mass and quality based on chest CT[J]. Chin J Geriatr, 2025, 44(6): 830-834. DOI: 10.3760/cma.j.issn.0254-9026.2025.06.027.
[31]
BESUTTI G, PELLEGRINI M, OTTONE M, et al. The impact of chest CT body composition parameters on clinical outcomes in COVID-19 patients[J/OL]. PLoS One, 2021, 16(5): e0251768 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/33989341/. DOI: 10.1371/journal.pone.0251768.
[32]
LIU M C, CHENG Y Y, LIN S C, et al. Machine learning survival prediction in esophageal cancer using radiomics and body composition from pretreatment and follow-up T12-level computed tomography[J/OL]. World J Gastrointest Oncol, 2025, 17(12): 112873 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/41480227/. DOI: 10.4251/wjgo.v17.i12.112873.
[33]
XU X A, DENG H H, GATENO J, et al. Federated multi-organ segmentation with inconsistent labels[J]. IEEE Trans Med Imaging, 2023, 42(10): 2948-2960. DOI: 10.1109/TMI.2023.3270140.
[34]
MA M, LI T, PENG Y, et al. "Why Is There a Tumor ": Tell Me the Reason, Show Me the Evidence[J/OL]. Proc Mach Learn Res, 2025, 267: 41992-42008 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/42079391/.
[35]
KAWAI T, AUTIERI M V, SCALIA R. Adipose tissue inflammation and metabolic dysfunction in obesity[J/OL]. Am J Physiol Cell Physiol, 2021, 320(3): C375-C391 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/33356944/. DOI: 10.1152/ajpcell.00379.2020.
[36]
LIPINA C, HUNDAL H S. Lipid modulation of skeletal muscle mass and function[J]. J Cachexia Sarcopenia Muscle, 2017, 8(2): 190-201. DOI: 10.1002/jcsm.12144.
[37]
ROHM T V, MEIER D T, OLEFSKY J M, et al. Inflammation in obesity, diabetes, and related disorders[J]. Immunity, 2022, 55(1): 31-55. DOI: 10.1016/j.immuni.2021.12.013.
[38]
TAN Y T, XUE R, PAN Y W, et al. Cancer Cachexia: molecular basis and therapeutic advances[J/OL]. Sig Transduct Target Ther, 2026, 11: 16 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/41526343/. DOI: 10.1038/s41392-025-02331-7.
[39]
GOODPASTER B H, BERGMAN B C, BRENNAN A M, et al. Intermuscular adipose tissue in metabolic disease[J]. Nat Rev Endocrinol, 2023, 19(5): 285-298. DOI: 10.1038/s41574-022-00784-2.
[40]
AGARWAL K, MANOHARAN F, KAPOOR N. Understanding body composition analysis in obesity management[J]. J Assoc Physicians Ind, 2025, 73(4): 11-17. DOI: 10.59556/japi.73.0846.
[41]
KIM Y H, YOON J W, LEE B H, et al. Artificial intelligence-based body composition analysis using computed tomography images predicts both prevalence and incidence of diabetes mellitus[J]. J Diabetes Investig, 2025, 16(2): 272-284. DOI: 10.1111/jdi.14365.
[42]
AN Q, ZHANG Q H, WANG Y, et al. Association between type 2 diabetes mellitus and body composition based on MRI fat fraction mapping[J/OL]. Front Public Health, 2024, 12: 1332346 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/38322122/. DOI: 10.3389/fpubh.2024.1332346.
[43]
LEE M H, ZEA R, GARRETT J W, et al. AI-based abdominal CT measurements of orthotopic and ectopic fat predict mortality and cardiometabolic disease risk in adults[J]. Eur Radiol, 2025, 35(1): 520-531. DOI: 10.1007/s00330-024-10935-w.
[44]
XU K W, KHAN M S, LI T Z, et al. AI body composition in lung cancer screening: added value beyond lung cancer detection[J/OL]. Radiology, 2023, 308(1): e222937 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/37489991/. DOI: 10.1148/radiol.222937.
[45]
LANGENBACH I L, HADZIC I, ZELEZNIK R, et al. Association of epicardial adipose tissue changes on serial chest CT scans with mortality: insights from the national lung screening trial[J/OL]. Radiology, 2025, 314(2): e240473 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/39964263/. DOI: 10.1148/radiol.240473.
[46]
ZAMBRANO CHAVES J M, LENCHIK L, GALLEGOS I O, et al. Abdominal CT metrics in 17, 646 patients reveal associations between myopenia, myosteatosis, and medical phenotypes: a phenome-wide association study[J/OL]. eBioMedicine, 2024, 103: 105116 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/38636199/. DOI: 10.1016/j.ebiom.2024.105116.
[47]
MAHENGE C M, AKASHEH R T, KINDER B, et al. CT-scan-assessed body composition and its association with tumor protein expression in endometrial cancer: the role of muscle and adiposity quantities[J/OL]. Cancers, 2024, 16(24): 4222 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/39766121/. DOI: 10.3390/cancers16244222.
[48]
DENG T, LYON C J, BERGIN S, et al. Obesity, inflammation, and cancer[J/OL]. Annu Rev Pathol Mech Dis, 2016, 11: 421-449 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/27193454/. DOI: 10.1146/annurev-pathol-012615-044359.
[49]
QUAIL D F, DANNENBERG A J. The obese adipose tissue microenvironment in cancer development and progression[J]. Nat Rev Endocrinol, 2019, 15(3): 139-154. DOI: 10.1038/s41574-018-0126-x.
[50]
HE W Z, JIANG C, LIU L L, et al. Association of body composition with survival and inflammatory responses in patients with non-metastatic nasopharyngeal cancer[J/OL]. Oral Oncol, 2020, 108: 104771 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/32485608/. DOI: 10.1016/j.oraloncology.2020.104771.
[51]
DAVIS E W, ATTWOOD K, PRUNIER J, et al. The association of body composition phenotypes before chemotherapy with epithelial ovarian cancer mortality[J]. J Natl Cancer Inst, 2024, 116(9): 1513-1524. DOI: 10.1093/jnci/djae112.
[52]
AYDıN E, GÜVEL VERDI E, BALCI M F, et al. The relationship between imaging-based body composition parameters and disease prognosis in patients with endometrial cancer[J]. J Obstet Gynaecol Res, 2024, 50(9): 1566-1573. DOI: 10.1111/jog.16010.
[53]
TAO J L, FANG J Y, CHEN L H, et al. Increased adipose tissue is associated with improved overall survival, independent of skeletal muscle mass in non-small cell lung cancer[J]. J Cachexia Sarcopenia Muscle, 2023, 14(6): 2591-2601. DOI: 10.1002/jcsm.13333.
[54]
FILIS P, PAPAGIANNOPOULOS C K, MARKOZANNES G, et al. Associations of sarcopenia, sarcopenia components and sarcopenic obesity with cancer incidence: a prospective cohort study of 414, 094 participants in UK Biobank[J]. Int J Cancer, 2025, 157(7): 1316-1332. DOI: 10.1002/ijc.35480.
[55]
CARDOSO I C R, DE PAULA N S, CHAVES G V. Body composition phenotypes and long-term survival of endometrial cancer[J/OL]. Clin Nutr ESPEN, 2025, 68: 88-94 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/40311924/. DOI: 10.1016/j.clnesp.2025.04.010.
[56]
HAN J, LIU X Y, WANG J J, et al. Prognostic value of body composition in patients with digestive tract cancers: a prospective cohort study of 8, 267 adults from China[J/OL]. Clin Nutr ESPEN, 2024, 62: 192-198 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/38901941/. DOI: 10.1016/j.clnesp.2024.04.017.
[57]
WANG J, LEADER J K, MENG X, et al. Beyond nodules: body composition as a biomarker for future lung cancer[J]. Eur Radiol, 2025, 35(12): 7834-7844. DOI: 10.1007/s00330-025-11735-6.
[58]
LIU J Y, YU X F, HUANG X Q, et al. Associations of muscle and adipose tissue parameters with long-term outcomes in middle and low rectal cancer: a retrospective cohort study[J/OL]. Cancer Imaging, 2023, 23(1): 5 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/36635737/. DOI: 10.1186/s40644-022-00514-x.
[59]
CAO H L, WANG Y S, ZHANG D F, et al. Periprostatic adipose tissue: a new perspective for diagnosing and treating prostate cancer[J]. J Cancer, 2024, 15(1): 204-217. DOI: 10.7150/jca.89750.
[60]
DREWA J, LAZAR-JUSZCZAK K, ADAMOWICZ J, et al. Periprostatic adipose tissue as a contributor to prostate cancer pathogenesis: a narrative review[J/OL]. Cancers, 2025, 17(3): 372 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/39941741/. DOI: 10.3390/cancers17030372.
[61]
BEŞLER M S, ÖLÇÜCÜOĞLU E, ÖLÇÜCÜOĞLU E. The prognostic role of perirenal fat tissue in non-metastatic renal cell carcinoma[J]. Jpn J Radiol, 2024, 42(11): 1262-1269. DOI: 10.1007/s11604-024-01609-0.
[62]
SU W H, WU Y K, LIAO S F, et al. A nomogram including sarcopenia for predicting progression-free survival in patients with localized papillary renal cell carcinoma: a retrospective cohort study[J]. Ann Surg Oncol, 2024, 31(9): 5815-5826. DOI: 10.1245/s10434-024-15666-2.
[63]
ABBASS T, DOLAN R D, HORGAN P G, et al. CT derived measurement of body composition: observations from a comparative analysis of patients with colorectal and lung cancer[J]. Nutr Cancer, 2025, 77(1): 70-78. DOI: 10.1080/01635581.2024.2392913.
[64]
XU K J, WANG X B, ZHOU C S, et al. Synergic value of 3D CT-derived body composition and triglyceride glucose body mass for survival prognostic modeling in unresectable pancreatic cancer[J/OL]. Front Nutr, 2025, 12: 1499188 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/40177184/. DOI: 10.3389/fnut.2025.1499188.
[65]
KLATTE D C F, WESTON A, MA Y H, et al. Temporal trends in body composition and metabolic markers prior to diagnosis of pancreatic ductal adenocarcinoma[J/OL]. Clin Gastroenterol Hepatol, 2024, 22(9): 1830-1838.e9 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/38703880/. DOI: 10.1016/j.cgh.2024.03.038.
[66]
BIAN L J, WU D P, CHEN Y G, et al. Associations of radiological features of adipose tissues with postoperative complications and overall survival of gastric cancer patients[J]. Eur Radiol, 2022, 32(12): 8569-8578. DOI: 10.1007/s00330-022-08918-w.
[67]
SU S Y, WENG T L, BAI J Y, et al. CT-based body composition analysis to study the effect of visceral obesity on postoperative complications in ovarian cancer: implications for young patients[J/OL]. World J Surg Oncol, 2025, 23(1): 95 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/40114153/. DOI: 10.1186/s12957-025-03740-2.
[68]
ZHAI T S, ZHANG B C, QU Z N, et al. Elevated visceral obesity quantified by CT is associated with adverse postoperative outcome of laparoscopic radical nephrectomy for renal clear cell carcinoma patients[J]. Int Urol Nephrol, 2018, 50(5): 845-850. DOI: 10.1007/s11255-018-1858-1.
[69]
LI X X, ZHANG S T, HUANG X L, et al. Development of a CT-assessed adiposity nomogram for predicting outcome in localized ccRCC[J]. Abdom Radiol (NY), 2024, 49(10): 3485-3495. DOI: 10.1007/s00261-024-04403-8.
[70]
MÜLLER L, MÄHRINGER-KUNZ A, AUER T A, et al. AI-derived body composition parameters as prognostic factors in patients with HCC undergoing TACE in a multicenter study[J/OL]. JHEP Rep, 2024, 6(8): 101125 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/39139458/. DOI: 10.1016/j.jhepr.2024.101125.
[71]
FENG Y, YU B R, LIU A X, et al. Prognostic impact of body composition in hepatocellular carcinoma patients undergoing interventional and systemic therapy[J/OL]. Front Nutr, 2025, 12: 1586202 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/40308633/. DOI: 10.3389/fnut.2025.1586202.
[72]
MATSUI R, OHASHI M, RI M, et al. Oncological impact of the preoperative combined assessment of skeletal muscle mass for patients undergoing curative gastrectomy for gastric cancer[J]. Surg Today, 2025, 55(7): 875-885. DOI: 10.1007/s00595-024-02978-9.
[73]
KHALID S I, MASSAAD E, SHIN J H. Assessing the prognostic impact of body composition phenotypes on surgical outcomes and survival in patients with spinal metastasis: a deep learning approach to preoperative CT analysis[J]. J Neurosurg Spine, 2025, 42(3): 356-365. DOI: 10.3171/2024.8.SPINE24722.
[74]
HUANG Y L, CUN H X, MOU Z L, et al. Multiparameter body composition analysis on chest CT predicts clinical outcomes in resectable non-small cell lung cancer[J/OL]. Insights Imaging, 2025, 16(1): 32 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/39912982/. DOI: 10.1186/s13244-025-01910-0.
[75]
KOLCK J, HOSSE C, BEETZ N L, et al. Beyond body mass index: Body composition profiling for perioperative risk stratification in intrahepatic cholangiocarcinoma patients[J/OL]. Cancer Rep (Hoboken), 2024, 7(9): e2070 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/39324689/. DOI: 10.1002/cnr2.2070.
[76]
LASCALA F, SILVA MORAES B K DA, MENDES M C S, et al. Prognostic value of myosteatosis and systemic inflammation in patients with resectable gastric cancer: a retrospective study[J]. Eur J Clin Nutr, 2023, 77(1): 116-126. DOI: 10.1038/s41430-022-01201-7.
[77]
ZOU F, ZHAO J H, ZHONG L H, et al. CT-based body composition and inflammatory nutritional biomarker nomogram for predicting early postoperative recurrence of non-small cell lung cancer: a multicenter study[J]. J Thorac Dis, 2025, 17(10): 8046-8062. DOI: 10.21037/jtd-2025-1211.
[78]
WANG S, CHENG L, DOU L, et al. Geriatric nutritional risk index and body composition dictate the prognosis of elderly patients with intrahepatic cholangiocarcinoma[J/OL]. Front Nutr, 2025, 12: 1565317 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/40123935/. DOI: 10.3389/fnut.2025.1565317.
[79]
WANG M X, XIE X Z, LIN J C, et al. Preoperative blood and CT-image nutritional indicators in short-term outcomes and machine learning survival framework of intrahepatic cholangiocarcinoma[J/OL]. Eur J Surg Oncol, 2025, 51(6): 109654 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/40009922/. DOI: 10.1016/j.ejso.2025.109654.
[80]
WANG R, ZHU L Y, HAO J Q, et al. Computed tomography-based body composition and systemic inflammation for outcome prediction in patients with resectable esophageal squamous cell carcinoma[J]. J Thorac Dis, 2025, 17(4): 2441-2452. DOI: 10.21037/jtd-2025-508.
[81]
SONG A Y, HUANG Z H, XU J H, et al. Development and validation of a preoperative CT-based body composition nomogram for predicting recurrence-free survival after radical surgery in patients with gastric cancer[J]. J Gastrointest Oncol, 2025, 16(3): 875-889. DOI: 10.21037/jgo-24-838.
[82]
XU M Y, WANG L, SUN S S, et al. A nomogram model based on CT-assessed body composition parameters for predicting postoperative recurrence in advanced gastric cancer[J]. Abdom Radiol (NY), 2026, 51(1): 26-38. DOI: 10.1007/s00261-025-05015-6.
[83]
LAN Q Q, GUAN X C, LU S Z, et al. Radiomics in addition to computed tomography-based body composition nomogram may improve the prediction of postoperative complications in gastric cancer patients[J]. Ann Nutr Metab, 2022, 78(6): 316-327. DOI: 10.1159/000526787.
[84]
CONG R C, MING J L, XU R N, et al. Development and validation of a preoperative CT-based risk score integrating morphological and body composition parameters to predict recurrence-free survival in gastric cancer patients following curative surgery[J/OL]. Eur J Radiol, 2025, 191: 112318 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/40684710/. DOI: 10.1016/j.ejrad.2025.112318.
[85]
CHEN Q Y, XIAO H, LI Y Y, et al. A transformer-based prognostic signature integrating tumor and body composition CT images predicts postoperative recurrence in gastric cancer[J/OL]. npj Digit Med, 2026, 9: 12 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/41339473/. DOI: 10.1038/s41746-025-02183-z.
[86]
CHEN Q Y, JIAN L, XIAO H, et al. Transformer-based skeletal muscle deep-learning model for survival prediction in gastric cancer patients after curative resection[J]. Gastric Cancer, 2025, 28(4): 684-695. DOI: 10.1007/s10120-025-01614-w.
[87]
SONG A Y, HUANG Z H, CHEN J H, et al. Baseline and early changes in CT body composition can predict recurrence-free survival after radical gastrectomy: a sex-specific study[J/OL]. Eur J Radiol, 2025, 183: 111935 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/39848123/. DOI: 10.1016/j.ejrad.2025.111935.
[88]
HASHIMOTO I, KOMORI K, ONUMA S, et al. Prognostic impact of adipose tissue loss at 1 month after surgery in patients with gastric cancer[J]. World J Surg, 2025, 49(2): 472-482. DOI: 10.1002/wjs.12370.
[89]
KHAN A A, SHAH S K, NAEEM W, et al. Postoperative sarcopenia and association with recurrence in resected early-stage non-small cell cancer[J]. J Am Coll Surg, 2025, 241(2): 269-279. DOI: 10.1097/XCS.0000000000001360.
[90]
KYLIES J, BALLHAUSE T M, STRIEFLER J, et al. The prognostic role of sarcopenia and muscle wasting in adult osteosarcoma patients: a longitudinal CT morphometric analysis[J/OL]. Eur J Surg Oncol, 2025, 51(9): 110293 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/40627918/. DOI: 10.1016/j.ejso.2025.110293.
[91]
LIU S, LU Y, HU J L, et al. The predictive value and model establishment of body composition in the long-term prognosis of patients after rectal cancer surgery[J]. China Oncol, 2025, 35(7): 672-684. DOI: 10.19401/j.cnki.1007-3639.2025.07.006.
[92]
YANG X X, WANG Z Y, CHEN C. Application value of body composition analysis based on preoperative CT images in predicting postoperative prognosis of patients with pancreatic neuroendocrine tumors[J]. Tumor, 2025, 45(8): 645-657. DOI: 10.3781/j.issn.1000-7431.2025.2502-0061.
[93]
KÜNNEMANN M D, RÖMER C, HELFEN A, et al. Exploration of fully-automated body composition analysis using routine CT-staging of lung cancer patients for survival prognosis[J/OL]. J Cachexia Sarcopenia Muscle, 2025, 16(4): e70021 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/40767951/. DOI: 10.1002/jcsm.70021.
[94]
WEI M Y, CAO K, HONG W, et al. Artificial intelligence measured 3D body composition to predict pathological response in rectal cancer patients[J]. ANZ J Surg, 2024, 94(7/8): 1286-1291. DOI: 10.1111/ans.18929.
[95]
LIN J X, TANG Y H, ZHOU W X, et al. Body composition parameters predict pathological response and outcomes in locally advanced gastric cancer after neoadjuvant treatment: a multicenter, international study[J]. Clin Nutr, 2021, 40(8): 4980-4987. DOI: 10.1016/j.clnu.2021.06.021.
[96]
JANG M K, PARK S, PARK C, et al. Hematologic toxicities, sarcopenia, and body composition change in breast cancer patients undergoing neoadjuvant chemotherapy[J/OL]. Support Care Cancer, 2023, 31(7): 419 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/37354335/. DOI: 10.1007/s00520-023-07890-5.
[97]
SHACHAR E, RAPHAEL A, KATZ U, et al. Body composition measures as a determinant of Alpelisib related toxicity[J]. Breast Cancer Res Treat, 2024, 206(2): 369-376. DOI: 10.1007/s10549-024-07315-9.
[98]
CAO K, YEUNG J, WEI M Y K, et al. Improving the prediction of chemotherapy dose-limiting toxicity in colon cancer patients using an AI-CT-based 3D body composition of the entire L1–L5 lumbar spine[J/OL]. Support Care Cancer, 2024, 33(1): 45 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/39707027/. DOI: 10.1007/s00520-024-09108-8.
[99]
LIU Y Z, SU P F, TAI A S, et al. Artificial intelligence-driven body composition analysis enhances chemotherapy toxicity prediction in colorectal cancer[J/OL]. Clin Nutr ESPEN, 2025, 69: 696-702 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/40803593/. DOI: 10.1016/j.clnesp.2025.08.013.
[100]
PARK J E, JO J, YOUK J, et al. Prognostic utility of body composition parameters based on computed tomography analysis of advanced non-small cell lung cancer treated with immune checkpoint inhibitors[J/OL]. Insights Imaging, 2023, 14(1): 182 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/37880430/. DOI: 10.1186/s13244-023-01532-4.
[101]
XIONG B Z, FU B Y, WU Y L, et al. Body composition predicts prognosis of hepatocellular carcinoma patients undergoing immune checkpoint inhibitors[J]. J Cancer Res Clin Oncol, 2023, 149(13): 11607-11617. DOI: 10.1007/s00432-023-05051-z.
[102]
LIN G T, HUANG J B, LIN J L, et al. Body composition parameters for predicting the efficacy of neoadjuvant chemotherapy with immunotherapy for gastric cancer[J/OL]. Front Immunol, 2022, 13: 1061044 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/36569876/. DOI: 10.3389/fimmu.2022.1061044.
[103]
ABBASS T, DOLAN R D, LAIRD B J, et al. The relationship between imaging-based body composition analysis and the systemic inflammatory response in patients with cancer: a systematic review[J/OL]. Cancers, 2019, 11(9): 1304 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/31487957/. DOI: 10.3390/cancers11091304.
[104]
HAN J, TANG M, LU C C, et al. Subcutaneous, but not visceral, adipose tissue as a marker for prognosis in gastric cancer patients with Cachexia[J]. Clin Nutr, 2021, 40(9): 5156-5161. DOI: 10.1016/j.clnu.2021.08.003.
[105]
BABIC A, ROSENTHAL M H, SUNDARESAN T K, et al. Adipose tissue and skeletal muscle wasting precede clinical diagnosis of pancreatic cancer[J/OL]. Nat Commun, 2023, 14: 4317 [2026-03-29]. https://www.nature.com/articles/s41467-023-40024-3. DOI: 10.1038/s41467-023-40024-3.
[106]
BETTARIGA F, TAAFFE D R, BORSATI A, et al. Exercise and inflammation in female survivors of breast cancer: systematic review and meta-analysis with secondary mediation analysis on body composition[J/OL]. J Cancer Surviv, 2025 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/40715644/. DOI: 10.1007/s11764-025-01873-7.
[107]
ZHOU Y, HAO N B, DUAN Z P, et al. Assessment of acute pancreatitis severity and prognosis with CT-measured body composition[J/OL]. Int J Gen Med, 2021, 14: 3971-3980 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/34349546/. DOI: 10.2147/IJGM.S322589.
[108]
HORIBE M, TAKAHASHI N, WESTON A D, et al. Association between computerized tomography (CT) study of body composition and severity of acute pancreatitis: Use of a novel Z-score supports obesity paradox[J]. Clin Nutr, 2022, 41(8): 1676-1679. DOI: 10.1016/j.clnu.2022.06.010.
[109]
FARQUHAR R, MATTHEWS S, BAXTER N, et al. Sarcopenia and sarcopenic obesity on body composition analysis is a significant predictor of mortality in severe acute pancreatitis: a longitudinal observational study[J]. World J Surg, 2023, 47(11): 2825-2833. DOI: 10.1007/s00268-023-07122-1.
[110]
SUROV A, HINNERICHS M, SHAHZADI I, et al. Parameters of body composition predict clinical course in acute colonic diverticulitis[J/OL]. J Cachexia Sarcopenia Muscle, 2025, 16(3): e13864 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/40524380/. DOI: 10.1002/jcsm.13864.
[111]
HA J, BRIDGE C P, ANDRIOLE K P, et al. Visceral fat quantified by a fully automated deep-learning algorithm and risk of incident and recurrent diverticulitis[J]. Dis Colon Rectum, 2025, 68(6): 726-735. DOI: 10.1097/DCR.0000000000003677.
[112]
TANG W L, XIE G, WANG D N, et al. Imaging-based assessment of body composition in patients with Crohn's disease: a systematic review[J/OL]. Int J Colorectal Dis, 2023, 38(1): 126 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/37171498/. DOI: 10.1007/s00384-023-04413-w.
[113]
BALI A S, SHEIKH W G EL, OUNI A, et al. Radiographically-measured sarcopenia predicts hospitalization in patients with Crohn's disease[J/OL]. J Clin Gastroenterol, 2026 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/41564027/. DOI: 10.1097/mcg.0000000000002317.
[114]
LEE H J, KUO C Y, TSAO Y C, et al. Brain age gap associations with body composition and metabolic indices in an Asian cohort: an MRI-based study[J/OL]. Arch Gerontol Geriatr, 2025, 133: 105830 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/40127523/. DOI: 10.1016/j.archger.2025.105830.
[115]
FRANK G, GUALTIERI P, CIANCI R, et al. Body composition and Alzheimer's disease: a holistic review[J/OL]. Int J Mol Sci, 2024, 25(17): 9573 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/39273520/. DOI: 10.3390/ijms25179573.
[116]
LUO Y X, YANG L L, YAO X Q. Gut microbiota-host lipid crosstalk in Alzheimer's disease: implications for disease progression and therapeutics[J/OL]. Mol Neurodegener, 2024, 19(1): 35 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/38627829/. DOI: 10.1186/s13024-024-00720-0.
[117]
SILVA PINTO G M DA, DE SOUZA CREPALDE L O, SCALLI A C A M, et al. Body composition differences in older adults with and without Alzheimer's disease: a systematic review and meta-analysis of observational studies[J]. J Alzheimers Dis, 2025, 103(3): 649-661. DOI: 10.1177/13872877241304603.
[118]
YAO F M, GU B N, YANG X H, et al. Correlations between lower limb muscle distribution and sarcopenia with Varus knee osteoarthritis progression[J/OL]. Chin J Jt Surg Electron Ed, 2025, 19(3): 292-301 [2026-03-29]. https://d.wanfangdata.com.cn/periodical/zhgjwkzz202503005. DOI: 10.3877/cma.j.issn.1674-134X.2025.03.005.
[119]
SAUVER J L ST, GROSSARDT B R, WESTON A D, et al. Associations between deep learning-derived fat, muscle, and bone measures from abdominal computed tomography scans and fall risk in persons aged 20 years or older[J/OL]. Mayo Clin Proc Digit Health, 2025, 3(4): 100299 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/41323361/. DOI: 10.1016/j.mcpdig.2025.100299.
[120]
SALHÖFER L, BONELLA F, MEETSCHEN M, et al. Automated 3D-body composition analysis as a predictor of survival in patients with idiopathic pulmonary fibrosis[J/OL]. J Thorac Imaging, 2025, 40(2): e0803 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/39183570/. DOI: 10.1097/RTI.0000000000000803.
[121]
TANG H L, XIE L H, LIU L, et al. Renal fat deposition measured on Dixon-based MRI is significantly associated with early kidney damage in obesity[J]. Abdom Radiol (NY), 2024, 49(10): 3476-3484. DOI: 10.1007/s00261-024-04391-9.
[122]
ZHU M H, LI H Y, YIN Y, et al. U-shaped relationship between subcutaneous adipose tissue index and mortality in liver cirrhosis[J]. J Cachexia Sarcopenia Muscle, 2023, 14(1): 508-516. DOI: 10.1002/jcsm.13154.
[123]
FUMAGALLI I A, LE S T, PENG P D, et al. Automated CT analysis of body composition as a frailty biomarker in abdominal surgery[J/OL]. JAMA Surg, 2024, 159(7): 766 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/38598191/. DOI: 10.1001/jamasurg.2024.0628.
[124]
SOH S, SUH Y J, LEE S, et al. Prognostic value of CT body composition analysis for 1-year mortality after transcatheter aortic valve replacement[J]. Eur Radiol, 2025, 35(1): 244-254. DOI: 10.1007/s00330-024-10953-8.
[125]
DEL GIORNO R, ROBALDO A, ASTORINO A, et al. The impact of body composition on mortality and hospital length of stay after endovascular and open aortic aneurysm repair: a retrospective cohort study[J/OL]. Nutrients, 2024, 16(18): 3205 [2026-03-29]. https://pubmed.ncbi.nlm.nih.gov/39339803/. DOI: 10.3390/nu16183205.
[126]
CALLERY M P, PRATT W B, KENT T S, et al. A prospectively validated clinical risk score accurately predicts pancreatic fistula after pancreatoduodenectomy[J]. J Am Coll Surg, 2013, 216(1): 1-14. DOI: 10.1016/j.jamcollsurg.2012.09.002.
[127]
FUKUSHIMA R, HARIMOTO N, ARAKI K, et al. Impact of body composition on the development of pancreatic fistula after pancreaticoduodenectomy[J]. Surg Today, 2025, 55(11): 1580-1588. DOI: 10.1007/s00595-025-03039-5.
[128]
TANAKA K, YAMADA S, SONOHARA F, et al. Pancreatic fat and body composition measurements by computed tomography are associated with pancreatic fistula after pancreatectomy[J]. Ann Surg Oncol, 2021, 28(1): 530-538. DOI: 10.1245/s10434-020-08581-9.
[129]
OLSSON L T, SANCHEZ A, MOURTZAKIS M, et al. Body composition may be a novel presurgical risk factor for acute kidney injury among clear cell renal cell cancer patients undergoing radical nephrectomy[J]. J Kidney Cancer VHL, 2025, 12(4): 19-29. DOI: 10.15586/jkc.v12i4.423.

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