Association Between Body Composition and Survival in Patients With Gastroesophageal Adenocarcinoma: An Automated Deep Learning Approach.
Matthias JungThierno D DialloTobias ScheefMarco ReisertAlexander RauMaximilian Frederik RusseFabian BambergStefan Fichtner-FeiglMichael QuanteJakob WeissPublished in: JCO clinical cancer informatics (2024)
DL enables opportunistic estimation of BC from routine staging CT to quantify prognostic information. In patients with GEAC, only a substantial decrease of VAT 6-12 months postsurgery was an independent predictor for DFS beyond traditional risk factors, which may help to identify individuals at high risk who go otherwise unnoticed.
Keyphrases
- body composition
- risk factors
- deep learning
- resistance training
- bone mineral density
- image quality
- dual energy
- lymph node
- computed tomography
- contrast enhanced
- clinical practice
- squamous cell carcinoma
- pet ct
- convolutional neural network
- artificial intelligence
- health information
- free survival
- locally advanced
- positron emission tomography
- machine learning
- magnetic resonance imaging
- magnetic resonance
- radiation therapy
- high intensity