Evaluation of automated computed tomography segmentation to assess body composition and mortality associations in cancer patients.
Elizabeth M Cespedes FelicianoKarteek PopuriDana CobzasVickie E BaracosMirza Faisal BegArafat Dad KhanCydney MaVincent ChowCarla M PradoJingjie XiaoVincent LiuWendy Y ChenJeffrey MeyerhardtKathleen B AlbersBette J CaanPublished in: Journal of cachexia, sarcopenia and muscle (2020)
In the first study to externally evaluate a commercially available software to assess body composition, automated segmentation of muscle and adipose tissues using ABACS was similar to manual analysis and associated with mortality after non-metastatic cancer. Automated methods will accelerate body composition research and, eventually, facilitate integration of body composition measures into clinical care.
Keyphrases
- body composition
- deep learning
- resistance training
- bone mineral density
- computed tomography
- machine learning
- high throughput
- convolutional neural network
- healthcare
- palliative care
- small cell lung cancer
- squamous cell carcinoma
- positron emission tomography
- gene expression
- papillary thyroid
- adipose tissue
- magnetic resonance imaging
- quality improvement
- insulin resistance
- cardiovascular events
- coronary artery disease
- cardiovascular disease
- risk factors
- squamous cell
- type diabetes
- chronic pain
- childhood cancer