ArticleScientific reports2024
Volumetric body composition analysis of the Cancer Genome Atlas reveals novel body composition traits and molecular markers Associated with Renal Carcinoma outcomes.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Association of AI-derived abdominal organ volumetry with postoperative outcomes in clear cell renal cell carcinoma: a multicentre study.Annals of medicine · 2026Observational
- The predictive value of abdominal fat area and volume for early recurrence of after resection hepatocellular carcinoma.BMC gastroenterology · 2026Article
- Leveraging Sarcopenia index by automated CT body composition analysis for pan cancer prognostic stratification.NPJ digital medicine · 2025Article
- Exchange of Quantitative Computed Tomography Assessed Body Composition Data Using Fast Healthcare Interoperability Resources as a Necessary Step Toward Interoperable Integration of Opportunistic Screening Into Clinical Practice: Methodological Development Study.Journal of medical Internet research · 2025Article
- AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans for mortality prediction: a multicentre study.The Lancet. Digital health · 2025Article
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Authors and funding
12 authors.
Funding
Abstract
Clinically, the body mass index remains the most frequently used metric of overall obesity, although it is flawed by its inability to account for different adipose (i.e., visceral, subcutaneous, and inter/intramuscular) compartments, as well as muscle mass. Numerous prior studies have demonstrated linkages between specific adipose or muscle compartments to outcomes of multiple diseases. Although there are no universally accepted standards for body composition measurement, many studies use a single slice at the L3 vertebral level. In this study, we use computed tomography (CT) studies from patients in The Cancer Genome Atlas (TCGA) to compare current L3-based techniques with volumetric techniques, demonstrating potential limitations with level-based approaches for assessing outcomes. In addition, we identify gene expression signatures in normal kidney that correlate with fat and muscle body composition traits that can be used to predict sex-specific outcomes in renal cell carcinoma.
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