{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T14:53:27Z","timestamp":1776783207279,"version":"3.51.2"},"reference-count":40,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.fr","ejradiology.com","clinicalkey.jp","clinicalkey.es","clinicalkey.com.au","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["European Journal of Radiology"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1016\/j.ejrad.2024.111748","type":"journal-article","created":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T01:08:01Z","timestamp":1726794481000},"page":"111748","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":5,"special_numbering":"C","title":["Comparison of different MRI-based unsupervised segmentation algorithms in predicting sarcopenia"],"prefix":"10.1016","volume":"181","author":[{"given":"Huayan","family":"Zuo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiyang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoli","family":"Bi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yijin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengxiu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunzhu","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiarong","family":"Gong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiu","family":"Bi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.ejrad.2024.111748_b0005","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1093\/ageing\/afz046","article-title":"Sarcopenia: revised European consensus on definition and diagnosis","volume":"48","author":"Cruz-Jentoft","year":"2019","journal-title":"Age Ageing"},{"issue":"1","key":"10.1016\/j.ejrad.2024.111748_b0010","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1002\/jcsm.12048","article-title":"SARC-F: a symptom score to predict persons with sarcopenia at risk for poor functional outcomes","volume":"7","author":"Malmstrom","year":"2016","journal-title":"J Cachexia Sarcopenia Muscle"},{"issue":"5","key":"10.1016\/j.ejrad.2024.111748_b0015","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1097\/MCO.0000000000000496","article-title":"Bioelectrical impedance analysis in the assessment of sarcopenia","volume":"21","author":"Gonzalez","year":"2018","journal-title":"Curr Opin Clin Nutr Metab Care"},{"issue":"1","key":"10.1016\/j.ejrad.2024.111748_b0020","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1186\/s12877-016-0349-4","article-title":"Sarcopenia in daily practice: assessment and management","volume":"16","author":"Beaudart","year":"2016","journal-title":"BMC Geriatr"},{"issue":"6","key":"10.1016\/j.ejrad.2024.111748_b0025","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1002\/nbm.3113","article-title":"Multi-parametric MRI characterization of inflammation in murine skeletal muscle","volume":"27","author":"Bryant","year":"2014","journal-title":"NMR in Biomedicine"},{"issue":"4","key":"10.1016\/j.ejrad.2024.111748_b0030","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1002\/jmri.25016","article-title":"Techniques and applications of skeletal muscle diffusion tensor imaging: A review","volume":"43","author":"Oudeman","year":"2016","journal-title":"J Magn Reson Imaging"},{"issue":"7\u20138","key":"10.1016\/j.ejrad.2024.111748_b0035","first-page":"157","article-title":"Sarcopenia","volume":"169","author":"Keller","year":"2018","journal-title":"Wiener Medizinische Wochenschrift"},{"issue":"6","key":"10.1016\/j.ejrad.2024.111748_b0040","doi-asserted-by":"crossref","first-page":"1366","DOI":"10.1007\/s00330-014-3121-1","article-title":"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","volume":"24","author":"Fischer","year":"2014","journal-title":"Eur Radiol"},{"issue":"9","key":"10.1016\/j.ejrad.2024.111748_b0045","doi-asserted-by":"crossref","first-page":"3205","DOI":"10.1007\/s00261-021-03294-3","article-title":"Sarcopenia: imaging assessment and clinical application","volume":"47","author":"Chianca","year":"2022","journal-title":"Abdom Radiol (NY)"},{"issue":"3","key":"10.1016\/j.ejrad.2024.111748_b0050","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1055\/s-0038-1641573","article-title":"Sarcopenia: Beyond Muscle Atrophy and into the New Frontiers of Opportunistic Imaging, Precision Medicine, and Machine Learning","volume":"22","author":"Lenchik","year":"2018","journal-title":"Semin Musculoskelet Radiol"},{"key":"10.1016\/j.ejrad.2024.111748_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.nut.2021.111227","article-title":"Use of artificial intelligence in the imaging of sarcopenia: A narrative review of current status and perspectives","volume":"89","author":"Rozynek","year":"2021","journal-title":"Nutrition"},{"issue":"1","key":"10.1016\/j.ejrad.2024.111748_b0060","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.clnu.2010.07.012","article-title":"Magnetic resonance imaging with k-means clustering objectively measures whole muscle volume compartments in sarcopenia\/cancer cachexia","volume":"30","author":"Gray","year":"2011","journal-title":"Clin Nutr"},{"key":"10.1016\/j.ejrad.2024.111748_b0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2022.110218","article-title":"Artificial intelligence for body composition and sarcopenia evaluation on computed tomography: A systematic review and meta-analysis","volume":"149","author":"Bedrikovetski","year":"2022","journal-title":"Eur J Radiol"},{"issue":"3","key":"10.1016\/j.ejrad.2024.111748_b0070","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.jamda.2019.12.012","article-title":"Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment","volume":"21","author":"Chen","year":"2020","journal-title":"J Am Med Dir Assoc"},{"issue":"1","key":"10.1016\/j.ejrad.2024.111748_b0075","doi-asserted-by":"crossref","first-page":"3152","DOI":"10.1038\/s41467-024-47512-0","article-title":"Enhancing NSCLC recurrence prediction with PET\/CT habitat imaging, ctDNA, and integrative radiogenomics-blood insights","volume":"15","author":"Sujit","year":"2024","journal-title":"Nat Commun"},{"issue":"2","key":"10.1016\/j.ejrad.2024.111748_b0080","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1002\/jmri.28423","article-title":"Preliminary MRI Study of Extracellular Volume Fraction for Identification of Lymphovascular Space Invasion of Cervical Cancer","volume":"57","author":"Wang","year":"2023","journal-title":"J Magn Reson Imaging"},{"issue":"4","key":"10.1016\/j.ejrad.2024.111748_b0085","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1017\/S0029665115000129","article-title":"Skeletal muscle mass and quality: evolution of modern measurement concepts in the context of sarcopenia","volume":"74","author":"Heymsfield","year":"2015","journal-title":"Proc Nutr Soc"},{"issue":"4","key":"10.1016\/j.ejrad.2024.111748_b0090","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s13539-014-0164-8","article-title":"Prevalence, incidence and clinical impact of cachexia: facts and numbers-update 2014","volume":"5","author":"von Haehling","year":"2014","journal-title":"J Cachexia Sarcopenia Muscle"},{"issue":"3","key":"10.1016\/j.ejrad.2024.111748_b0095","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1159\/000521241","article-title":"Definition and Diagnostic Criteria for Sarcopenic Obesity: ESPEN and EASO Consensus Statement","volume":"15","author":"Donini","year":"2022","journal-title":"Obes Facts"},{"issue":"1","key":"10.1016\/j.ejrad.2024.111748_b0100","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1097\/MCO.0000000000000519","article-title":"Sarcopenia and obesity","volume":"22","author":"Zamboni","year":"2019","journal-title":"Curr Opin Clin Nutr Metab Care"},{"issue":"Suppl 1","key":"10.1016\/j.ejrad.2024.111748_b0105","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1111\/ggi.12220","article-title":"Incidence, reversibility, risk factors and the protective effect of high body mass index against sarcopenia in community-dwelling older Chinese adults","volume":"14","author":"Yu","year":"2014","journal-title":"Geriatr Gerontol Int"},{"issue":"6","key":"10.1016\/j.ejrad.2024.111748_b0110","doi-asserted-by":"crossref","DOI":"10.3390\/jcm8060775","article-title":"Nutritional Laboratory Markers in Malnutrition","volume":"8","author":"Keller","year":"2019","journal-title":"J Clin Med"},{"issue":"3","key":"10.1016\/j.ejrad.2024.111748_b0115","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1093\/ajcn\/82.3.531","article-title":"Lower serum albumin concentration and change in muscle mass: the Health, Aging and Body Composition Study","volume":"82","author":"Visser","year":"2005","journal-title":"Am J Clin Nutr"},{"issue":"4 Pt 1","key":"10.1016\/j.ejrad.2024.111748_b0120","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.1164\/ajrccm.162.4.9910106","article-title":"Albumin-mediated regulation of cellular glutathione and nuclear factor kappa B activation","volume":"162","author":"Cantin","year":"2000","journal-title":"Am J Respir Crit Care Med"},{"issue":"1","key":"10.1016\/j.ejrad.2024.111748_b0125","doi-asserted-by":"crossref","first-page":"58","DOI":"10.3945\/ajcn.115.111203","article-title":"What is the best reference site for a single MRI slice to assess whole-body skeletal muscle and adipose tissue volumes in healthy adults?","volume":"102","author":"Schweitzer","year":"2015","journal-title":"Am J Clin Nutr"},{"issue":"2","key":"10.1016\/j.ejrad.2024.111748_b0130","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1148\/radiol.12120399","article-title":"Quantification of muscle fat in patients with low back pain: comparison of multi-echo MR imaging with single-voxel MR spectroscopy","volume":"266","author":"Fischer","year":"2013","journal-title":"Radiology"},{"issue":"11","key":"10.1016\/j.ejrad.2024.111748_b0135","doi-asserted-by":"crossref","first-page":"806","DOI":"10.2519\/jospt.2011.3576","article-title":"Magnetic resonance imaging: the underlying principles","volume":"41","author":"McMahon","year":"2011","journal-title":"J Orthop Sports Phys Ther"},{"issue":"5","key":"10.1016\/j.ejrad.2024.111748_b0140","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1038\/s41576-018-0088-9","article-title":"Challenges in unsupervised clustering of single-cell RNA-seq data","volume":"20","author":"Kiselev","year":"2019","journal-title":"Nat Rev Genet"},{"key":"10.1016\/j.ejrad.2024.111748_b0145","unstructured":"Macqueen, J.B., Some Methods for Classification and Analysis of Multivariate Observations. 1966."},{"issue":"4","key":"10.1016\/j.ejrad.2024.111748_b0150","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1002\/jmri.23884","article-title":"Automated unsupervised multi-parametric classification of adipose tissue depots in skeletal muscle","volume":"37","author":"Valentinitsch","year":"2012","journal-title":"Journal of Magnetic Resonance Imaging"},{"key":"10.1016\/j.ejrad.2024.111748_b0155","first-page":"186","volume":"60","author":"Feng","year":"2017","journal-title":"A Multi-Scale 3D Otsu Thresholding Algorithm for Medical Image Segmentation."},{"key":"10.1016\/j.ejrad.2024.111748_b0160","first-page":"299","volume":"116","author":"Merzban","year":"2019","journal-title":"Efficient Solution of Otsu Multilevel Image Thresholding: A Comparative Study."},{"issue":"1","key":"10.1016\/j.ejrad.2024.111748_b0165","doi-asserted-by":"crossref","first-page":"15065","DOI":"10.1038\/s41598-021-93026-w","article-title":"Deep-learning framework and computer assisted fatty infiltration analysis for the supraspinatus muscle in MRI","volume":"11","author":"Ro","year":"2021","journal-title":"Sci Rep"},{"issue":"3","key":"10.1016\/j.ejrad.2024.111748_b0170","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.cmpb.2014.02.014","article-title":"A novel automatic suspicious mass regions identification using Havrda & Charvat entropy and Otsu's N thresholding","volume":"114","author":"Kurt","year":"2014","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"10.1016\/j.ejrad.2024.111748_b0175","first-page":"315","volume":"31","author":"Gupta","year":"1998","journal-title":"A Gaussian-Mixture-Based Image Segmentation Algorithm."},{"issue":"11","key":"10.1016\/j.ejrad.2024.111748_b0180","doi-asserted-by":"crossref","first-page":"4223","DOI":"10.1118\/1.2791035","article-title":"A Gaussian mixture model for definition of lung tumor volumes in positron emission tomography","volume":"34","author":"Aristophanous","year":"2007","journal-title":"Medical Physics"},{"key":"10.1016\/j.ejrad.2024.111748_b0185","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.neucom.2020.12.082","article-title":"Unsupervised deep clustering via adaptive GMM modeling and optimization","volume":"433","author":"Wang","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.ejrad.2024.111748_b0190","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2022.1037896","article-title":"Visualising spatial heterogeneity in glioblastoma using imaging habitats","volume":"12","author":"Waqar","year":"2022","journal-title":"Frontiers in Oncology"},{"key":"10.1016\/j.ejrad.2024.111748_b0195","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.oraloncology.2016.09.006","article-title":"Feasibility of using head and neck CT imaging to assess skeletal muscle mass in head and neck cancer patients","volume":"62","author":"Swartz","year":"2016","journal-title":"Oral Oncology"},{"issue":"5","key":"10.1016\/j.ejrad.2024.111748_b0200","doi-asserted-by":"crossref","first-page":"e221843","DOI":"10.1148\/radiol.221843","article-title":"Combining Deep Learning and Handcrafted Radiomics for Classification of Suspicious Lesions on Contrast-enhanced Mammograms","volume":"307","author":"Beuque","year":"2023","journal-title":"Radiology"}],"container-title":["European Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X24004649?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X24004649?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T17:12:17Z","timestamp":1738429937000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0720048X24004649"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":40,"alternative-id":["S0720048X24004649"],"URL":"https:\/\/doi.org\/10.1016\/j.ejrad.2024.111748","relation":{},"ISSN":["0720-048X"],"issn-type":[{"value":"0720-048X","type":"print"}],"subject":[],"published":{"date-parts":[[2024,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Comparison of different MRI-based unsupervised segmentation algorithms in predicting sarcopenia","name":"articletitle","label":"Article Title"},{"value":"European Journal of Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ejrad.2024.111748","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"111748"}}