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

Table 2 Performance metrics of the VGG19 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	0.963	91.11	82.81	92.73	69.01	96.50	17.19	0.753
Val	0.758	79.00	58.25	83.06	40.19	91.05	41.75	0.476
Test	0.614 (0.532–0.692)	71.58 (67.84–75.31)	36.54 (22.91–50.00)	75.81 (71.82–79.49)	15.45	90.81	63.46	0.217

95% confidence intervals (CIs) for area under the receiver operating characteristic curve were computed using the DeLong method, whereas 95%CIs for accuracy, sensitivity, and specificity were computed using the Wilson score interval method. CIs are reported for the independent test cohort only. AUC: Area under the receiver operating characteristic curve; ACC: Accuracy; 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.