Radiomics and deep learning predict neoadjuvant immunochemotherapy response in locally advanced esophageal squamous cell carcinoma

Table 2 Predictive performances of the best performing models developing on different kind of features

Model	Cohort	AUC	95%CI	Sensitivity	Specificity	NPV	PPV	Accuracy
Clinical model	Train	0.998	0.995-1.000	0.927	1.000	0.957	1.000	0.972
	Internal validation	0.899	0.813-0.985	0.720	0.909	0.741	0.900	0.809
	External validation	0.636	0.441-0.830	0.700	0.571	0.700	0.571	0.647
Radiomics model	Train	0.996	0.989-1.000	0.951	0.985	0.970	0.975	0.972
	Internal validation	0.904	0.816-0.991	0.600	0.955	0.677	0.938	0.766
	External validation	0.439	0.228-0.650	0.450	0.429	0.529	0.353	0.441
Deep learning model	Train	0.985	0.968-1.000	0.951	0.924	0.968	0.886	0.935
	Internal validation	0.820	0.699-0.941	0.760	0.864	0.760	0.864	0.809
	External validation	0.664	0.470-0.859	0.900	0.429	0.750	0.692	0.706
Radiomics-deep learning model	Train	0.998	0.994-1.000	0.927	1.000	0.957	1.000	0.972
	Internal validation	0.855	0.742-0.967	0.600	0.864	0.655	0.833	0.723
	External validation	0.779	0.609-0.948	1.000	0.357	1.000	0.690	0.735
Clinical-radiomics-deep learning model	Train	1.000	1.000-1.000	1.000	1.000	1.000	1.000	1.000
	Internal validation	0.887	0.794-0.980	0.680	0.909	0.714	0.895	0.787
	External validation	0.643	0.450-0.836	0.700	0.286	0.400	0.583	0.529

AUC: Area under the curve; NPV: Negative predictive value; PPV: Positive predictive value.