Current trends of artificial intelligence in cancer imaging

Table 1 Most commonly adopted alghoritms in supervised machine learning

ML technique	ML alghoritms	Description
Linear	(1) Linear regression; and (2) Logistic regression	Linear methods are used to modelling the relationship between the dependent variable and one or more independent variables
Nonlinear	(1) Naive Bayes; (2) Decision tree; (3) k-Nearest Neighbors; (4) Support vector machines; and (5) Neural network	Nonlinear approaches are used to produce predictive insights depending on nonlinear relationships in experimental data
Ensemble	(1) Random forest; (2) Bootstrap aggregation; and (3) Stacked generalization	Ensemble techniques stack multiple models in order to improve prediction robustness and provide more accurate predictions than any individual model

ML: Machine learning.

Table 2 Possible clinical applications of artificial intelligence in oncological imaging

Clinical application	Oncologic field	Imaging modality	AI technique
Clinical-radiological workflow	Breast cancer[9]	Mammography	ML
	Image acquisition[10,11]	CT, MRI	DL
Cancer detection	Breast cancer[12,13]	Mammography	DL
	Lung cancer[14]	X-Ray, CT	DL
Tumor segmentation	Breast Cancer[17,18]	MRI	DL
Tumor characterization	Adrenal cancer[20]	MRI	ML
	Renal cancer[21]	MRI	ML
	Lung cancer[22]	CT	ML
Tumor staging	Head and neck cancer[23]	CT	ML
	Endometrial cancer[24]	MRI	ML
Treatment monitoring	Breast cancer [26]	MRI	ML

CT: Computed tomography; MRI: Magnetic resonance imaging; AI: Artificial intelligence; ML: Machine learning; DL: Deep learning.