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Table 2: Predictive performance of 2 machine learning models for the identification of medulloblastoma molecular subgroups
MRI Dataset/Targeted Subgroup AUC with Double 10-Fold Cross-Validation AUC with 3-Dataset Cross-Validation
T1 
    SHH 0.67 0.73
    WNT 0.56 0.47
    Group 3 0.40 0.54
    Group 4 0.79 0.76
T2 
    SHH 0.70 0.66
    WNT 0.63 0.72
    Group 3 0.51 0.57
    Group 4 0.54 0.59
T1 + T2 
    SHH 0.79 0.70
    WNT 0.45 0.45
    Group 3 0.70 0.39
    Group 4 0.83 0.80

MRI Dataset/Targeted Subgroup	AUC with Double 10-Fold Cross-Validation	AUC with 3-Dataset Cross-Validation
T1
SHH	0.67	0.73
WNT	0.56	0.47
Group 3	0.40	0.54
Group 4	0.79	0.76
T2
SHH	0.70	0.66
WNT	0.63	0.72
Group 3	0.51	0.57
Group 4	0.54	0.59
T1 + T2
SHH	0.79	0.70
WNT	0.45	0.45
Group 3	0.70	0.39
Group 4	0.83	0.80