Preoperative perineural invasion in rectal cancer based on deep learning radiomics stacking nomogram: A retrospective study

Table 3 Prediction performance of four models (arterial support vector machine, venous support vector machine, CT-Tstage and Nomogram)

Model	Dataset	AUC	95%CI	Sensitivity	Specificity	Recall	Accuracy	Precision	F1-score
ASVM	Train	0.904	0.865-0.943	0.840	0.885	0.840	0.863	0.880	0.860
	Test	0.890	0.794-0.987	0.857	0.850	0.857	0.854	0.851	0.854
VSVM	Train	0.890	0.850-0.930	0.926	0.703	0.926	0.815	0.757	0.786
	Test	0.867	0.778-0.956	0.810	0.800	0.810	0.805	0.802	0.806
CT-Tstage	Train	0.647	0.583-0.710	0.553	0.689	0.553	0.621	0.640	0.593
	Test	0.730	0.607-0.854	0.525	0.825	0.525	0.675	0.750	0.618
Nomogram	Train	0.964	0.944-0.983	0.800	0.789	0.800	0.795	0.791	0.795
	Test	0.955	0.900-0.999	0.952	0.900	0.952	0.928	0.905	0.919

AUC: Area under curve; ASVM: Arterial support vector machine; VSVM: Venous support vector machine.