Table 4.

Summary of findings across key articles. The machine learning classifiers in bold characters represent those that yielded the most significant results and the AUC values are related to the results from those classifiers in bold characters.

Study	Analyzed images	Machine learning classifiers	Most relevant selected features	AUC
Tahmassebi et al.	DCE, DWI T2	Linear support vector machine Linear discriminant analysis logistic regression Random forests Stochastic gradient descent Decision tree Adaptive boosting Extreme gradient boosting (XGBoost)	Change in lesion size Complete pattern of shrinkage Mean transit time Peritumoral edema Minimum ADC value	0.86
O’Flynn et al.	DCE, DWI, T2	Linear discriminant analysis	Enhancement fraction (EF) Tumor volume	0.76
Mani et al.	DCE, DWI	Linear classifiers (Gaussian Naïve Bayes, Logistic Regression, and Bayesian Logistic Regression) decision tree-based classifiers (CART and Random Forests) Kernel based classifier (Support Vector Machine) Rule learner (Ripper)	See Table 1.	0.96
Mani et al.	DCE, DWI	GS-10 HITON-MB BLCD-MB	Mean ADC post one cycle of treatment Mean of the change of the top 15% of kep as estimated by the TK model	0.86
Cain et al.	T1 non-fat sat, DCE	Multivariate logistic regression classifier (fitglm) Support vector machine classifier (fitcsvm and fitSVMposterior)	Change in variance of uptake	0.71
Aghaei et al.	DCE	Simple feature fusion method Artificial neural network (ANN) with a wrapper subset evaluator	Average contrast enhancement Standard deviation of contrast enhancement inside an entire tumor region Standard deviation of contrast enhancement in the enhanced area Average pixel value of necrotic regions Ratio of necrotic volume over tumor volume	0.96
Ha et al.	First T1 postcontrast dynamic images	Convolutional neural networks (CNN)	Not specified	0.88