Deep learning radiomics nomogram based on multi-regional features for predicting lymph node metastasis and prognosis in colorectal cancer

Table 4 Comparison of the performance of three models in the training, internal validation and external validation cohorts

Cohort	Models	AUC	ACC	SEN	SPE	PPV	NPV
Training cohort	Clinical	0.703 (0.623-0.780)	0.695	0.630	0.730	0.557	0.785
	DLRR	0.935 (0.889-0.968)	0.870	0.852	0.880	0.793	0.917
	Combined	0.944 (0.905-0.973)	0.812	0.926	0.750	0.667	0.949
Internal validation cohort	Clinical	0.637 (0.527-0.751)	0.636	0.517	0.730	0.600	0.659
	DLRR	0.877 (0.789-0.951)	0.742	0.621	0.838	0.750	0.738
	Combined	0.878 (0.788-0.949)	0.773	0.793	0.757	0.719	0.824
External validation cohort	Clinical	0.624 (0.492-0.760)	0.638	0.583	0.676	0.560	0.697
	DLRR	0.849 (0.731-0.941)	0.759	0.875	0.676	0.656	0.885
	Combined	0.855 (0.737-0.946)	0.690	0.917	0.529	0.579	0.900

AUC: Area under the receiver operating characteristic curve; ACC: Accuracy; SEN: Sensitivity; SPE: Specificity; PPV: Positive predictive value; NPV: Negative predictive value; DLRR: Deep learning-radiomics integrated model.