Table 3:

Studies investigating the role of AI in predicting MGMT promoter methylation status of glioma patients

Study	Purpose	Number of Patients	Findings
Li et al.43 (2018)	To build a radiomics model from multiregional and multiparametric MRI to predict MGMT promoter methylation status in GBM patients	193 (multicenter)	Radiomics model with minimal set of 6 all-relevant features predicted MGMT status with accuracy of 80% (AUC = 0.88) Radiomics model with 8 univariately-predictive and non-redundant features predicted MGMT status with accuracy of 70% (AUC = 0.76) Combining clinical features with radiomic features did not significantly improve performance
Xi et al.44 (2018)	To analyze utility of MRI-based radiomics features in predicting MGMT promoter methylation status in GBM patients	98 (n = 48 methylated; n = 50 unmethylated)	Best performance for predicting MGMT status was achieved by combining T1WI, T2WI and CE-T1WI (accuracy = 86.6%) Radiomic features of T1WI had accuracy of 67.6% Radiomic features of CE-T1WI had accuracy of 82% Radiomic features of T2WI had accuracy of 69.3%
Qian et al.51 (2020)	Using 18F-DOPA PET-based radiomics to predict MGMT status in GBM patients	86	Radiomics signature to predict MGMT methylation status using features extracted from GBM contour alone had accuracy of 80% Prediction accuracy was not improved with additional input features
Kong et al.52 (2019)	Using 18F-FDG PET-based radiomics to predict MGMT status in diffuse glioma patients	107	Radiomics signature had the best performance with accuracy of 91.3% and 77.8% (AUC of 0.94 and 0.86) in the primary and validation cohorts, respectively Clinical model had accuracy 64.8% and 66.4% in the primary and validation cohort, respectively Fusion model had accuracy of 64.8% and 72.7% in the primary and validation cohort, respectively
Huang et al.42 (2021)	Predicting MGMT methylation status in gliomas using MR-based radiomics with textural features	53	Combined radiomics model using multiparametric MRI predicted MGMT methylation status with AUC, sensitivity, and specificity of 0.82, 90.5% and 72.7%, respectively in the GBM dataset AUC, sensitivity, and specificity of 0.83, 70.2% and 90.6% in the overall glioma dataset
Vils et al.45 (2021)	Predicting MGMT methylation status using multi-center MRI-based radiomics in recurrent GBM patients	69 (DIRECTOR trial)	CE-T1W MRI-based radiomic model to predict MGMT status was established using linear intensity interpolation and had AUC of 0.67 in both training and validation cohorts
Korfiatis et al.49 (2017)	Comparing three different ResNet architectures in predicting MGMT methylation status without distinct tumor segmentation step	155 (n = 66 methylated; n = 89 unmethylated tumors)	ResNet50 (50 layers) was the best performing model with prediction accuracy of 94.9% on test set ResNet34 (34 layers) achieved an accuracy of 80.7% ResNet18 (18 layers) achieved an accuracy of 76.8%
Le et al.47 (2020)	Evaluating a novel radiomics-based XGBoost model to identify MGMT methylation status in IDH1 wildtype GBM patients	53	9 radiomics features were extracted from multimodality MRI for model construction XGBoost classifier predicted MGMT status with accuracy of 88.7%, AUC of 0.896, sensitivity of 88% and specificity of 89%
Crisi & Filice48 (2020)	Stratification of MGMT methylation status in GBM patients using DSC-MRI-based radiomics features	59	Used 14 radiomics features to build a multilayer deep learning model that classified MGMT methylation status into 3 groups Their model had AUC, sensitivity, and specificity of 0.84, 75% and 85%, respectively
Lu et al.50 (2020)	Combining MRI based-radiomic, semantic and clinical features to improve prediction of MGMT methylation status in GBM patients	181 MRI studies	Optimal cut-off value for MGMT promoter methylation index was 12.75% Their model combined radiomic, VASARI and clinical features to predict MGMT status and had an accuracy that varied between 45% and 67%