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CT-Derived Body Composition Is a Predictor of Survival after Esophagectomy
Background: Body composition can be accurately quantified based on computed tomography (CT) and typically reflects an individual’s overall health status. However, there is a dearth of research examining the relationship between body composition and survival following esophagectomy. Methods: We creat...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10058526/ https://www.ncbi.nlm.nih.gov/pubmed/36983109 http://dx.doi.org/10.3390/jcm12062106 |
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author | Iyer, Kartik Beeche, Cameron A. Gezer, Naciye S. Leader, Joseph K. Ren, Shangsi Dhupar, Rajeev Pu, Jiantao |
author_facet | Iyer, Kartik Beeche, Cameron A. Gezer, Naciye S. Leader, Joseph K. Ren, Shangsi Dhupar, Rajeev Pu, Jiantao |
author_sort | Iyer, Kartik |
collection | PubMed |
description | Background: Body composition can be accurately quantified based on computed tomography (CT) and typically reflects an individual’s overall health status. However, there is a dearth of research examining the relationship between body composition and survival following esophagectomy. Methods: We created a cohort consisting of 183 patients who underwent esophagectomy for esophageal cancer without neoadjuvant therapy. The cohort included preoperative PET-CT scans, along with pathologic and clinical data, which were collected prospectively. Radiomic, tumor, PET, and body composition features were automatically extracted from the images. Cox regression models were utilized to identify variables associated with survival. Logistic regression and machine learning models were developed to predict one-, three-, and five-year survival rates. Model performance was evaluated based on the area under the receiver operating characteristics curve (ROC/AUC). To test for the statistical significance of the impact of body composition on survival, body composition features were excluded for the best-performing models, and the DeLong test was used. Results: The one-year survival model contained 10 variables, including three body composition variables (bone mass, bone density, and visceral adipose tissue (VAT) density), and demonstrated an AUC of 0.817 (95% CI: 0.738–0.897). The three-year survival model incorporated 14 variables, including three body composition variables (intermuscular adipose tissue (IMAT) volume, IMAT mass, and bone mass), with an AUC of 0.693 (95% CI: 0.594–0.792). For the five-year survival model, 10 variables were included, of which two were body composition variables (intramuscular adipose tissue (IMAT) volume and visceral adipose tissue (VAT) mass), with an AUC of 0.861 (95% CI: 0.783–0.938). The one- and five-year survival models exhibited significantly inferior performance when body composition features were not incorporated. Conclusions: Body composition features derived from preoperative CT scans should be considered when predicting survival following esophagectomy. |
format | Online Article Text |
id | pubmed-10058526 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100585262023-03-30 CT-Derived Body Composition Is a Predictor of Survival after Esophagectomy Iyer, Kartik Beeche, Cameron A. Gezer, Naciye S. Leader, Joseph K. Ren, Shangsi Dhupar, Rajeev Pu, Jiantao J Clin Med Article Background: Body composition can be accurately quantified based on computed tomography (CT) and typically reflects an individual’s overall health status. However, there is a dearth of research examining the relationship between body composition and survival following esophagectomy. Methods: We created a cohort consisting of 183 patients who underwent esophagectomy for esophageal cancer without neoadjuvant therapy. The cohort included preoperative PET-CT scans, along with pathologic and clinical data, which were collected prospectively. Radiomic, tumor, PET, and body composition features were automatically extracted from the images. Cox regression models were utilized to identify variables associated with survival. Logistic regression and machine learning models were developed to predict one-, three-, and five-year survival rates. Model performance was evaluated based on the area under the receiver operating characteristics curve (ROC/AUC). To test for the statistical significance of the impact of body composition on survival, body composition features were excluded for the best-performing models, and the DeLong test was used. Results: The one-year survival model contained 10 variables, including three body composition variables (bone mass, bone density, and visceral adipose tissue (VAT) density), and demonstrated an AUC of 0.817 (95% CI: 0.738–0.897). The three-year survival model incorporated 14 variables, including three body composition variables (intermuscular adipose tissue (IMAT) volume, IMAT mass, and bone mass), with an AUC of 0.693 (95% CI: 0.594–0.792). For the five-year survival model, 10 variables were included, of which two were body composition variables (intramuscular adipose tissue (IMAT) volume and visceral adipose tissue (VAT) mass), with an AUC of 0.861 (95% CI: 0.783–0.938). The one- and five-year survival models exhibited significantly inferior performance when body composition features were not incorporated. Conclusions: Body composition features derived from preoperative CT scans should be considered when predicting survival following esophagectomy. MDPI 2023-03-08 /pmc/articles/PMC10058526/ /pubmed/36983109 http://dx.doi.org/10.3390/jcm12062106 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Iyer, Kartik Beeche, Cameron A. Gezer, Naciye S. Leader, Joseph K. Ren, Shangsi Dhupar, Rajeev Pu, Jiantao CT-Derived Body Composition Is a Predictor of Survival after Esophagectomy |
title | CT-Derived Body Composition Is a Predictor of Survival after Esophagectomy |
title_full | CT-Derived Body Composition Is a Predictor of Survival after Esophagectomy |
title_fullStr | CT-Derived Body Composition Is a Predictor of Survival after Esophagectomy |
title_full_unstemmed | CT-Derived Body Composition Is a Predictor of Survival after Esophagectomy |
title_short | CT-Derived Body Composition Is a Predictor of Survival after Esophagectomy |
title_sort | ct-derived body composition is a predictor of survival after esophagectomy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10058526/ https://www.ncbi.nlm.nih.gov/pubmed/36983109 http://dx.doi.org/10.3390/jcm12062106 |
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