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Development of quantification software for evaluating body composition contents and its clinical application in sarcopenic obesity
In sarcopenic obesity, the importance of evaluating muscle and fat mass is unquestionable. There exist diverse quantification methods for assessing muscle and fat mass by imaging techniques; thus these methods must be standardized for clinical practice. This study developed a quantification software...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7320181/ https://www.ncbi.nlm.nih.gov/pubmed/32591563 http://dx.doi.org/10.1038/s41598-020-67461-0 |
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author | Kim, SeungJin Kim, Tae-Hoon Jeong, Chang-Won Lee, ChungSub Noh, SiHyeong Kim, Ji Eon Yoon, Kwon-Ha |
author_facet | Kim, SeungJin Kim, Tae-Hoon Jeong, Chang-Won Lee, ChungSub Noh, SiHyeong Kim, Ji Eon Yoon, Kwon-Ha |
author_sort | Kim, SeungJin |
collection | PubMed |
description | In sarcopenic obesity, the importance of evaluating muscle and fat mass is unquestionable. There exist diverse quantification methods for assessing muscle and fat mass by imaging techniques; thus these methods must be standardized for clinical practice. This study developed a quantification software for the body composition imaging using abdominal magnetic resonance (MR) images and compared the difference between sarcopenic obesity and healthy controls for clinical application. Thirty patients with sarcopenic obesity and 30 healthy controls participated. The quantification software was developed based on an ImageJ multiplatform and the processing steps are as follows: execution, setting, confirmation, and extraction. The variation in the muscle area (MA), subcutaneous fat area (SA), and visceral fat area (VA) was analyzed with an independent two sample T-test. There were significant differences in SA (p < 0.001) and VA (p = 0.011), whereas there was no difference in MA (p = 0.421). Regarding the ratios, there were significant differences in MA/SA (p < 0.001), MA/VA (p = 0.002), and MA/(SA + VA) (p < 0.001). Overall, intraclass correlation coefficients were higher than 0.9, indicating excellent reliability. This study developed customized sarcopenia-software for assessing body composition using abdominal MR images. The clinical findings demonstrate that the quantitative body composition areas and ratios can assist in the differential diagnosis of sarcopenic obesity or sarcopenia. |
format | Online Article Text |
id | pubmed-7320181 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-73201812020-06-30 Development of quantification software for evaluating body composition contents and its clinical application in sarcopenic obesity Kim, SeungJin Kim, Tae-Hoon Jeong, Chang-Won Lee, ChungSub Noh, SiHyeong Kim, Ji Eon Yoon, Kwon-Ha Sci Rep Article In sarcopenic obesity, the importance of evaluating muscle and fat mass is unquestionable. There exist diverse quantification methods for assessing muscle and fat mass by imaging techniques; thus these methods must be standardized for clinical practice. This study developed a quantification software for the body composition imaging using abdominal magnetic resonance (MR) images and compared the difference between sarcopenic obesity and healthy controls for clinical application. Thirty patients with sarcopenic obesity and 30 healthy controls participated. The quantification software was developed based on an ImageJ multiplatform and the processing steps are as follows: execution, setting, confirmation, and extraction. The variation in the muscle area (MA), subcutaneous fat area (SA), and visceral fat area (VA) was analyzed with an independent two sample T-test. There were significant differences in SA (p < 0.001) and VA (p = 0.011), whereas there was no difference in MA (p = 0.421). Regarding the ratios, there were significant differences in MA/SA (p < 0.001), MA/VA (p = 0.002), and MA/(SA + VA) (p < 0.001). Overall, intraclass correlation coefficients were higher than 0.9, indicating excellent reliability. This study developed customized sarcopenia-software for assessing body composition using abdominal MR images. The clinical findings demonstrate that the quantitative body composition areas and ratios can assist in the differential diagnosis of sarcopenic obesity or sarcopenia. Nature Publishing Group UK 2020-06-26 /pmc/articles/PMC7320181/ /pubmed/32591563 http://dx.doi.org/10.1038/s41598-020-67461-0 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Kim, SeungJin Kim, Tae-Hoon Jeong, Chang-Won Lee, ChungSub Noh, SiHyeong Kim, Ji Eon Yoon, Kwon-Ha Development of quantification software for evaluating body composition contents and its clinical application in sarcopenic obesity |
title | Development of quantification software for evaluating body composition contents and its clinical application in sarcopenic obesity |
title_full | Development of quantification software for evaluating body composition contents and its clinical application in sarcopenic obesity |
title_fullStr | Development of quantification software for evaluating body composition contents and its clinical application in sarcopenic obesity |
title_full_unstemmed | Development of quantification software for evaluating body composition contents and its clinical application in sarcopenic obesity |
title_short | Development of quantification software for evaluating body composition contents and its clinical application in sarcopenic obesity |
title_sort | development of quantification software for evaluating body composition contents and its clinical application in sarcopenic obesity |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7320181/ https://www.ncbi.nlm.nih.gov/pubmed/32591563 http://dx.doi.org/10.1038/s41598-020-67461-0 |
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