Deep learning radio-clinical signature for predicting neoadjuvant chemotherapy response and prognosis from pretreatment CT images of locally advanced gastric cancer patients.
Xiang-Dong ChengJiahui ChenFeng LiYanqiang ZhangPengfei YuLitao YangLing HuangJiancheng SunShangqi ChenChengwei ShiYuanshui SunZaisheng YeLi YuanJiahui ChenQin WeiJingli XuHandong XuYahan TongZhehan BaoChencui HuangYiming LiYian DuZhiyuan XuXiangdong ChengPublished in: International journal of surgery (London, England) (2023)
We proposed a DLCS model that combined imaging features with clinical risk factors to accurately predict tumor response and identify the risk of OS in LAGC patients priors to NCT that can then be used to guide personalized treatment plans with the help of computerized tumor-level characterization.
Keyphrases
- neoadjuvant chemotherapy
- end stage renal disease
- locally advanced
- deep learning
- chronic kidney disease
- risk factors
- ejection fraction
- squamous cell carcinoma
- peritoneal dialysis
- computed tomography
- rectal cancer
- clinical trial
- patient reported outcomes
- lymph node
- magnetic resonance imaging
- open label
- combination therapy
- photodynamic therapy
- smoking cessation
- magnetic resonance
- phase ii study
- clinical decision support