Artificial intelligence measured 3D body composition to predict pathological response in rectal cancer patients.
Matthew Yuan-Kun WeiKe CaoWei HongJosephine YeungMargaret LeePeter GibbsIan G FaragherPaul N BairdJustin M YeungPublished in: ANZ journal of surgery (2024)
This is the first study in the literature utilizing AI-measured 3D Body composition in LARC patients to assess their impact on pathological response. SM volume and age were positive predictors of pCR disease in both male and female patients following NAT for LARC. Future studies investigating the impact of body composition on clinical outcomes and patients on other neoadjuvant regimens such as TNT are potential avenues for further research.
Keyphrases
- body composition
- end stage renal disease
- artificial intelligence
- ejection fraction
- newly diagnosed
- chronic kidney disease
- rectal cancer
- resistance training
- prognostic factors
- peritoneal dialysis
- bone mineral density
- machine learning
- patient reported outcomes
- risk assessment
- squamous cell carcinoma
- lymph node
- deep learning
- big data
- postmenopausal women
- locally advanced
- human health
- patient reported
- high intensity