ArticleRadiology2023
Abdominal CT Body Composition Thresholds Using Automated AI Tools for Predicting 10-year Adverse Outcomes.
Article in Radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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29 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 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
- Pooled two-cohort MRI body composition phenotyping with open-source deep learning.Communications medicine · 2026Article
- Exploring Adipose Tissue Behavior in CT: Impact of Age, Sex, and Contrast Media on Body Composition, Liver and Skeletal Muscle.Journal of imaging · 2026Article
- Deep Learning-Derived Body Composition Analysis Predicts Long-Term Mortality After Transcatheter Aortic Valve Replacement.Mayo Clinic proceedings. Digital health · 2026Article
- Methodologies for establishing and validating cut-points and comparative standards in medical imaging-based body composition analysis: a scoping review protocol.Systematic reviews · 2026Article
- Article
- CT-Assessed Body Composition as Predictor of Post-Operative Complications in Lung Cancer Patients.Cancers · 2026Review
- Associations Between Deep Learning-Derived Fat, Muscle, and Bone Measures From Abdominal Computed Tomography Scans and Fall Risk in Persons Aged 20 Years or Older.Mayo Clinic proceedings. Digital health · 2025Article
- Methodology for a fully automated pipeline of AI-based body composition tools for abdominal CT.Abdominal radiology (New York) · 2025Article
- Data-driven abdominal phenotypes of type 2 diabetes in lean, overweight, and obese cohorts from computed tomography.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Improved muscle and fat segmentation for body composition measures on quantitative CT.International journal of computer assisted radiology and surgery · 2025Article
- Deep learning-quantified body composition from positron emission tomography/computed tomography and cardiovascular outcomes: a multicentre study.European heart journal · 2025Article
- Body composition after treatment of intra-mucosal gastric cancer with endoscopic resection compared to gastrectomy.Communications medicine · 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
- A unified approach to medical image segmentation by leveraging mixed supervision and self and transfer learning (MIST).Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2025Article
- Comparing fully automated AI body composition biomarkers at differing virtual monoenergetic levels using dual-energy CT.Abdominal radiology (New York) · 2025Article
- Future Applications of Cardiothoracic CT.Radiology · 2025Review
- AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans for mortality prediction: a multicentre study.The Lancet. Digital health · 2025Article
- Article
- Myosteatosis predicts the prognosis of patients with ST-elevation myocardial infarction who undergo emergency percutaneous coronary intervention.Frontiers in endocrinology · 2025Article
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6 authors at 2 institutions in 1 country.
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Abstract
Background CT-based body composition measures derived from fully automated artificial intelligence tools are promising for opportunistic screening. However, body composition thresholds associated with adverse clinical outcomes are lacking. Purpose To determine population and sex-specific thresholds for muscle, abdominal fat, and abdominal aortic calcium measures at abdominal CT for predicting risk of death, adverse cardiovascular events, and fragility fractures. Materials and Methods In this retrospective single-center study, fully automated algorithms for quantifying skeletal muscle (L3 level), abdominal fat (L3 level), and abdominal aortic calcium were applied to noncontrast abdominal CT scans from asymptomatic adults screened from 2004 to 2016. Longitudinal follow-up documented subsequent death, adverse cardiovascular events (myocardial infarction, cerebrovascular event, and heart failure), and fragility fractures. Receiver operating characteristic (ROC) curve analysis was performed to derive thresholds for body composition measures to achieve optimal ROC curve performance and high specificity (90%) for 10-year risks. Results A total of 9223 asymptomatic adults (mean age, 57 years ± 7 [SD]; 5152 women and 4071 men) were evaluated (median follow-up, 9 years). Muscle attenuation and aortic calcium had the highest diagnostic performance for predicting death, with areas under the ROC curve of 0.76 for men (95% CI: 0.72, 0.79) and 0.72 for women (95% CI: 0.69, 0.76) for muscle attenuation. Sex-specific thresholds were higher in men than women (
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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.