The artificial intelligence and machine learning in lung cancer immunotherapy

Table 2 Summary of machine learning methods in lung cancer immunotherapy prediction

Material	Task	Secondary task	Algorithm
CT, PET/CT	Prognosis	Efficacy of immunotherapy	DT, BT, RF, SVM, GLM, ANN, CNN
Genomics	Treatment response	Survive	RF, MLP, unsupervised clustering
Proteomics	Survive		Iterative unsupervised machine learning
Microbiology	Survive	Treatment response	RF, MLP
Blood	Survive	Efficacy of immunotherapy	RF, MLP, SVM, elastic network, partial least squares discriminant analysis, Gaussian process classifier
Blood	irAE		ANN
Database	irAE		XGBoosted

CT Computer Tomography, PET/CT Positron emission tomography/Computer Tomography, DT Decision Trees, BT Boosting Tree, RF Random Forests, SVM Support Vector Machine, GLM Generalized Linear Model, ANN Artificial Neural Network, CNN Convolutional Neural Network, MLP Multilayer Perceptron, XGBoosted eXtreme Gradient Boosting

ISSN: 1756-8722