Comparison of deep learning-based recurrence-free survival with random survival forest and Cox proportional hazard models in Stage-I NSCLC patients.
İrem KarGökhan KocamanFarrukh İbrahimovSerkan EnönErdal CoşgunAtilla Halil ElhanPublished in: Cancer medicine (2023)
In conclusion, machine-learning techniques can be useful in predicting recurrence for lung cancer and guide clinicians both in choosing the adjuvant treatment options and best follow-up programs.
Keyphrases
- free survival
- machine learning
- deep learning
- end stage renal disease
- small cell lung cancer
- ejection fraction
- chronic kidney disease
- early stage
- artificial intelligence
- climate change
- public health
- peritoneal dialysis
- prognostic factors
- palliative care
- advanced non small cell lung cancer
- convolutional neural network
- big data
- epidermal growth factor receptor
- clinical evaluation