ReviewDiagnostics (Basel, Switzerland)2023
Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions.
Review in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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
23 citing papers in PubMed.
- Radiomics and anaesthetic planning: Quantitative imaging as a new frontier in preoperative risk assessment.World journal of radiology · 2026Review
- Future of CT body composition research: Methodological discrepancies and advances.Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition · 2026Review
- Methodological standards for body composition assessment-an expert-endorsed guide for research and clinical applications: bioimpedance, dual-energy X-ray absorptiometry, computerized tomography, and ultrasound methods.The American journal of clinical nutrition · 2026Review
- External Validation of an Open-Source Model for Automated Muscle Segmentation in CT Imaging of Cancer Patients.Journal of imaging · 2026Article
- Intravenous contrast medium impairs CT-based muscle quality but not quantity assessment: a translational study.Frontiers in radiology · 2026Article
- Artificial intelligence-driven body composition analysis and its association with survival in patients with colorectal liver metastases.Contemporary oncology (Poznan, Poland) · 2026Article
- The hidden burden: incidentalomas detected on abdominopelvic computed tomography in bariatric surgery candidates - a single-center experience.Journal of medicine and life · 2025Observational
- Sarcopenic obesity exacerbates gastric cancer prognosis via increased intermuscular and visceral fat accumulation.European journal of medical research · 2025Observational
- AI in Adipose Imaging: Revolutionizing Visceral Adipose Tissue, Ectopic Fat, and Cardiovascular Risk Assessment.Current atherosclerosis reports · 2025Review
- Development and validation of a CT-measured body composition radiomics model for prognostic assessment in resected pancreatic adenocarcinoma.Scientific reports · 2025Article
- Beyond BMI: An opinion on the clinical value of AI-powered CT body composition analysis.Biomolecules & biomedicine · 2025Review
- Predicting Primary Graft Dysfunction in Systemic Sclerosis Lung Transplantation Using Machine-Learning and CT Features.Clinical transplantation · 2025Article
- Transforming body composition of computational phantoms based on form-finding dynamics.Medical physics · 2025Article
- Article
- Body composition and checkpoint inhibitor treatment outcomes in advanced melanoma: a multicenter cohort study.Journal of the National Cancer Institute · 2025Article
- Predicting post-lung transplant survival in systemic sclerosis using CT-derived features from preoperative chest CT scans.European radiology · 2025Article
- A Decade of Progress in Ultrasound Assessments of Subcutaneous and Total Body Fat: A Scoping Review.Life (Basel, Switzerland) · 2025Review
- The Impact of Radiation Dose on CT-Based Body Composition Analysis: A Large-Animal Study.Journal of cachexia, sarcopenia and muscle · 2025Article
- Fat-Free Mass: Friend or Foe to Metabolic Health?Journal of cachexia, sarcopenia and muscle · 2025Review
- Strategic Considerations for Selecting Artificial Intelligence Solutions for Institutional Integration: A Single-Center Experience.Mayo Clinic proceedings. Digital health · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
CT body composition analysis has been shown to play an important role in predicting health and has the potential to improve patient outcomes if implemented clinically. Recent advances in artificial intelligence and machine learning have led to high speed and accuracy for extracting body composition metrics from CT scans. These may inform preoperative interventions and guide treatment planning. This review aims to discuss the clinical applications of CT body composition in clinical practice, as it moves towards widespread clinical implementation.
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.