Magnetic resonance imaging-based radiomics model for preoperative assessment of risk stratification in endometrial cancer

Table 4 Outcomes of radiomics models in validation set

Algorithms	AUC (95%CI)	Accuracy	Sensitivity	Specificity
LR	0.794 (0.706-0.976)	0.794	0.765	0.824
KNN	0.893 (0.766-0.971)	0.765	0.823	0.705
DT	0.676 (0.529-0.824)	0.676	0.529	0.824
RF	0.915 (0.806-0.986)	0.824	0.824	0.824
GB	0.907 (0.792-0.979)	0.794	0.706	0.882
XGBoost	0.869 (0.739-0.965)	0.765	0.706	0.824
GBDT	0.872 (0.742-0.958)	0.765	0.647	0.882

AUC: Area under the curve; CI: Confidence interval; DT: Decision tree; GB: Gradient boosting; GBDT: Gradient boosting decision tree; KNN: K-nearest neighbor; LR: Logistic regression; RF: Random forest; XGBoost: eXtreme gradient boosting.