Machine learning-based radiomic nomogram from unenhanced computed tomography and clinical data predicts bowel resection in incarcerated inguinal hernia

Table 4 Predictive performance of clinical model, radiomic signature and nomogram in the training and test sets

Group	Signature	AUC (95%CI)	ACC	SEN	SPE	PPV	NPV
Training set	Clinic signature	0.760 (0.679-0.840)	0.714	0.707	0.723	0.460	0.878
	Rad signature	0.828 (0.753-0.902)	0.832	0.610	0.908	0.694	0.872
	Nomogram	0.864 (0.800-0.929)	0.845	0.707	0.892	0.690	0.899
Test set	Clinic signature	0.747 (0.598-0.897)	0.811	0.533	0.946	0.727	0.833
	Rad signature	0.791 (0.668-0.915)	0.698	0.933	0.622	0.483	0.958
	Nomogram	0.800 (0.669-0.931)	0.830	0.667	0.895	0.714	0.872

AUC: Area under the curve; CI: Confidence interval; ACC: Accuracy; SEN: Sensitivity; SPE: Specificity; PPV: Positive predictive value; NPV: Negative predictive value.