ReviewKorean journal of radiology2025
Effects of Computed Tomography Technical Parameters on Body-Composition Analysis.
Review in Korean journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Technical feasibility of AI-based longitudinal multi-organ volumetry on low-dose PET/CT over 13 years.Japanese journal of radiology · 2026Article
- Development and validation of a prediction model for adverse outcomes in viral pneumonia using CT-based quantitative body composition analysis.Journal of thoracic disease · 2026Article
- MSCT Assessment of Perivascular Adipose Tissue and Visceral Fat Characteristics in Aortic, Iliac, and Lower Limb Aneurysms.Biomedicines · 2026Article
- Review
- CT-Assessed Body Composition as Predictor of Post-Operative Complications in Lung Cancer Patients.Cancers · 2026Review
- Artificial intelligence standardizes CT-based body composition analysis in breast cacer to address methodological heterogeneity.Frontiers in oncology · 2026Review
- Artificial intelligence-driven assessment of sarcopenia in orthopedic geriatrics: technical progress and clinical implications.Frontiers in endocrinology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Body-composition analysis (BCA) is gaining increasing clinical importance, because abnormalities in muscle and fat distribution are closely associated with patient outcomes for various diseases. Although several methods for assessing body composition are available, including bioelectrical impedance analysis, dual-energy X-ray absorptiometry, and magnetic resonance imaging, computed tomography (CT) has emerged as the most widely used imaging modality owing to its accuracy, accessibility, and artificial intelligence-driven automated analytical capabilities. CT-based BCA enables the precise quantification of skeletal muscle and adipose tissues, but its measurements can be influenced by various technical factors, such as the contrast phase, tube current and voltage, slice thickness, reconstruction algorithm, and scanner type. These parameters particularly affect attenuation-based metrics such as muscle density. Recent technological advancements, such as iterative reconstruction, dual-energy CT, and photon-counting CT, have resulted in new capabilities but may further introduce variability. This review summarizes the effects of CT parameters on BCA results and underscores the need for awareness and consistency when performing CT-based BCA. A better understanding of these factors may improve measurement reproducibility and support broader clinical and research applications.
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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.