Prediction of epidermal growth factor receptor ( EGFR ) mutation status in lung adenocarcinoma patients on computed tomography (CT) images using 3-dimensional (3D) convolutional neural network.
Guojin ZhangLan ShangYuntai CaoJing ZhangShenglin LiRong QianHuan LiuZhuoli ZhangHong PuQiong ManWeifang KongPublished in: Quantitative imaging in medicine and surgery (2024)
mutation status in patients with lung adenocarcinoma and is expected to become an auxiliary tool for clinicians.
Keyphrases
- epidermal growth factor receptor
- convolutional neural network
- computed tomography
- tyrosine kinase
- deep learning
- advanced non small cell lung cancer
- end stage renal disease
- small cell lung cancer
- positron emission tomography
- newly diagnosed
- ejection fraction
- chronic kidney disease
- dual energy
- magnetic resonance imaging
- contrast enhanced
- peritoneal dialysis
- palliative care
- prognostic factors
- optical coherence tomography
- machine learning
- pet ct