Cargando…

Pediatric body composition based on automatic segmentation of computed tomography scans: a pilot study

BACKGROUND: Body composition during childhood may predispose to negative health outcomes later in life. Automatic segmentation may assist in quantifying pediatric body composition in children. OBJECTIVE: To evaluate automatic segmentation for body composition on pediatric computed tomography (CT) sc...

Descripción completa

Detalles Bibliográficos
Autores principales: Samim, Atia, Spijkers, Suzanne, Moeskops, Pim, Littooij, Annemieke S., de Jong, Pim A., Veldhuis, Wouter B., de Vos, Bob D., van Santen, Hanneke M., Nievelstein, Rutger A. J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10635977/
https://www.ncbi.nlm.nih.gov/pubmed/37640800
http://dx.doi.org/10.1007/s00247-023-05739-x
_version_ 1785146397890183168
author Samim, Atia
Spijkers, Suzanne
Moeskops, Pim
Littooij, Annemieke S.
de Jong, Pim A.
Veldhuis, Wouter B.
de Vos, Bob D.
van Santen, Hanneke M.
Nievelstein, Rutger A. J.
author_facet Samim, Atia
Spijkers, Suzanne
Moeskops, Pim
Littooij, Annemieke S.
de Jong, Pim A.
Veldhuis, Wouter B.
de Vos, Bob D.
van Santen, Hanneke M.
Nievelstein, Rutger A. J.
author_sort Samim, Atia
collection PubMed
description BACKGROUND: Body composition during childhood may predispose to negative health outcomes later in life. Automatic segmentation may assist in quantifying pediatric body composition in children. OBJECTIVE: To evaluate automatic segmentation for body composition on pediatric computed tomography (CT) scans and to provide normative data on muscle and fat areas throughout childhood using automatic segmentation. MATERIALS AND METHODS: In this pilot study, 537 children (ages 1–17 years) who underwent abdominal CT after high-energy trauma at a Dutch tertiary center (2002–2019) were retrospectively identified. Of these, the CT images of 493 children (66% boys) were used to establish normative data. Muscle (psoas, paraspinal and abdominal wall) and fat (subcutaneous and visceral) areas were measured at the third lumbar vertebral (L3) level by automatic segmentation. A representative subset of 52 scans was also manually segmented to evaluate the performance of automatic segmentation. RESULTS: For manually-segmented versus automatically-segmented areas (52 scans), mean Dice coefficients were high for muscle (0.87–0.90) and subcutaneous fat (0.88), but lower for visceral fat (0.60). In the control group, muscle area was comparable for both sexes until the age of 13 years, whereafter, boys developed relatively more muscle. From a young age, boys were more prone to visceral fat storage than girls. Overall, boys had significantly higher visceral-to-subcutaneous fat ratios (median 1.1 vs. 0.6, P<0.01) and girls higher fat-to-muscle ratios (median 1.0 vs. 0.7, P<0.01). CONCLUSION: Automatic segmentation of L3-level muscle and fat areas allows for accurate quantification of pediatric body composition. Using automatic segmentation, the development in muscle and fat distribution during childhood (in otherwise healthy) Dutch children was demonstrated. SUPPLEMENTARY INFORMATION: Supplementary material is available at 10.1007/s00247-023-05739-x.
format Online
Article
Text
id pubmed-10635977
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Springer Berlin Heidelberg
record_format MEDLINE/PubMed
spelling pubmed-106359772023-11-14 Pediatric body composition based on automatic segmentation of computed tomography scans: a pilot study Samim, Atia Spijkers, Suzanne Moeskops, Pim Littooij, Annemieke S. de Jong, Pim A. Veldhuis, Wouter B. de Vos, Bob D. van Santen, Hanneke M. Nievelstein, Rutger A. J. Pediatr Radiol Original Article BACKGROUND: Body composition during childhood may predispose to negative health outcomes later in life. Automatic segmentation may assist in quantifying pediatric body composition in children. OBJECTIVE: To evaluate automatic segmentation for body composition on pediatric computed tomography (CT) scans and to provide normative data on muscle and fat areas throughout childhood using automatic segmentation. MATERIALS AND METHODS: In this pilot study, 537 children (ages 1–17 years) who underwent abdominal CT after high-energy trauma at a Dutch tertiary center (2002–2019) were retrospectively identified. Of these, the CT images of 493 children (66% boys) were used to establish normative data. Muscle (psoas, paraspinal and abdominal wall) and fat (subcutaneous and visceral) areas were measured at the third lumbar vertebral (L3) level by automatic segmentation. A representative subset of 52 scans was also manually segmented to evaluate the performance of automatic segmentation. RESULTS: For manually-segmented versus automatically-segmented areas (52 scans), mean Dice coefficients were high for muscle (0.87–0.90) and subcutaneous fat (0.88), but lower for visceral fat (0.60). In the control group, muscle area was comparable for both sexes until the age of 13 years, whereafter, boys developed relatively more muscle. From a young age, boys were more prone to visceral fat storage than girls. Overall, boys had significantly higher visceral-to-subcutaneous fat ratios (median 1.1 vs. 0.6, P<0.01) and girls higher fat-to-muscle ratios (median 1.0 vs. 0.7, P<0.01). CONCLUSION: Automatic segmentation of L3-level muscle and fat areas allows for accurate quantification of pediatric body composition. Using automatic segmentation, the development in muscle and fat distribution during childhood (in otherwise healthy) Dutch children was demonstrated. SUPPLEMENTARY INFORMATION: Supplementary material is available at 10.1007/s00247-023-05739-x. Springer Berlin Heidelberg 2023-08-29 2023 /pmc/articles/PMC10635977/ /pubmed/37640800 http://dx.doi.org/10.1007/s00247-023-05739-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Samim, Atia
Spijkers, Suzanne
Moeskops, Pim
Littooij, Annemieke S.
de Jong, Pim A.
Veldhuis, Wouter B.
de Vos, Bob D.
van Santen, Hanneke M.
Nievelstein, Rutger A. J.
Pediatric body composition based on automatic segmentation of computed tomography scans: a pilot study
title Pediatric body composition based on automatic segmentation of computed tomography scans: a pilot study
title_full Pediatric body composition based on automatic segmentation of computed tomography scans: a pilot study
title_fullStr Pediatric body composition based on automatic segmentation of computed tomography scans: a pilot study
title_full_unstemmed Pediatric body composition based on automatic segmentation of computed tomography scans: a pilot study
title_short Pediatric body composition based on automatic segmentation of computed tomography scans: a pilot study
title_sort pediatric body composition based on automatic segmentation of computed tomography scans: a pilot study
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10635977/
https://www.ncbi.nlm.nih.gov/pubmed/37640800
http://dx.doi.org/10.1007/s00247-023-05739-x
work_keys_str_mv AT samimatia pediatricbodycompositionbasedonautomaticsegmentationofcomputedtomographyscansapilotstudy
AT spijkerssuzanne pediatricbodycompositionbasedonautomaticsegmentationofcomputedtomographyscansapilotstudy
AT moeskopspim pediatricbodycompositionbasedonautomaticsegmentationofcomputedtomographyscansapilotstudy
AT littooijannemiekes pediatricbodycompositionbasedonautomaticsegmentationofcomputedtomographyscansapilotstudy
AT dejongpima pediatricbodycompositionbasedonautomaticsegmentationofcomputedtomographyscansapilotstudy
AT veldhuiswouterb pediatricbodycompositionbasedonautomaticsegmentationofcomputedtomographyscansapilotstudy
AT devosbobd pediatricbodycompositionbasedonautomaticsegmentationofcomputedtomographyscansapilotstudy
AT vansantenhannekem pediatricbodycompositionbasedonautomaticsegmentationofcomputedtomographyscansapilotstudy
AT nievelsteinrutgeraj pediatricbodycompositionbasedonautomaticsegmentationofcomputedtomographyscansapilotstudy