Share:
Share this content in WeChat
X
Review
Research progress of imaging and artificial intelligence technology in quantitative assessment of sarcopenia in liver cirrhosis
XU Yuan  LIU Jianli 

Cite this article as: Xu Y, Liu JL. Research progress of imaging and artificial intelligence technology in quantitative assessment of sarcopenia in liver cirrhosis[J]. Chin J Magn Reson Imaging, 2022, 13(11): 149-153. DOI:10.12015/issn.1674-8034.2022.11.030.


[Abstract] Sarcopenia is a common complication of liver cirrhosis and an important cause of poor prognosis in patients with liver cirrhosis, the early identification and prevention has become the focus of clinical work and a hot spot. Imaging methods can not only evaluate liver lesions in patients with liver cirrhosis, but also quantify muscle area, muscle density and muscle fat content to evaluate the prognosis of liver cirrhosis; in addition, the application of artificial intelligence (AI) technology in the medical field has provided new ideas for accurate and rapid identification and quantitative assessment of cirrhotic sarcopenia. This article focuses on dual-energy X-ray absorptiometry (DEXA), ultrasound (US), MRI, CT and AI techniques for quantitative evaluation of cirrhotic sarcopenia are reviewed with the aim of providing imaging references to guide clinical decision-making.
[Keywords] liver cirrhosis;sarcopenia;muscle area;muscle density;muscle fat content;dual-energy X-ray absorptiometry;ultrasound;magnetic resonance imaging;computed tomography;artificial intelligence;radiomics

XU Yuan   LIU Jianli*  

Radiology Department of Lanzhou University Second Hospital, Second Clinical School of Lanzhou University, Key Laboratory of Medical Imaging of Gansu Province, Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China

Liu JL, E-mail: lz8943115@qq.com

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China Regional Project (No. 81960337); Basic Research Innovation Group Project of Gansu Province (No. 21JR7RA432); Lanzhou Talent Innovation and Entrepreneurship Project (No. 2020-RC-49); Lanzhou University Second Hospital "Cuiying Postgraduate Instructor" Cultivation Program Project (No. CYDSPY202003).
Received  2022-03-29
Accepted  2022-10-11
DOI: 10.12015/issn.1674-8034.2022.11.030
Cite this article as: Xu Y, Liu JL. Research progress of imaging and artificial intelligence technology in quantitative assessment of sarcopenia in liver cirrhosis[J]. Chin J Magn Reson Imaging, 2022, 13(11): 149-153. DOI:10.12015/issn.1674-8034.2022.11.030.

[1]
Garcia-Pagan JC, Francoz C, Montagnese S, et al. Management of the major complications of cirrhosis: beyond guidelines[J]. J Hepatol, 2021, 75(Suppl 1): S135-S146. DOI: 10.1016/j.jhep.2021.01.027.
[2]
Hepatology Branch of Chinese Medical Association. Guidelines for diagnosis and treatment of liver cirrhosis[J]. J Pract Hepatol, 2019, 22(6): 846-865. DOI: 10.3969/j.issn.1672-5069.2019.06.004.
[3]
Tantai XX, Liu Y, Yeo YH, et al. Effect of sarcopenia on survival in patients with cirrhosis: a meta-analysis[J]. J Hepatol, 2022, 76(3): 588-599. DOI: 10.1016/j.jhep.2021.11.006.
[4]
Chen LK, Woo J, Assantachai P, et al. Asian working group for sarcopenia: 2019 consensus update on sarcopenia diagnosis and treatment[J]. J Am Med Dir Assoc, 2020, 21(3): 300-307. DOI: 10.1016/j.jamda.2019.12.012.
[5]
Donini LM, Busetto L, Bischoff SC, et al. Definition and diagnostic criteria for sarcopenic obesity: ESPEN and EASO consensus statement[J]. Obes Facts, 2022, 15(3): 321-335. DOI: 10.1159/000521241.
[6]
Saeki C, Tsubota A. Influencing factors and molecular pathogenesis of sarcopenia and osteosarcopenia in chronic liver disease[J/OL]. Life (Basel), 2021, 11(9) [2022-03-30]. https://doi.org/10.3390/life11090899. DOI: 10.3390/life11090899.
[7]
Zhang PY, Qin XH, Wang YZ. Research progress of sarcopenia in patients with cirrhosis[J]. Parenter & Enter Nutr, 2022, 29(1): 57-61, 64. DOI: 10.16151/j.1007-810x.2022.01.011.
[8]
Ebadi M, Bhanji RA, Mazurak VC, et al. Sarcopenia in cirrhosis: from pathogenesis to interventions[J]. J Gastroenterol, 2019, 54(10): 845-859. DOI: 10.1007/s00535-019-01605-6.
[9]
Marasco G, Dajti E, Ravaioli F, et al. Clinical impact of sarcopenia assessment in patients with liver cirrhosis[J]. Expert Rev Gastroenterol Hepatol, 2021, 15(4): 377-388. DOI: 10.1080/17474124.2021.1848542.
[10]
Ooi PH, Hager A, Mazurak VC, et al. Sarcopenia in chronic liver disease: impact on outcomes[J]. Liver Transpl, 2019, 25(9): 1422-1438. DOI: 10.1002/lt.25591.
[11]
Kang SH, Jeong WK, Baik SK, et al. Impact of sarcopenia on prognostic value of cirrhosis: going beyond the hepatic venous pressure gradient and MELD score[J]. J Cachexia Sarcopenia Muscle, 2018, 9(5): 860-870. DOI: 10.1002/jcsm.12333.
[12]
Montano-Loza AJ, Duarte-Rojo A, Meza-Junco J, et al. Inclusion of sarcopenia within MELD (MELD-sarcopenia) and the prediction of mortality in patients with cirrhosis[J/OL]. Clin Transl Gastroenterol, 2015, 6(7) [2022-03-30]. https://doi.org/10.1038/ctg.2015.31. DOI: 10.1038/ctg.2015.31.
[13]
van Vugt JLA, Alferink LJM, Buettner S, et al. A model including sarcopenia surpasses the MELD score in predicting waiting list mortality in cirrhotic liver transplant candidates: a competing risk analysis in a national cohort[J]. J Hepatol, 2018, 68(4): 707-714. DOI: 10.1016/j.jhep.2017.11.030.
[14]
Surov A, Paul L, Meyer HJ, et al. Apparent diffusion coefficient is a novel imaging biomarker of myopathic changes in liver cirrhosis[J/OL]. J Clin Med, 2018, 7(10) [2022-03-30]. https://doi.org/10.3390/jcm7100359. DOI: 10.3390/jcm7100359.
[15]
Cespiati A, Meroni M, Lombardi R, et al. Impact of sarcopenia and myosteatosis in non-cirrhotic stages of liver diseases: similarities and differences across aetiologies and possible therapeutic strategies[J/OL]. Biomedicines, 2022, 10(1) [2022/3/30]. https://doi.org/10.3390/biomedicines10010182. DOI: 10.3390/biomedicines10010182.
[16]
Aamann L, Dam G, Borre M, et al. Resistance training increases muscle strength and muscle size in patients with liver cirrhosis[J]. Clin Gastroenterol Hepatol, 2020, 18(5): 1179-1187. DOI: 10.1016/j.cgh.2019.07.058.
[17]
Liu JC, Ma JQ, Yang CT, et al. Sarcopenia in patients with cirrhosis after transjugular intrahepatic portosystemic shunt placement[J]. Radiology, 2022, 303(3): 711-719. DOI: 10.1148/radiol.211172.
[18]
Messina C, Albano D, Gitto S, et al. Body composition with dual energy X-ray absorptiometry: from basics to new tools[J]. Quant Imaging Med Surg, 2020, 10(8): 1687-1698. DOI: 10.21037/qims.2020.03.02.
[19]
Cruz-Jentoft AJ, Bahat G, Bauer J, et al. Sarcopenia: revised European consensus on definition and diagnosis[J]. Age Ageing, 2019, 48(1):16-31. DOI: 10.1093/ageing/afy169.
[20]
Lindqvist C, Majeed A, Wahlin S. Body composition assessed by dual-energy X-ray absorptiometry predicts early infectious complications after liver transplantation[J]. J Hum Nutr Diet, 2017, 30(3): 284-291. DOI: 10.1111/jhn.12417.
[21]
Eriksen CS, Kimer N, Suetta C, et al. Arm lean mass determined by dual-energy X-ray absorptiometry is superior to characterize skeletal muscle and predict sarcopenia-related mortality in cirrhosis[J]. Am J Physiol Gastrointest Liver Physiol, 2021, 320(5): G729-G740. DOI: 10.1152/ajpgi.00478.2020.
[22]
Santos LAA, Lima TB, Qi XS, et al. Refining dual-energy X-ray absorptiometry data to predict mortality among cirrhotic outpatients: a retrospective study[J/OL]. Nutrition, 2021, 85 [2022-03-30]. https://doi.org/10.1016/j.nut.2020.111132. DOI: 10.1016/j.nut.2020.111132.
[23]
Chianca V, Albano D, Messina C, et al. Sarcopenia: imaging assessment and clinical application[J]. Abdom Radiol (NY), 2022, 47(9): 3205-3216. DOI: 10.1007/s00261-021-03294-3.
[24]
Perkisas S, Bastijns S, Baudry S, et al. Application of ultrasound for muscle assessment in sarcopenia: 2020 SARCUS update[J]. Eur Geriatr Med, 2021, 12(1): 45-59. DOI: 10.1007/s41999-020-00433-9.
[25]
Perkisas S, Baudry S, Bauer J, et al. Application of ultrasound for muscle assessment in sarcopenia: towards standardized measurements[J]. Eur Geriatr Med, 2018, 9(6): 739-757. DOI: 10.1007/s41999-018-0104-9.
[26]
Sconfienza LM. Sarcopenia: ultrasound today, smartphones tomorrow?[J]. Eur Radiol, 2019, 29(1): 1-2. DOI: 10.1007/s00330-018-5833-0.
[27]
Kobayashi K, Maruyama H, Kiyono S, et al. Application of transcutaneous ultrasonography for the diagnosis of muscle mass loss in patients with liver cirrhosis[J]. J Gastroenterol, 2018, 53(5): 652-659. DOI: 10.1007/s00535-017-1378-2.
[28]
Tandon P, Low G, Mourtzakis M, et al. A model to identify sarcopenia in patients with cirrhosis[J]. Clin Gastroenterol Hepatol, 2016, 14(10): 1473-1480. DOI: 10.1016/j.cgh.2016.04.040.
[29]
Hari A, Berzigotti A, Štabuc B, et al. Muscle psoas indices measured by ultrasound in cirrhosis - Preliminary evaluation of sarcopenia assessment and prediction of liver decompensation and mortality[J]. Dig Liver Dis, 2019, 51(11): 1502-1507. DOI: 10.1016/j.dld.2019.08.017.
[30]
Lee CM, Kang BK, Kim M. Radiologic Definition of Sarcopenia in Chronic Liver Disease[J/OL]. Life (Basel), 2021, 11(2) [2022-03-30]. https://doi.org/10.3390/life11020086. DOI: 10.3390/life11020086.
[31]
Fischer MA, Pfirrmann CW, Espinosa N, et al. Dixon-based MRI for assessment of muscle-fat content in phantoms, healthy volunteers and patients with achillodynia: comparison to visual assessment of calf muscle quality[J]. Eur Radiol, 2014, 24(6): 1366-1375. DOI: 10.1007/s00330-014-3121-1.
[32]
Praktiknjo M, Book M, Luetkens J, et al. Fat-free muscle mass in magnetic resonance imaging predicts acute-on-chronic liver failure and survival in decompensated cirrhosis[J]. Hepatology, 2018, 67(3): 1014-1026. DOI: 10.1002/hep.29602.
[33]
Beer L, Bastati N, Ba-Ssalamah A, et al. MRI-defined sarcopenia predicts mortality in patients with chronic liver disease[J]. Liver Int, 2020, 40(11): 2797-2807. DOI: 10.1111/liv.14648.
[34]
Meyer HJ, Schneider I, Emmer A, et al. Associations between apparent diffusion coefficient values and histopathological tissue alterations in myopathies[J/OL]. Brain Behav, 2020, 10(11) [2022-03-30]. https://doi.org/10.1002/brb3.1809. DOI: 10.1002/brb3.1809.
[35]
Jacobsen EB, Hamberg O, Quistorff B, et al. Reduced mitochondrial adenosine triphosphate synthesis in skeletal muscle in patients with Child-Pugh class B and C cirrhosis[J]. Hepatology, 2001, 34(1): 7-12. DOI: 10.1053/jhep.2001.25451.
[36]
Buchard B, Boirie Y, Cassagnes L, et al. Assessment of malnutrition, sarcopenia and frailty in patients with cirrhosis: which tools should we use in clinical practice?[J/OL]. Nutrients, 2020, 12(1) [2022-03-30]. https://doi.org/10.3390/nu12010186. DOI: 10.3390/nu12010186.
[37]
Grimm A, Meyer H, Nickel MD, et al. Repeatability of Dixon magnetic resonance imaging and magnetic resonance spectroscopy for quantitative muscle fat assessments in the thigh[J]. J Cachexia Sarcopenia Muscle, 2018, 9(6): 1093-1100. DOI: 10.1002/jcsm.12343.
[38]
Forbes SC, Lott DJ, Finkel RS, et al. MRI/MRS evaluation of a female carrier of Duchenne muscular dystrophy[J]. Neuromuscul Disord, 2012, 22(Suppl 2): S111-S121. DOI: 10.1016/j.nmd.2012.05.013.
[39]
Codari M, Zanardo M, di Sabato ME, et al. MRI-derived biomarkers related to sarcopenia: a systematic review[J]. J Magn Reson Imaging, 2020, 51(4): 1117-1127. DOI: 10.1002/jmri.26931.
[40]
Triplett WT, Baligand C, Forbes SC, et al. Chemical shift-based MRI to measure fat fractions in dystrophic skeletal muscle[J]. Magn Reson Med, 2014, 72(1): 8-19. DOI: 10.1002/mrm.24917.
[41]
Karampinos DC, Baum T, Nardo L, et al. Characterization of the regional distribution of skeletal muscle adipose tissue in type 2 diabetes using chemical shift-based water/fat separation[J]. J Magn Reson Imaging, 2012, 35(4): 899-907. DOI: 10.1002/jmri.23512.
[42]
Guerini H, Omoumi P, Guichoux F, et al. Fat suppression with Dixon techniques in musculoskeletal magnetic resonance imaging: a pictorial review[J]. Semin Musculoskelet Radiol, 2015, 19(4): 335-347. DOI: 10.1055/s-0035-1565913.
[43]
Giraudo C, Cavaliere A, Lupi A, et al. Established paths and new avenues: a review of the main radiological techniques for investigating sarcopenia[J]. Quant Imaging Med Surg, 2020, 10(8): 1602-1613. DOI: 10.21037/qims.2019.12.15.
[44]
Faron A, Sprinkart AM, Kuetting DLR, et al. Body composition analysis using CT and MRI: intra-individual intermodal comparison of muscle mass and myosteatosis[J/OL]. Sci Rep, 2020, 10 [2022-03-30]. https://doi.org/10.1038/s41598-020-68797-3. DOI: 10.1038/s41598-020-68797-3.
[45]
Zeng X, Shi ZW, Yu JJ, et al. Sarcopenia as a prognostic predictor of liver cirrhosis: a multicentre study in China[J]. J Cachexia Sarcopenia Muscle, 2021, 12(6): 1948-1958. DOI: 10.1002/jcsm.12797.
[46]
Carey EJ, Lai JC, Sonnenday C, et al. A North American expert opinion statement on sarcopenia in liver transplantation[J]. Hepatology, 2019, 70(5): 1816-1829. DOI: 10.1002/hep.30828.
[47]
Paternostro R, Bardach C, Hofer BS, et al. Prognostic impact of sarcopenia in cirrhotic patients stratified by different severity of portal hypertension[J]. Liver Int, 2021, 41(4): 799-809. DOI: 10.1111/liv.14758.
[48]
Hari A. Muscular abnormalities in liver cirrhosis[J]. World J Gastroenterol, 2021, 27(29): 4862-4878. DOI: 10.3748/wjg.v27.i29.4862.
[49]
Ebadi M, Wang CW, Lai JC, et al. Poor performance of psoas muscle index for identification of patients with higher waitlist mortality risk in cirrhosis[J]. J Cachexia Sarcopenia Muscle, 2018, 9(6): 1053-1062. DOI: 10.1002/jcsm.12349.
[50]
Aubrey J, Esfandiari N, Baracos VE, et al. Measurement of skeletal muscle radiation attenuation and basis of its biological variation[J]. Acta Physiol (Oxf), 2014, 210(3): 489-497. DOI: 10.1111/apha.12224.
[51]
Wang CW, Feng S, Covinsky KE, et al. A comparison of muscle function, mass, and quality in liver transplant candidates: results from the functional assessment in liver transplantation study[J]. Transplantation, 2016, 100(8): 1692-1698. DOI: 10.1097/TP.0000000000001232.
[52]
Feng HJ, Wang XY, Mao LH, et al. Relationship between sarcopenia/myosteatosis and frailty in hospitalized patients with cirrhosis: a sex-stratified analysis[J/OL]. Ther Adv Chronic Dis, 2021, 12 [2022-03-30]. https://doi.org/10.1177/20406223211026996. DOI: 10.1177/20406223211026996.
[53]
Qi M, Zhang SW, Liu B. Clinical significance and diagnosis of sarcopenia with CT and MR[J]. Chin J Osteoporos, 2018, 24(11): 1530-1534. DOI: 10.3969/j.issn.1006-7108.2018.11.029.
[54]
Wang B, Torriani M. Artificial intelligence in the evaluation of body composition[J]. Semin Musculoskelet Radiol, 2020, 24(1): 30-37. DOI: 10.1055/s-0039-3400267.
[55]
Malhotra P, Gupta S, Koundal D, et al. Deep neural networks for medical image segmentation[J/OL]. J Healthc Eng, 2022 [2022-03-30]. https://doi.org/10.1155/2022/9580991. DOI: 10.1155/2022/9580991.
[56]
Zou WY, Enchakalody BE, Zhang P, et al. Automated measurements of body composition in abdominal CT scans using artificial intelligence can predict mortality in patients with cirrhosis[J]. Hepatol Commun, 2021, 5(11): 1901-1910. DOI: 10.1002/hep4.1768.
[57]
Barnard R, Tan J, Roller B, et al. Machine learning for automatic paraspinous muscle area and attenuation measures on low-dose chest CT scans[J]. Acad Radiol, 2019, 26(12): 1686-1694. DOI: 10.1016/j.acra.2019.06.017.
[58]
Wang NC, Zhang P, Tapper EB, et al. Automated measurements of muscle mass using deep learning can predict clinical outcomes in patients with liver disease[J]. Am J Gastroenterol, 2020, 115(8): 1210-1216. DOI: 10.14309/ajg.0000000000000662.
[59]
Kim Y. Machine learning models for sarcopenia identification based on radiomic features of muscles in computed tomography[J/OL]. Int J Environ Res Public Health, 2021, 18(16) [2022-03-30]. https://doi.org/10.3390/ijerph18168710. DOI: 10.3390/ijerph18168710.
[60]
Zhang SY, Yu MR, Chen D, et al. Role of MRI-based radiomics in locally advanced rectal cancer (Review)[J/OL]. Oncol Rep, 2022, 47(2) [2022-03-30]. https://doi.org/10.3892/or.2021.8245. DOI: 10.3892/or.2021.8245.

PREV Myocardial fibrosis CMR and its application progress in diabetic cardiomyopathy
NEXT Application and research progress of radiomics in intraductal papillary mucinous neoplasm of the pancreas
  



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