Computed tomography radiomics-based machine learning nomogram for preoperative prediction of glypican-3 expression in hepatocellular carcinoma

Table 5 Performance of the combined model for predicting glypican-3 expression status in hepatocellular carcinoma

Set	Model	AUC	95%CI	Sensitivity	Specificity	PPV	NPV	Accuracy
Training set	Clinic	0.861	0.783-0.940	0.500	0.977	0.933	0.754	0.792
	ALL	0.959	0.921-0.997	0.964	0.818	0.771	0.973	0.875
	Nomogram	0.979	0.955-1.000	0.964	0.864	0.818	0.974	0.903
Testing set	Clinic	0.845	0.759-0.931	0.495	0.688	0.599	0.935	0.935
	ALL	0.862	0.638-1.000	0.705	0.759	0.725	0.957	0.742
	Nomogram	0.948	0.832-1.000	0.815	0.897	0.801	0.963	0.871

Clinic: Clinical model; ALL: Random forest radiomics model; Nomogram: Combined model; AUC: Area under the curve; PPV: Positive predictive value; NPV: Negative predictive value.