Endoscopic ultrasound-based deep learning for predicting chemotherapy response in unresectable pancreatic ductal adenocarcinoma

Table 5 Performance metrics of the ResNeXt50 model for chemotherapy response prediction in the training, internal validation, and independent test cohorts

Group	AUC (95%CI)	ACC (%) (95%CI)	SEN (%) (95%CI)	SPE (%) (95%CI)	PPV (%)	NPV (%)	FNR (%)	F1
Train	0.971	93.34	83.51	95.27	77.52	96.73	16.49	0.804
Val	0.896	88.81	75.79	91.36	63.16	95.07	24.21	0.689
Test	0.848 (0.798–0.890)	82.16 (78.63–85.48)	65.38 (51.06–77.78)	84.19 (80.96–87.30)	33.33	95.26	34.62	0.442

95% confidence intervals (CIs) for the area under the receiver operating characteristic curve were computed using the DeLong method; 95%CIs for accuracy, sensitivity, and specificity were computed using the Wilson score interval method. ACC: Accuracy; AUC: Area under the receiver operating characteristic curve; CI: Confidence interval; F1: F1-score; FNR: False negative rate; NPV: Negative predictive value; PPV: Positive predictive value; SEN: Sensitivity; SPE: Specificity; Test: Independent test cohort; Train: Training cohort; Val: Internal validation cohort.