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Body composition estimation from selected slices: equations computed from a new semi-automatic thresholding method developed on whole-body CT scans
BACKGROUND: Estimating volumes and masses of total body components is important for the study and treatment monitoring of nutrition and nutrition-related disorders, cancer, joint replacement, energy-expenditure and exercise physiology. While several equations have been offered for estimating total b...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5438582/ https://www.ncbi.nlm.nih.gov/pubmed/28533960 http://dx.doi.org/10.7717/peerj.3302 |
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author | Lacoste Jeanson, Alizé Dupej, Ján Villa, Chiara Brůžek, Jaroslav |
author_facet | Lacoste Jeanson, Alizé Dupej, Ján Villa, Chiara Brůžek, Jaroslav |
author_sort | Lacoste Jeanson, Alizé |
collection | PubMed |
description | BACKGROUND: Estimating volumes and masses of total body components is important for the study and treatment monitoring of nutrition and nutrition-related disorders, cancer, joint replacement, energy-expenditure and exercise physiology. While several equations have been offered for estimating total body components from MRI slices, no reliable and tested method exists for CT scans. For the first time, body composition data was derived from 41 high-resolution whole-body CT scans. From these data, we defined equations for estimating volumes and masses of total body AT and LT from corresponding tissue areas measured in selected CT scan slices. METHODS: We present a new semi-automatic approach to defining the density cutoff between adipose tissue (AT) and lean tissue (LT) in such material. An intra-class correlation coefficient (ICC) was used to validate the method. The equations for estimating the whole-body composition volume and mass from areas measured in selected slices were modeled with ordinary least squares (OLS) linear regressions and support vector machine regression (SVMR). RESULTS AND DISCUSSION: The best predictive equation for total body AT volume was based on the AT area of a single slice located between the 4th and 5th lumbar vertebrae (L4-L5) and produced lower prediction errors (|PE| = 1.86 liters, %PE = 8.77) than previous equations also based on CT scans. The LT area of the mid-thigh provided the lowest prediction errors (|PE| = 2.52 liters, %PE = 7.08) for estimating whole-body LT volume. We also present equations to predict total body AT and LT masses from a slice located at L4-L5 that resulted in reduced error compared with the previously published equations based on CT scans. The multislice SVMR predictor gave the theoretical upper limit for prediction precision of volumes and cross-validated the results. |
format | Online Article Text |
id | pubmed-5438582 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-54385822017-05-22 Body composition estimation from selected slices: equations computed from a new semi-automatic thresholding method developed on whole-body CT scans Lacoste Jeanson, Alizé Dupej, Ján Villa, Chiara Brůžek, Jaroslav PeerJ Nutrition BACKGROUND: Estimating volumes and masses of total body components is important for the study and treatment monitoring of nutrition and nutrition-related disorders, cancer, joint replacement, energy-expenditure and exercise physiology. While several equations have been offered for estimating total body components from MRI slices, no reliable and tested method exists for CT scans. For the first time, body composition data was derived from 41 high-resolution whole-body CT scans. From these data, we defined equations for estimating volumes and masses of total body AT and LT from corresponding tissue areas measured in selected CT scan slices. METHODS: We present a new semi-automatic approach to defining the density cutoff between adipose tissue (AT) and lean tissue (LT) in such material. An intra-class correlation coefficient (ICC) was used to validate the method. The equations for estimating the whole-body composition volume and mass from areas measured in selected slices were modeled with ordinary least squares (OLS) linear regressions and support vector machine regression (SVMR). RESULTS AND DISCUSSION: The best predictive equation for total body AT volume was based on the AT area of a single slice located between the 4th and 5th lumbar vertebrae (L4-L5) and produced lower prediction errors (|PE| = 1.86 liters, %PE = 8.77) than previous equations also based on CT scans. The LT area of the mid-thigh provided the lowest prediction errors (|PE| = 2.52 liters, %PE = 7.08) for estimating whole-body LT volume. We also present equations to predict total body AT and LT masses from a slice located at L4-L5 that resulted in reduced error compared with the previously published equations based on CT scans. The multislice SVMR predictor gave the theoretical upper limit for prediction precision of volumes and cross-validated the results. PeerJ Inc. 2017-05-18 /pmc/articles/PMC5438582/ /pubmed/28533960 http://dx.doi.org/10.7717/peerj.3302 Text en ©2017 Lacoste Jeanson et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Nutrition Lacoste Jeanson, Alizé Dupej, Ján Villa, Chiara Brůžek, Jaroslav Body composition estimation from selected slices: equations computed from a new semi-automatic thresholding method developed on whole-body CT scans |
title | Body composition estimation from selected slices: equations computed from a new semi-automatic thresholding method developed on whole-body CT scans |
title_full | Body composition estimation from selected slices: equations computed from a new semi-automatic thresholding method developed on whole-body CT scans |
title_fullStr | Body composition estimation from selected slices: equations computed from a new semi-automatic thresholding method developed on whole-body CT scans |
title_full_unstemmed | Body composition estimation from selected slices: equations computed from a new semi-automatic thresholding method developed on whole-body CT scans |
title_short | Body composition estimation from selected slices: equations computed from a new semi-automatic thresholding method developed on whole-body CT scans |
title_sort | body composition estimation from selected slices: equations computed from a new semi-automatic thresholding method developed on whole-body ct scans |
topic | Nutrition |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5438582/ https://www.ncbi.nlm.nih.gov/pubmed/28533960 http://dx.doi.org/10.7717/peerj.3302 |
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