ArticleMedical physics2022
Estimating 3-D whole-body composition from a chest CT scan.
Article in Medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- A systematic review of automated segmentation of 3D computed-tomography scans for volumetric body composition analysis.Journal of cachexia, sarcopenia and muscle · 2023Pooled it
- Volumetric assessment from L2 to L4 improves the reliability and validity of body composition by CT.European journal of radiology · 2026Article
- Body composition's effect on the bone-vascular axis of osteoporosis discovered in AI-based CT analysis of COPD patients.European radiology · 2026Observational
- Prognostic value of body composition analysis in cancer patients treated with immunotherapy: a systematic evaluation of TotalSegmentator-based biomarkers.La Radiologia medica · 2026Article
- CT-Assessed Body Composition as Predictor of Post-Operative Complications in Lung Cancer Patients.Cancers · 2026Review
- Chest CT muscle Volume + Body weight: a two-parameter index to operationalize sarcopenia screening in older inpatients.Aging clinical and experimental research · 2025Article
- 3D deep learning-based muscle volume quantification from thoracic CT as a surrogate for DXA-Derived appendicular muscle mass in older adults.Aging clinical and experimental research · 2025Article
- Article
- Predicting Primary Graft Dysfunction in Systemic Sclerosis Lung Transplantation Using Machine-Learning and CT Features.Clinical transplantation · 2025Article
- Predicting post-lung transplant survival in systemic sclerosis using CT-derived features from preoperative chest CT scans.European radiology · 2025Article
- A generalized health index: automated thoracic CT-derived biomarkers predict life expectancy.The British journal of radiology · 2025Article
- Review
- Synergic value of 3D CT-derived body composition and triglyceride glucose body mass for survival prognostic modeling in unresectable pancreatic cancer.Frontiers in nutrition · 2025Article
- CT-Based Lung Size Matching in Delayed Chest Closure for Systemic Sclerosis Lung Transplantation.Clinical transplantation · 2024Article
- CT-Derived Features as Predictors of Clot Burden and Resolution.Bioengineering (Basel, Switzerland) · 2024Article
- Elexacaftor/tezacaftor/ivacaftor influences body composition in adults with cystic fibrosis: a fully automated CT-based analysis.Scientific reports · 2024Article
- Synergic prognostic value of 3D CT scan subcutaneous fat and muscle masses for immunotherapy-treated cancer.Journal for immunotherapy of cancer · 2023Article
- Article
- CT-derived body composition associated with lung cancer recurrence after surgery.Lung cancer (Amsterdam, Netherlands) · 2023Article
- CT-Derived Body Composition Is a Predictor of Survival after Esophagectomy.Journal of clinical medicine · 2023Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundEstimating 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.
methodsA 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
resultsThe R
conclusionOur 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.