ArticleAbdominal radiology (New York)2024
Comparing fully automated AI body composition measures derived from thin and thick slice CT image data.
Article in Abdominal radiology (New York), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Defining Reference Values for Skeletal Muscle Metrics on Abdominal CT Using Data From Healthy Young Adult Populations: A Systematic Review and Meta-Analysis.AJR. American journal of roentgenology · 2025Pooled it
- Article
- Longitudinal evaluation of intra-patient changes in computed tomography-based body composition measures.Abdominal radiology (New York) · 2026Article
- Automated Deep Learning Detection of Hepatic Steatosis on Noncontrast Computed Tomography Scans and Discrepancy With Scan Reports.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2026Article
- Computed Tomography-Derived visceral fat area is associated with increased risk of kidney stone recurrence.Urolithiasis · 2026Article
- Quantification of Abdominal Aortic Calcification on CT: Clinical Validation for Assessment Of Cardiovascular Risk in Oncology.Journal of imaging informatics in medicine · 2026Article
- Ratio of visceral-to-subcutaneous fat area improves long-term mortality prediction over either measure alone: automated CT-based AI measures with longitudinal follow-up in a large adult cohort.Abdominal radiology (New York) · 2026Article
- Influence of CT parameters upon the quantification of sarcopenia and myosteatosis: a study with human bodies.Scientific reports · 2026Article
- Artificial intelligence standardizes CT-based body composition analysis in breast cacer to address methodological heterogeneity.Frontiers in oncology · 2026Review
- Effects of Computed Tomography Technical Parameters on Body-Composition Analysis.Korean journal of radiology · 2025Review
- Methodology for a fully automated pipeline of AI-based body composition tools for abdominal CT.Abdominal radiology (New York) · 2025Article
- AI-enabled body composition biomarkers at post-mortem CT for enriching autopsy: analysis of a large decedent cohort.Abdominal radiology (New York) · 2025Article
- Automated abdominal CT imaging biomarkers and clinical frailty measures associated with postoperative deceased-donor liver transplant outcomes.European radiology · 2025Article
- Opportunistic prognostication by computerized tomography (CT) in the emergency department: analysis on 1920 patients and creation of a simple and fast scoring system.La Radiologia medica · 2025Article
- Opportunistic Screening of Bone Fragility Using Computed Tomography.Seminars in musculoskeletal radiology · 2024Review
- Enhanced muscle and fat segmentation for CT-based body composition analysis: a comparative study.International journal of computer assisted radiology and surgery · 2024Article
- Adjusting for the effect of IV contrast on automated CT body composition measures during the portal venous phase.Abdominal radiology (New York) · 2024Article
- Enhanced Muscle and Fat Segmentation for CT-Based Body Composition Analysis: A Comparative Study.ArXiv · 2024Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
purposeTo compare fully automated artificial intelligence body composition measures derived from thin (1.25 mm) and thick (5 mm) slice abdominal CT data.
methodsIn this retrospective study, fully automated CT-based body composition algorithms for quantifying bone attenuation, muscle attenuation, muscle area, liver attenuation, liver volume, spleen volume, visceral-to-subcutaneous fat ratio (VSR) and aortic calcium were applied to both thin (1.25 × 0.625 mm) and thick (5 × 3 mm) abdominal CT series from two patient cohorts: unenhanced scans in asymptomatic adults undergoing colorectal cancer screening, and post-contrast scans in patients with colorectal cancer. Body composition measures derived from thin and thick slice data were compared, including correlation coefficients and Bland-Altman analysis.
resultsA total of 9882 CT scans (mean age, 57.0 years; 4527 women, 5355 men) were evaluated, including 8947 non-contrast and 935 contrast-enhanced CT exams. Very strong positive correlation was observed for all soft tissue measures: muscle attenuation (r
conclusionAutomated body composition measures derived from thin and thick abdominal CT data are strongly correlated and show agreement, particularly for soft tissue applications, making it feasible to use either series for these CT-based body composition algorithms.
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Registered trials
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