ReviewRadiographics : a review publication of the Radiological Society of North America, Inc
Opportunistic Screening at Abdominal CT: Use of Automated Body Composition Biomarkers for Added Cardiometabolic Value.
Review in Radiographics : a review publication of the Radiological Society of North America, Inc. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 87 papers, 2 of them syntheses 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
87 citing papers in PubMed, 2 syntheses or guidelines pooled it, 123 citations in OpenAlex.
- The Correlation of Computed Tomography (CT)-Based Body Composition and Survival in Pancreatic Cancer Patients: A Systematic Review.Tomography (Ann Arbor, Mich.) · 2026Pooled it
- Body composition as a predictor of chemotherapy-related toxicity in ovarian cancer patients: A systematic review.Frontiers in oncology · 2022Pooled it
- Incidental Coronary Artery Calcium: Opportunistic Screening of Previous Nongated Chest Computed Tomography Scans to Improve Statin Rates (NOTIFY-1 Project).Circulation · 2023Trial
- Volumetric assessment from L2 to L4 improves the reliability and validity of body composition by CT.European journal of radiology · 2026Article
- Abdominal MRI-Derived Liver, Visceral Fat, and Pancreatic Phenotypes Improve Prediction of Incident Type 2 Diabetes.Diabetes/metabolism research and reviews · 2026Article
- Automated framework for volumetric musculoskeletal segmentation and assessment of torso CT images in large-scale databases.International journal of computer assisted radiology and surgery · 2026Article
- Multi-center validation of automated CT-based L1 vertebral Hounsfield unit.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026Article
- Article
- Longitudinal evaluation of intra-patient changes in computed tomography-based body composition measures.Abdominal radiology (New York) · 2026Article
- Sexual dimorphism and pro-atherogenic computed tomography radiomic phenotypes of subcutaneous and visceral abdominal fat.International journal of obesity (2005) · 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
- Pericoronary, but Not Epicardial, Cardiac Fat Thickness Is Associated with Sarcopenia in Hospitalized Older Adults.Medicina (Kaunas, Lithuania) · 2026Observational
- From Pixels to Prediction: Reviewing the Role of Artificial Intelligence in Body Composition Analysis.Journal of cachexia, sarcopenia and muscle · 2026Review
- Detecting dataset bias in medical AI using a generalized and modality agnostic auditing approach.NPJ digital medicine · 2026Article
- FocusedON-BC: A Robust Deep Learning Framework for Automated Body Composition Assessment.Nutrients · 2026Article
- eCAPRI: a novel tool combining clinical and imaging data for post-TAVI mortality prediction.European radiology · 2026Article
- Advancing osteoporosis opportunistic screening: multicenter validation of a deep learning algorithm using abdominal CT scans.Abdominal radiology (New York) · 2026Article
- Standardizing attenuation across tube voltages and vertebral levels for opportunistic osteoporosis screening on low-dose chest CT.Radiology advances · 2026Article
- Hepatic and abdominal adiposity in type 2 diabetes as assessed with machine learning on computed tomography scans.Diabetes, obesity & metabolism · 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
27 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
6 authors at 1 institution in 1 country.
Funding
Abstract
Abdominal CT is a frequently performed imaging examination for a wide variety of clinical indications. In addition to the immediate reason for scanning, each CT examination contains robust additional data on body composition that generally go unused in routine clinical practice. There is now growing interest in harnessing this additional information. Prime examples of cardiometabolic information include measurement of bone mineral density for osteoporosis screening, quantification of aortic calcium for assessment of cardiovascular risk, quantification of visceral fat for evaluation of metabolic syndrome, assessment of muscle bulk and density for diagnosis of sarcopenia, and quantification of liver fat for assessment of hepatic steatosis. All of these relevant biometric measures can now be fully automated through the use of artificial intelligence algorithms, which provide rapid and objective assessment and allow large-scale population-based screening. Initial investigations into these measures of body composition have demonstrated promising performance for prediction of future adverse events that matches or exceeds the best available clinical prediction models, particularly when these CT-based measures are used in combination. In this review, the concept of CT-based opportunistic screening is discussed, and an overview of the various automated biomarkers that can be derived from essentially all abdominal CT examinations is provided, drawing heavily on the authors' experience. As radiology transitions from a volume-based to a value-based practice, opportunistic screening represents a promising example of adding value to services that are already provided. If the potentially high added value of these objective CT-based automated measures is ultimately confirmed in subsequent investigations, this opportunistic screening approach could be considered for intentional CT-based screening.
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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.