Table 4:

Studies investigating the use of ML to differentiate between types of brain tumors

Study	Purpose	Number of Patients	Findings
Kim et al.56 (2018)	Differentiate GBM vs. primary central nervous system lymphoma (PCNSL) using multiparametric MRI-based radiomics	143 patients (n = 86 training; n = 57 validation)	15 features used in final model AUC validation = 0.956 AUC training = 0.979
Shrot et al.57 (2019)	Differentiating different brain tumors using basic and advanced MRI-based radiomics	141 patients (41 GBM, 38 METS, 50 meningioma & 12 PCNSL)	Classification used morphologic MRI, perfusion MRI & DTI metrics Feature subset selection via SVMs Binary SVM classification accuracy ranged from 81.6 to 97.0
Niu et al.58 (2019)	Differentiating between different meningioma subtypes using basic MRI-based radiomics	241 patients (n = 80 meningiothelial meningioma, n = 80 fibrous meningioma, n = 81 transitional meningioma)	Fisher discriminant analysis model for binary differentiation between meningioma types had accuracies between 98.8% and 100% Leave one out cross validation had accuracies between 91.3% and 100%
Nakagawa et al.59 (2018)	Differentiating GBM vs PCNSL using ML method based on texture features in multiparametric MRI	70 patients	Prediction model developed using univariate logistic regression and XGBoost rCBV offered highest AUC of 0.86 (rCBV AUC = 0.83; skewness of CE-T1WI AUC = 0.78) AUC of XGBoost was significantly higher than that of two radiologists (0.98 vs 0.84).
Dong et al.60 (2019)	Differentiating between pilocytic astrocytoma (PA) and GBM using MRI quantitative radiomic features by a decision tree model	66 patients (PA n = 31; GBM n = 35)	Subset of 12 features selected by feature stability and Boruta algorithm to build decision tree model Training set: accuracy = 87%; sensitivity = 90%; specificity = 83% Validation set: accuracy = 86%, sensitivity = 80%; specificity = 91%
Zhang et al.61 (2018)	Using MRI-based radiomics to differentiate between non-functioning pituitary adenoma subtypes	112 patients (training set n = 75; test set n = 37)	T1WI had AUC of 0.83 and 0.80 in training and test sets, respectively CE-T1WI features added no additional value to model
Chakrabarty et al.62 (2021)	Train a CNN to differentiate between tumor types (HGG, LGG, metastases, meningioma, pituitary adenoma, acoustic neuroma & healthy tissue)	1373 (BraTs, TCGA, LGG-1p19q dataset, internal and external dataset)	Internal data set: sensitivities, PPVs, AUCs and area under the precision-recall curves (AUPRCs) ranged from 87%−100%, 85% to 100%, 0.98 to 1.0, and 0.91 to 1.0, respectively External data set: sensitivities, PPVs, AUCs and AUPRCs ranged from 91% to 97%, 73% to 99%, 0.97 to 0.98, and 0.9 to 1.0, respectively
Qian et al.53 (2019)	To identify the optimal radiomic ML classifier for differentiating GBM vs METS	412	SVM + LASSO classifier had highest prediction efficacy (AUC = 0.90, accuracy = 82.7%, sensitivity = 79.8%, specificity = 87.3%, PPV = 90%, and NPV = 72.9%)
Artzi et al.54 (2019)	To differentiate between GBM and METS using CE-T1WI MRI-based radiomics	439	Best results for differentiating GBM vs. METS were obtained using SVM classifier which had a mean accuracy, AUC, sensitivity, and specificity of 85%, 0.96, 86% and 85%, respectively Optimal differentiation of GBM and METS subtypes achieved using SVM classifier with accuracy, AUC, sensitivity, and specificities ranging between 75%−90%, 0.57–0.98, 11%−100%, and 76%−99% respectively,
Kniep et al.55 (2018)	Using multiparametric MRI-based radiomics to predict tumor type in brain metastasis (SCLC, BC, MM, GC and NSCLC)	189	AUC for predicting type of brain metastasis ranged between 0.64 (NSCLC) and 0.82 (MM) Prediction performance of classifier was superior to radiologists’ readings MM had highest increase in sensitivity (17%) using classifier compared to radiologists’ readings