Table 3.

Main consequence of 3 models.

Signature	Accuracy	AUC	95% CI	Sensitivity	Specificity	PPV	NPV	Cohort
Clinic signature	0.734	0.741	0.6592–0.8228	0.763	0.633	0.878	0.437	Train
Rad signature	0.839	0.858	0.8007–0.9158	0.870	0.735	0.919	0.621	Train
Nomogram	0.803	0.894	0.8434–0.9438	0.787	0.857	0.950	0.538	Train
Clinic signature	0.655	0.705	0.5505–0.8604	0.628	0.750	0.900	0.360	Test
Rad signature	0.636	0.822	0.7036–0.9398	0.535	1.000	1.000	0.375	Test
Nomogram	0.782	0.843	0.7354–0.9507	0.767	0.833	0.943	0.500	Test

AUC: In train and test cohorts, both clinical signature and rad signature get the prefect fitting. The Nomogram using the LR algorithm was performed to combine clinical signature and rad signature, which shows the best performance.

AUC, area under the curve; LR, logistic regression; NPV negative predictive value; PPV, positive predictive value.