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Estimating 3‐D whole‐body composition from a chest CT scan
BACKGROUND: Estimating whole‐body composition from limited region‐computed tomography (CT) scans has many potential applications in clinical medicine; however, it is challenging. PURPOSE: To investigate if whole‐body composition based on several tissue types (visceral adipose tissue [VAT], subcutane...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10084085/ https://www.ncbi.nlm.nih.gov/pubmed/35737963 http://dx.doi.org/10.1002/mp.15821 |
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author | Pu, Lucy Ashraf, Syed F. Gezer, Naciye S. Ocak, Iclal Dresser, Daniel E. Leader, Joseph K. Dhupar, Rajeev |
author_facet | Pu, Lucy Ashraf, Syed F. Gezer, Naciye S. Ocak, Iclal Dresser, Daniel E. Leader, Joseph K. Dhupar, Rajeev |
author_sort | Pu, Lucy |
collection | PubMed |
description | BACKGROUND: Estimating whole‐body composition from limited region‐computed tomography (CT) scans has many potential applications in clinical medicine; however, it is challenging. PURPOSE: To investigate if whole‐body composition based on several tissue types (visceral adipose tissue [VAT], subcutaneous adipose tissue [SAT], intermuscular adipose tissue [IMAT], skeletal muscle [SM], and bone) can be reliably estimated from a chest CT scan only. METHODS: A cohort of 97 lung cancer subjects who underwent both chest CT scans and whole‐body positron emission tomography‐CT scans at our institution were collected. We used our in‐house software to automatically segment and quantify VAT, SAT, IMAT, SM, and bone on the CT images. The field‐of‐views of the chest CT scans and the whole‐body CT scans were standardized, namely, from vertebra T1 to L1 and from C1 to the bottom of the pelvis, respectively. Multivariate linear regression was used to develop the computer models for estimating the volumes of whole‐body tissues from chest CT scans. Subject demographics (e.g., gender and age) and lung volume were included in the modeling analysis. Ten‐fold cross‐validation was used to validate the performance of the prediction models. Mean absolute difference (MAD) and R‐squared (R (2)) were used as the performance metrics to assess the model performance. RESULTS: The R (2) values when estimating volumes of whole‐body SAT, VAT, IMAT, total fat, SM, and bone from the regular chest CT scans were 0.901, 0.929, 0.900, 0.933, 0.928, and 0.918, respectively. The corresponding MADs (percentage difference) were 1.44 ± 1.21 L (12.21% ± 11.70%), 0.63 ± 0.49 L (29.68% ± 61.99%), 0.12 ± 0.09 L (16.20% ± 18.42%), 1.65 ± 1.40 L (10.43% ± 10.79%), 0.71 ± 0.68 L (5.14% ± 4.75%), and 0.17 ± 0.15 L (4.32% ± 3.38%), respectively. CONCLUSION: Our algorithm shows promise in its ability to estimate whole‐body compositions from chest CT scans. Body composition measures based on chest CT scans are more accurate than those based on vertebra third lumbar. |
format | Online Article Text |
id | pubmed-10084085 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100840852023-04-11 Estimating 3‐D whole‐body composition from a chest CT scan Pu, Lucy Ashraf, Syed F. Gezer, Naciye S. Ocak, Iclal Dresser, Daniel E. Leader, Joseph K. Dhupar, Rajeev Med Phys QUANTITATIVE IMAGING AND IMAGE PROCESSING BACKGROUND: Estimating whole‐body composition from limited region‐computed tomography (CT) scans has many potential applications in clinical medicine; however, it is challenging. PURPOSE: To investigate if whole‐body composition based on several tissue types (visceral adipose tissue [VAT], subcutaneous adipose tissue [SAT], intermuscular adipose tissue [IMAT], skeletal muscle [SM], and bone) can be reliably estimated from a chest CT scan only. METHODS: A cohort of 97 lung cancer subjects who underwent both chest CT scans and whole‐body positron emission tomography‐CT scans at our institution were collected. We used our in‐house software to automatically segment and quantify VAT, SAT, IMAT, SM, and bone on the CT images. The field‐of‐views of the chest CT scans and the whole‐body CT scans were standardized, namely, from vertebra T1 to L1 and from C1 to the bottom of the pelvis, respectively. Multivariate linear regression was used to develop the computer models for estimating the volumes of whole‐body tissues from chest CT scans. Subject demographics (e.g., gender and age) and lung volume were included in the modeling analysis. Ten‐fold cross‐validation was used to validate the performance of the prediction models. Mean absolute difference (MAD) and R‐squared (R (2)) were used as the performance metrics to assess the model performance. RESULTS: The R (2) values when estimating volumes of whole‐body SAT, VAT, IMAT, total fat, SM, and bone from the regular chest CT scans were 0.901, 0.929, 0.900, 0.933, 0.928, and 0.918, respectively. The corresponding MADs (percentage difference) were 1.44 ± 1.21 L (12.21% ± 11.70%), 0.63 ± 0.49 L (29.68% ± 61.99%), 0.12 ± 0.09 L (16.20% ± 18.42%), 1.65 ± 1.40 L (10.43% ± 10.79%), 0.71 ± 0.68 L (5.14% ± 4.75%), and 0.17 ± 0.15 L (4.32% ± 3.38%), respectively. CONCLUSION: Our algorithm shows promise in its ability to estimate whole‐body compositions from chest CT scans. Body composition measures based on chest CT scans are more accurate than those based on vertebra third lumbar. John Wiley and Sons Inc. 2022-07-11 2022-11 /pmc/articles/PMC10084085/ /pubmed/35737963 http://dx.doi.org/10.1002/mp.15821 Text en © 2022 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | QUANTITATIVE IMAGING AND IMAGE PROCESSING Pu, Lucy Ashraf, Syed F. Gezer, Naciye S. Ocak, Iclal Dresser, Daniel E. Leader, Joseph K. Dhupar, Rajeev Estimating 3‐D whole‐body composition from a chest CT scan |
title | Estimating 3‐D whole‐body composition from a chest CT scan |
title_full | Estimating 3‐D whole‐body composition from a chest CT scan |
title_fullStr | Estimating 3‐D whole‐body composition from a chest CT scan |
title_full_unstemmed | Estimating 3‐D whole‐body composition from a chest CT scan |
title_short | Estimating 3‐D whole‐body composition from a chest CT scan |
title_sort | estimating 3‐d whole‐body composition from a chest ct scan |
topic | QUANTITATIVE IMAGING AND IMAGE PROCESSING |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10084085/ https://www.ncbi.nlm.nih.gov/pubmed/35737963 http://dx.doi.org/10.1002/mp.15821 |
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