Predicting occult lymph node metastasis in solid-predominantly invasive lung adenocarcinoma across multiple centers using radiomics-deep learning fusion model.
Weiwei TianQinqin YanXinyu HuangRui FengFei ShanDaoying GengZhiyong ZhangPublished in: Cancer imaging : the official publication of the International Cancer Imaging Society (2024)
The radiomics-deep learning fusion model showed promising ability to generalize in predicting OLNM from CT scans, potentially aiding personalized treatment for SPILAC patients across multiple centers.
Keyphrases
- lymph node metastasis
- deep learning
- squamous cell carcinoma
- contrast enhanced
- end stage renal disease
- papillary thyroid
- computed tomography
- ejection fraction
- newly diagnosed
- peritoneal dialysis
- chronic kidney disease
- convolutional neural network
- artificial intelligence
- machine learning
- dual energy
- prognostic factors
- magnetic resonance
- positron emission tomography
- african american
- patient reported