Nomograms integrating CT radiomic and deep learning signatures to predict overall survival and progression-free survival in NSCLC patients treated with chemotherapy.
Runsheng ChangShouliang QiYanan WuYong YueXiaoye ZhangWei QianPublished in: Cancer imaging : the official publication of the International Cancer Imaging Society (2023)
By integrating the TNM stage, CT radiomic signature, and deep learning signatures, the established nomograms can predict the individual prognosis of NSCLC patients who received chemotherapy. The integrated nomogram has the potential to improve the individualized treatment and precise management of NSCLC patients.
Keyphrases
- deep learning
- free survival
- small cell lung cancer
- advanced non small cell lung cancer
- end stage renal disease
- computed tomography
- image quality
- contrast enhanced
- newly diagnosed
- chronic kidney disease
- artificial intelligence
- locally advanced
- brain metastases
- prognostic factors
- convolutional neural network
- peritoneal dialysis
- machine learning
- positron emission tomography
- lymph node metastasis
- magnetic resonance imaging
- magnetic resonance
- rectal cancer
- radiation therapy
- chemotherapy induced
- smoking cessation