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Body composition determinants of radiation dose during abdominopelvic CT
OBJECTIVES: We designed a prospective study to investigate the in-vivo relationship between abdominal body composition and radiation exposure to determine the strongest body composition predictor of dose length product (DLP) at CT. METHODS: Following institutional review board approval, quantitative...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5825306/ https://www.ncbi.nlm.nih.gov/pubmed/29063481 http://dx.doi.org/10.1007/s13244-017-0577-y |
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author | McLaughlin, Patrick D. Chawke, Liam Twomey, Maria Murphy, Kevin P. O’Neill, Siobhán B. McWilliams, Sebastian R. James, Karl Kavanagh, Richard G. Sullivan, Charles Chan, Faimee E. Moore, Niamh O’Connor, Owen J. Eustace, Joseph A. Maher, Michael M. |
author_facet | McLaughlin, Patrick D. Chawke, Liam Twomey, Maria Murphy, Kevin P. O’Neill, Siobhán B. McWilliams, Sebastian R. James, Karl Kavanagh, Richard G. Sullivan, Charles Chan, Faimee E. Moore, Niamh O’Connor, Owen J. Eustace, Joseph A. Maher, Michael M. |
author_sort | McLaughlin, Patrick D. |
collection | PubMed |
description | OBJECTIVES: We designed a prospective study to investigate the in-vivo relationship between abdominal body composition and radiation exposure to determine the strongest body composition predictor of dose length product (DLP) at CT. METHODS: Following institutional review board approval, quantitative analysis was performed prospectively on 239 consecutive patients who underwent abdominopelvic CT. DLP, BMI, volumes of abdominal adipose tissue, muscle, bone and solid organs were recorded. RESULTS: All measured body composition parameters correlated positively with DLP. Linear regression (R(2) = 0.77) revealed that total adipose volume was the strongest predictor of radiation exposure [B (95% CI) = 0.027(0.024–0.030), t=23.068, p < 0.001]. Stepwise linear regression using DLP as the dependent and BMI and total adipose tissue as independent variables demonstrated that total adipose tissue is more predictive of DLP than BMI [B (95% CI) = 16.045 (11.337-20.752), t=6.681, p < 0.001]. CONCLUSIONS: The volume of adipose tissue was the strongest predictor of radiation exposure in our cohort. MAIN MESSAGE: • Individual body composition variables correlate with DLP at abdominopelvic CT. • Total abdominal adipose tissue is the strongest predictor of radiation exposure. • Muscle volume is also a significant but weaker predictor of DLP. |
format | Online Article Text |
id | pubmed-5825306 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-58253062018-02-27 Body composition determinants of radiation dose during abdominopelvic CT McLaughlin, Patrick D. Chawke, Liam Twomey, Maria Murphy, Kevin P. O’Neill, Siobhán B. McWilliams, Sebastian R. James, Karl Kavanagh, Richard G. Sullivan, Charles Chan, Faimee E. Moore, Niamh O’Connor, Owen J. Eustace, Joseph A. Maher, Michael M. Insights Imaging Original Article OBJECTIVES: We designed a prospective study to investigate the in-vivo relationship between abdominal body composition and radiation exposure to determine the strongest body composition predictor of dose length product (DLP) at CT. METHODS: Following institutional review board approval, quantitative analysis was performed prospectively on 239 consecutive patients who underwent abdominopelvic CT. DLP, BMI, volumes of abdominal adipose tissue, muscle, bone and solid organs were recorded. RESULTS: All measured body composition parameters correlated positively with DLP. Linear regression (R(2) = 0.77) revealed that total adipose volume was the strongest predictor of radiation exposure [B (95% CI) = 0.027(0.024–0.030), t=23.068, p < 0.001]. Stepwise linear regression using DLP as the dependent and BMI and total adipose tissue as independent variables demonstrated that total adipose tissue is more predictive of DLP than BMI [B (95% CI) = 16.045 (11.337-20.752), t=6.681, p < 0.001]. CONCLUSIONS: The volume of adipose tissue was the strongest predictor of radiation exposure in our cohort. MAIN MESSAGE: • Individual body composition variables correlate with DLP at abdominopelvic CT. • Total abdominal adipose tissue is the strongest predictor of radiation exposure. • Muscle volume is also a significant but weaker predictor of DLP. Springer Berlin Heidelberg 2017-10-23 /pmc/articles/PMC5825306/ /pubmed/29063481 http://dx.doi.org/10.1007/s13244-017-0577-y Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Article McLaughlin, Patrick D. Chawke, Liam Twomey, Maria Murphy, Kevin P. O’Neill, Siobhán B. McWilliams, Sebastian R. James, Karl Kavanagh, Richard G. Sullivan, Charles Chan, Faimee E. Moore, Niamh O’Connor, Owen J. Eustace, Joseph A. Maher, Michael M. Body composition determinants of radiation dose during abdominopelvic CT |
title | Body composition determinants of radiation dose during abdominopelvic CT |
title_full | Body composition determinants of radiation dose during abdominopelvic CT |
title_fullStr | Body composition determinants of radiation dose during abdominopelvic CT |
title_full_unstemmed | Body composition determinants of radiation dose during abdominopelvic CT |
title_short | Body composition determinants of radiation dose during abdominopelvic CT |
title_sort | body composition determinants of radiation dose during abdominopelvic ct |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5825306/ https://www.ncbi.nlm.nih.gov/pubmed/29063481 http://dx.doi.org/10.1007/s13244-017-0577-y |
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