Computed tomography-based radiomics combined with machine learning allows differentiation between primary intestinal lymphoma and Crohn's disease

Table 2 The performance of models in the internal/external validation set

Dataset	Score	Radiomics (All phase)	Combined (radiomics + clinical)
Internal validation set	AUC	0.96 (95%CI: 0.90-1.00)	0.99 (95%CI: 0.96-1.00)
	Accuracy	0.92 (95%CI: 0.78-0.98)	0.94 (95%CI: 0.81-1.00)
	Sensitivity	0.93 (95%CI: 0.68-1.00)	1.00 (95%CI: 0.78-1.00)
	Specificity	0.91 (95%CI: 0.70-1.00)	0.91 (95%CI: 0.70-1.00)
External validation set	AUC	0.98 (95%CI: 0.94-1.00)	1.00 (95%CI: 0.99-1.00)
	Accuracy	0.93 (95%CI: 0.82-0.98)	1.00 (95%CI: 0.93-1.00)
	Sensitivity	0.96 (95%CI: 0.79-1.00)	1.00 (95%CI: 0.86-1.00)
	Specificity	0.90 (95%CI: 0.73-0.98)	1.00 (95%CI: 0.88-1.00)

AUC: Area under the curve; CI: Confidence interval.