Table 4 Diagnostic performance of combined models based on radiomic and clinicoradiological variables applied to 4 different ML algorithms and MLP.

Models	AUC	95% CI		Sensitivity	Specificity	Accuracy	PPV	NPV
Models	AUC	Lower	Upper	Sensitivity	Specificity	Accuracy	PPV	NPV
LR
Training set	0.909	0.869	0.950	0.798	0.922	0.861	0.908	0.826
Validation set	0.805	0.708	0.901	0.7381	0.800	0.770	0.775	0.766
RF
Training set	0.972	0.954	0.990	0.919	0.903	0.911	0.901	0.921
Validation set	0.833	0.742	0.925	0.810	0.733	0.770	0.739	0.805
SGD
Training set	0.500	0.500	0.500	1.000	0.000	0.490	0.490	0.000
Validation set	0.500	0.500	0.500	1.000	0.000	0.483	0.483	0.000
SVM
Training set	0.889	0.843	0.934	0.727	0.932	0.832	0.911	0.781
Validation set	0.797	0.699	0.895	0.667	0.822	0.747	0.778	0.726
MLP*
Training set	0.927	0.893	0.955	0.991	0.380	0.671	0.592	0.979
Validation set	0.835	0.719	0.929	0.935	0.333	0.655	0.617	0.818

AUC area under the ROC curves, CI confidence interval, PPV positive prediction value, NPV negative prediction value, LR logistic regression, RF random forest, SGD stochastic gradient descent, SVM support vector machine, MLP multilayer perceptron.
*The optimal predictive performance in the validation set was observed in the MLP model.

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