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 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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:12 |
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| Enthalten in: |
Journal of clinical medicine - 12(2023), 6 vom: 08. März |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Iyer, Kartik [VerfasserIn] |
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| Links: |
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| Themen: |
Body composition |
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| Anmerkungen: |
Date Revised 04.11.2023 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3390/jcm12062106 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a CONCLUSIONS: Body composition features derived from preoperative CT scans should be considered when predicting survival following esophagectomy | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a body composition | |
| 650 | 4 | |a esophageal cancer | |
| 650 | 4 | |a esophagectomy | |
| 650 | 4 | |a radiomics | |
| 650 | 4 | |a survival | |
| 700 | 1 | |a Beeche, Cameron A |e verfasserin |4 aut | |
| 700 | 1 | |a Gezer, Naciye S |e verfasserin |4 aut | |
| 700 | 1 | |a Leader, Joseph K |e verfasserin |4 aut | |
| 700 | 1 | |a Ren, Shangsi |e verfasserin |4 aut | |
| 700 | 1 | |a Dhupar, Rajeev |e verfasserin |4 aut | |
| 700 | 1 | |a Pu, Jiantao |e verfasserin |4 aut | |
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