Preoperative prediction of perineural invasion of rectal cancer based on a magnetic resonance imaging radiomics model: A dual-center study

Table 3 Performance of various predictive models in the training, internal validation and external validation groups

Models	Training group	Internal validation group	External validation group
T2WI
AUC (95%CI)	0.817 (0.733-0.901)	0.763 (0.626-0.900)	0.759 (0.644-0.875)
Sensitivity	0.564	0.294	0.480
Specificity	0.899	0.886	0.857
Positive predictive value	0.733	0.556	0.632
Negative predictive value	0.807	0.721	0.764
T1CE
AUC (95%CI)	0.798 (0.707-0.890)	0.689 (0.521-0.857)	0.841 (0.752-0.930)
Sensitivity	0.487	0.471	0.480
Specificity	0.937	0.857	0.878
Positive predictive value	0.792	0.615	0.667
Negative predictive value	0.787	0.769	0.768
T2WI + T1CE
AUC (95%CI)	0.839 (0.757-0.921)	0.787 (0.650-0.923)	0.836 (0.735-0.937)
Sensitivity	0.641	0.529	0.560
Specificity	0.899	0.914	0.939
Positive predictive value	0.758	0.750	0.824
Negative predictive value	0.835	0.800	0.807
Clinical model
AUC (95%CI)	0.804 (0.727-0.881)	0.828 (0.719-0.937)	0.813 (0.724-0.903)
Sensitivity	0.718	0.706	0.800
Specificity	0.747	0.829	0.694
Positive predictive value	0.583	0.667	0.571
Negative predictive value	0.843	0.829	0.872
CR model
AUC (95%CI)	0.889 (0.824-0.954)	0.889 (0.803-0.976)	0.894 (0.814-0.974)
Sensitivity	0.692	0.647	0.760
Specificity	0.924	0.886	0.899
Positive predictive value	0.818	0.733	0.792
Negative predictive value	0.859	0.838	0.880

T2WI: T2-weighted imaging; T1CE: Contrast-enhanced T1WI; CR: Clinical-radiomics prediction model; AUC: Area under receiver operating characteristic curve.