Deep Learning to Predict EGFR Mutation and PD-L1 Expression Status in Non-Small-Cell Lung Cancer on Computed Tomography Images.
Chengdi WangXiuyuan XuJun ShaoKai ZhouKefu ZhaoYanqi HeJingwei LiJixiang GuoZhang YiWei-Min LiPublished in: Journal of oncology (2021)
In this study, a noninvasive and effective model was proposed to predict EGFR mutation and PD-L1 expression status as a clinical decision support tool. Additionally, the combination of deep learning features with clinical features demonstrated great stratification capabilities in the prognostic model. Our team would continue to explore the application of imaging markers for treatment selection of lung cancer patients.
Keyphrases
- deep learning
- clinical decision support
- small cell lung cancer
- computed tomography
- convolutional neural network
- epidermal growth factor receptor
- artificial intelligence
- tyrosine kinase
- machine learning
- electronic health record
- high resolution
- positron emission tomography
- palliative care
- magnetic resonance imaging
- quality improvement
- optical coherence tomography
- contrast enhanced
- replacement therapy
- pet ct
- combination therapy
- photodynamic therapy