From pixels to patient care: deep learning-enabled pathomics signature offers precise outcome predictions for immunotherapy in esophageal squamous cell cancer.
Butuo LiWenru QinLinlin YangHaoqian LiChao JiangYueyuan YaoShuping ChengBing ZouBingjie FanTaotao DongLinlin WangPublished in: Journal of translational medicine (2024)
The outcome supervised pathomics signature based on deep learning has the potential to enable superior prognostic stratification of ESCC patients receiving PD-1 inhibitors, which convert the images pixels to an effective and labour-saving tool to optimize clinical management of ESCC patients.
Keyphrases
- deep learning
- squamous cell
- convolutional neural network
- end stage renal disease
- machine learning
- artificial intelligence
- newly diagnosed
- chronic kidney disease
- ejection fraction
- prognostic factors
- peritoneal dialysis
- papillary thyroid
- patient reported outcomes
- optical coherence tomography
- lymph node metastasis
- patient reported
- human health
- climate change