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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

Overview of attention for article published in PeerJ, May 2017
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Title
Body composition estimation from selected slices: equations computed from a new semi-automatic thresholding method developed on whole-body CT scans
Published in
PeerJ, May 2017
DOI 10.7717/peerj.3302
Pubmed ID
Authors

Alizé Lacoste Jeanson, Ján Dupej, Chiara Villa, Jaroslav Brůžek

Abstract

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. 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). 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.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 50 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 10 20%
Student > Ph. D. Student 5 10%
Researcher 5 10%
Student > Postgraduate 4 8%
Student > Master 4 8%
Other 11 22%
Unknown 11 22%
Readers by discipline Count As %
Medicine and Dentistry 14 28%
Nursing and Health Professions 5 10%
Agricultural and Biological Sciences 3 6%
Biochemistry, Genetics and Molecular Biology 2 4%
Social Sciences 2 4%
Other 7 14%
Unknown 17 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 May 2017.
All research outputs
#20,421,487
of 22,973,051 outputs
Outputs from PeerJ
#11,983
of 13,381 outputs
Outputs of similar age
#273,097
of 313,772 outputs
Outputs of similar age from PeerJ
#349
of 359 outputs
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