Table 5 Performance of the best MRI- and CT-based machine learning classification model
Data | ICG-R15 | Cohort | AUC (95%CI) | Accuracy | Sensitivity | Specificity | model |
|---|---|---|---|---|---|---|---|
MRI | ICG-R15 ≤ 10% vs. ICG-R15>10% | Training | 0.996(0.989–1.000) | 0.987 | 0.980 | 1.000 | XGBoost |
Test | 0.899(0.784–1.000) | 0.853 | 0.875 | 0.833 | XGBoost | ||
ICG-R15 ≤ 20% vs. ICG-R15>20% | Training | 0.995(0.986–1.000) | 0.962 | 0.929 | 0.980 | Random Forest | |
Test | 0.979(0.941–1.000) | 0.882 | 1.000 | 0.857 | Random Forest | ||
ICG-R15 ≤ 30% vs. ICG-R15>30% | Training | 0.997(0.991–1.000) | 0.962 | 1.000 | 0.951 | XGBoost | |
Test | 0.961(0.890–1.000) | 0.941 | 1.000 | 0.968 | XGBoost | ||
CT | ICG-R15 ≤ 10% vs. ICG-R15>10% | Training | 0.998(0.995–1.000) | 0.970 | 0.957 | 1.000 | XGBoost |
Test | 0.822(0.700–0.944) | 0.842 | 0.917 | 0.714 | XGBoost | ||
ICG-R15 ≤ 20% vs. ICG-R15>20% | Training | 0.866(0.781–0.951) | 0.842 | 0.872 | 0.830 | SVM | |
Test | 0.860(0.758–0.963) | 0.842 | 0.840 | 0.844 | SVM | ||
ICG-R15 ≤ 30% vs. ICG-R15>30% | Training | 0.997(0.991–1.000) | 0.992 | 1.000 | 0.991 | XGBoost | |
Test | 0.938(0.824–1.000) | 0.965 | 0.800 | 0.981 | XGBoost |