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

Table 4 Performance metrics of the ResNet50 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	0.962	92.71	79.30	95.34	76.87	95.93	20.70	0.781
Val	0.871	83.65	76.49	85.05	50.00	94.87	23.51	0.605
Test	0.844 (0.798–0.884)	81.74 (78.01–85.06)	59.62 (45.44–72.73)	84.42 (80.84–87.65)	31.63	94.53	40.38	0.413

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.