ArticleClinical nutrition (Edinburgh, Scotland)2020
Automated body composition analysis of clinically acquired computed tomography scans using neural networks.
Article in Clinical nutrition (Edinburgh, Scotland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
69 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Malnutrition, Frailty, and Sarcopenia in Patients With Cirrhosis: 2021 Practice Guidance by the American Association for the Study of Liver Diseases.Hepatology (Baltimore, Md.) · 2021Guideline
- Prognostic role of radiomics-based body composition analysis for the 1-year survival for hepatocellular carcinoma patients.Journal of cachexia, sarcopenia and muscle · 2023Trial
- AutoLMISeg: A Fully Automated Pipeline for 3D Lumbar Muscle Volumetry and Densitometry on CT - Technical Validation Against an Operator-Driven Workflow.Journal of imaging informatics in medicine · 2026Article
- Prognostic value of automated computed tomography body composition in hepatocellular carcinoma: a volumetric versus single-slice comparison.Journal of liver cancer · 2026Article
- Enhancing the accuracy of bioimpedance-derived appendicular skeletal muscle mass in aged adults through machine learning.European geriatric medicine · 2026Article
- A feasibility study of computed tomography muscle phenotyping for physiotherapy selection in peripheral arterial disease.Journal of vascular surgery cases and innovative techniques · 2026Article
- Predicting SIRS after PCNL using machine learning: the joint impact of sarcopenia and staghorn stones.World journal of urology · 2026Article
- Impact of data augmentation size on deep learning-based third lumbar vertebra computed tomography skeletal muscle segmentation performance.Quantitative imaging in medicine and surgery · 2026Article
- Development and external validation of a preoperative CT body composition-based model for predicting postoperative metastasis in colorectal cancer: a multicenter retrospective cohort study.Abdominal radiology (New York) · 2026Article
- Anemia-Driven Phenotypes in Lung Cancer: Linking Inflammation and Sarcopenia.Diagnostics (Basel, Switzerland) · 2026Article
- Technical performance of L3 skeletal muscle area (SMA) measurement on CT for L3 skeletal muscle index (L3SMI) assessment.Scientific reports · 2026Article
- Malnutrition and diabetes outcomes 12 months after total pancreatectomy: A descriptive cohort study.JPEN. Journal of parenteral and enteral nutrition · 2026Article
- CT-derived body composition as a predictor of supervised exercise therapy response in intermittent claudication and peripheral arterial disease: COMPOSE-PAD study protocol.Biomolecules & biomedicine · 2026Article
- Reassessing prognostic markers in metastatic renal cell carcinoma in the era of immune checkpoint inhibitors: the enduring value of body composition, nutritional, and inflammatory indices.International journal of clinical oncology · 2026Article
- Exploration of an interpretable machine learning-based screening manner for low muscle mass among Chinese community-dwelling older adults using routine physical examination information.BMC geriatrics · 2026Article
- Intravenous contrast medium impairs CT-based muscle quality but not quantity assessment: a translational study.Frontiers in radiology · 2026Article
- Artificial intelligence standardizes CT-based body composition analysis in breast cacer to address methodological heterogeneity.Frontiers in oncology · 2026Review
- Independent validation of the Mosamatic deep learning automated skeletal muscle and adipose tissue segmentation tool in an external Chinese cancer patient cohort.BJR artificial intelligence · 2026Article
- Machine learning based on body composition radiomics for predicting early recurrence in colorectal cancer: a multicenter study.Frontiers in nutrition · 2026Article
- Post-Operative Atrial Fibrillation: Novel Predictive Value of CT-Derived Adipose Tissue Density in Minimally Invasive Mitral Surgery.Physiological research · 2025Article
9 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
BACKGROUND &
aimsThe quantity and quality of skeletal muscle and adipose tissue is an important prognostic factor for clinical outcomes across several illnesses. Clinically acquired computed tomography (CT) scans are commonly used for quantification of body composition, but manual analysis is laborious and costly. The primary aim of this study was to develop an automated body composition analysis framework using CT scans.
methodsCT scans of the 3rd lumbar vertebrae from critically ill, liver cirrhosis, pancreatic cancer, and clear cell renal cell carcinoma patients, as well as renal and liver donors, were manually analyzed for body composition. Ninety percent of scans were used for developing and validating a neural network for the automated segmentation of skeletal muscle and adipose tissues. Network accuracy was evaluated with the remaining 10 percent of scans using the Dice similarity coefficient (DSC), which quantifies the overlap (0 = no overlap, 1 = perfect overlap) between human and automated segmentations.
resultsOf the 893 patients, 44% were female, with a mean (±SD) age and body mass index of 52.7 (±15.8) years old and 28.0 (±6.1) kg/m
conclusionsOur network displayed excellent ability to analyze diverse body composition phenotypes and clinical cohorts, which will create feasible opportunities to advance our capacity to predict health outcomes in clinical populations.
Indexed as
Identifiers
What OpenQuestion holds
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.