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
- newly diagnosed
- chronic kidney disease
- ejection fraction
- rectal cancer
- resistance training
- peritoneal dialysis
- prognostic factors
- machine learning
- bone mineral density
- deep learning
- patient reported outcomes
- squamous cell carcinoma
- big data
- locally advanced
- lymph node
- high intensity