Article Figures & Data
Figures
- Fig 1.
Illustration of 2 strategies used to evaluate the supervised machine learning models to predict the molecular subgroups of medulloblastoma. The upper and lower figures show details of double 10-fold cross-validation and 3-dataset cross-validation schemes, respectively.
- Fig 2.
Receiver operating characteristic curves with a double 10-fold cross-validation scheme for support vector machine to predict the 4 main molecular subgroups of medulloblastoma with the use of computational MR imaging features.
- Fig 3.
Receiver operating characteristic curves with the largest mean AUC values for 4 distinct molecular subgroups of medulloblastoma with a 3-dataset cross-validation scheme.
- Fig 4.
MR imaging appearance of the 4 core molecular subgroups of medulloblastoma on T2-weighted and contrast-enhanced T1-weighted images.
Tables
Characteristic Institutional Cohort Stanford Boston Toronto No. of patients 32 28 49 Age (mean) (yr) 10.14 ± 8.49 8.54 ± 4.52 7.53 ± 3.69 Male sex (No.) (%) 23 (72) 9 (32) 32 (65) Molecular subgroup (No.) SHH 11 9 10 WNT 4 5 10 Group 3 7 5 12 Group 4 10 9 17 MRI availability (No.) 3T 5 0 1 1.5T 27 28 48 T1-weighted 32 26 48 2D T1-weighted 28 25 7 3D T1-weighted 4 1 41 T2-weighted 30 27 29 T1- and T2-weighted 30 25 27 - 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
