↓ Skip to main content

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
Altmetric Badge

Mentioned by

twitter
1 tweeter
facebook
1 Facebook page

Citations

dimensions_citation
13 Dimensions

Readers on

mendeley
37 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
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.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 8 22%
Student > Ph. D. Student 5 14%
Student > Doctoral Student 4 11%
Student > Master 4 11%
Researcher 3 8%
Other 9 24%
Unknown 4 11%
Readers by discipline Count As %
Medicine and Dentistry 10 27%
Nursing and Health Professions 5 14%
Agricultural and Biological Sciences 3 8%
Biochemistry, Genetics and Molecular Biology 2 5%
Social Sciences 2 5%
Other 6 16%
Unknown 9 24%

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 19 May 2017.
All research outputs
#7,893,610
of 10,490,471 outputs
Outputs from PeerJ
#4,000
of 4,739 outputs
Outputs of similar age
#172,362
of 263,569 outputs
Outputs of similar age from PeerJ
#294
of 358 outputs
Altmetric has tracked 10,490,471 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,739 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 11th percentile – i.e., 11% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 263,569 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 358 others from the same source and published within six weeks on either side of this one. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.