ArticleJournal of clinical medicine2023
CT-Derived Body Composition Is a Predictor of Survival after Esophagectomy.
Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 11 citations in OpenAlex.
- Postoperative changes in body composition after laparoscopic and open resection of colorectal liver metastases: data from the randomized OSLO-COMET trial.Surgical endoscopy · 2025Trial
- Beyond nodules: body composition as a biomarker for future lung cancer.European radiology · 2025Article
- Predicting Primary Graft Dysfunction in Systemic Sclerosis Lung Transplantation Using Machine-Learning and CT Features.Clinical transplantation · 2025Article
- Computed tomography-based body composition and systemic inflammation for outcome prediction in patients with resectable esophageal squamous cell carcinoma.Journal of thoracic disease · 2025Article
- Predicting post-lung transplant survival in systemic sclerosis using CT-derived features from preoperative chest CT scans.European radiology · 2025Article
- CT-Based Lung Size Matching in Delayed Chest Closure for Systemic Sclerosis Lung Transplantation.Clinical transplantation · 2024Article
- IntegratingBMC cancer · 2024Article
- Association Between Body Composition and Survival in Patients With Gastroesophageal Adenocarcinoma: An Automated Deep Learning Approach.JCO clinical cancer informatics · 2024Article
- Graphical modeling of causal factors associated with the postoperative survival of esophageal cancer subjects.Medical physics · 2024Article
- Fully automated 3D body composition analysis and its association with overall survival in head and neck squamous cell carcinoma patients.Frontiers in oncology · 2023Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
backgroundBody 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.
methodsWe 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.
resultsThe 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.
conclusionsBody composition features derived from preoperative CT scans should be considered when predicting survival following esophagectomy.
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