Background & aims Body composition analysis on CT images is a valuable tool for
sarcopenia assessment. We aimed to develop and validate a deep neural network …

Objective Body composition comprises prognostic information in patients with various
malignancies and can be opportunistically determined from routine computed tomography …

Purpose To develop and evaluate a fully automated algorithm for segmenting the abdomen
from CT to quantify body composition. Materials and Methods For this retrospective study, a …

Background: Body composition is increasingly being recognized as an important prognostic
factor for health outcomes across cancer, liver cirrhosis, and critically ill patients. Computed …

Background & aims The quantity and quality of skeletal muscle and adipose tissue is an
important prognostic factor for clinical outcomes across several illnesses. Clinically acquired …

Purpose Tracing muscle groups manually on CT to calculate body composition parameters
and diagnose sarcopenia is costly and time consuming. Artificial Intelligence (AI) provides …

Objectives To develop a pipeline for automated body composition analysis and skeletal
muscle assessment with integrated quality control for large-scale application in opportunistic …

Background Body composition is associated with survival outcome in oncological patients,
but it is not routinely calculated. Manual segmentation of subcutaneous adipose tissue (SAT) …

Objectives Body tissue composition is a long-known biomarker with high diagnostic and
prognostic value not only in cardiovascular, oncological, and orthopedic diseases but also in …

The amounts of muscle and fat in a person's body, known as body composition, are
correlated with cancer risks, cancer survival, and cardiovascular risk. The current gold …