Body composition estimation from selected slices: equations computed from a new semi-automatic thresholding method developed on whole-body CT scans

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F17%3A10367099" target="_blank" >RIV/00216208:11310/17:10367099 - isvavai.cz</a>

Alternative codes found

RIV/00216208:11320/17:10367099

Result on the web

<a href="http://dx.doi.org/10.7717/peerj.3302" target="_blank" >http://dx.doi.org/10.7717/peerj.3302</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.7717/peerj.3302" target="_blank" >10.7717/peerj.3302</a>

Result language

angličtina

Original language name

Body composition estimation from selected slices: equations computed from a new semi-automatic thresholding method developed on whole-body CT scans

Original language description

Back ground. 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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10600 - Biological sciences

Project

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Continuities

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

PeerJ

ISSN

2167-8359

e-ISSN

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Volume of the periodical

5

Issue of the periodical within the volume

may

Country of publishing house

US - UNITED STATES

Number of pages

18

Pages from-to

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UT code for WoS article

000401846100003

EID of the result in the Scopus database

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