Preoperative CT-based deep learning radiomics model to predict lymph node metastasis and patient prognosis in bladder cancer: a two-center study

Insights into Imaging

Table 5 Results of combined model, radiomics signature, and the clinical model predictive ability for LNM status

Set	Model	AUC (95%CI)	ACC	SEN	SPE	PPV	NPV
Training	Combined model	0.980 (0.967–0.993)	0.932	0.939	0.925	0.926	0.938
	Radiomics signature	0.969 (0.951–0.987)	0.915	0.918	0.912	0.912	0.918
	Clinical model	0.764 (0.697–0.831)	0.843	0.395	0.959	0.714	0.860
External test	Combined model	0.834 (0.659–1.000)	0.870	0.400	0.977	0.800	0.878
	Radiomics signature	0.893 (0.769–1.000)	0.852	0.400	0.955	0.667	0.875
	Clinical model	0.624 (0.402–0.846)	0.833	0.300	0.955	0.600	0.857

AUC area under the receiver operating characteristic curve, CI confidence interval, ACC accuracy, SEN sensitivity, SPE specificity, PPV positive predictive value, NPV negative predictive value