AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans enhances mortality prediction: multicenter study.
Jirong YiAnna M MichalowskaAakash ShanbhagRobert J H MillerJolien GeersWenhao ZhangAditya KillekarNipun ManralMark LemleyMikolaj BuchwaldJacek KwiecinskiJianhang ZhouPaul B KavanaghJoanna X LiangValerie BuiloffTerrence D RuddyAndrew J EinsteinAttila FeherRobert J H MillerAlbert J SinusasDaniel S BermanDamini DeyPiotr J SlomkaPublished in: medRxiv : the preprint server for health sciences (2024)
The comprehensive body composition analysis can be routinely performed, at the point of care, for all cardiac perfusion scans utilizing CTAC. Automatically-obtained volumetric body composition quantification metrics provide added value over existing risk factors, using already-obtained scans to significantly improve the risk stratification of patients and clinical decision-making.
Keyphrases
- body composition
- computed tomography
- contrast enhanced
- resistance training
- risk factors
- bone mineral density
- dual energy
- end stage renal disease
- decision making
- left ventricular
- chronic kidney disease
- ejection fraction
- magnetic resonance imaging
- positron emission tomography
- prognostic factors
- peritoneal dialysis
- magnetic resonance
- cardiovascular events
- image quality
- type diabetes
- cardiovascular disease
- patient reported outcomes
- machine learning
- patient reported
- pet ct