Multicenter deep learning model for pancreatic cancer detection using endoscopic ultrasound

Table 6 Ablation study results on the external validation set

Methods	Precision (%) (95%CI)↑	Sensitivity (%) (95%CI)↑	Accuracy (%) (95%CI)↑	Specificity (%) (95%CI)↑	F1 score (%) (95%CI)↑
GFEb	83.62 (78.15-89.10)	74.00 (67.88-80.49)	78.11 (73.78-82.43)	82.94 (77.06-88.54)	78.51 (73.63-82.95)
LFEb	86.71 (81.39-92.03)	75.00 (68.93-81.42)	80.27 (75.95-84.32)	86.47 (81.03-91.47)	80.43 (75.80-84.85)
LFE + CSAb	87.10 (82.18-92.19)	81.00 (75.50-86.46)	83.24 (79.18-87.30)	85.88 (80.46-91.14)	83.94 (79.77-87.77)
LFE + GFEa	86.57 (82.03-91.35)	87.00 (82.16-91.67)	85.68 (82.16-89.19)	84.12 (78.33-89.54)	86.78 (83.24-90.10)
MCEUS-C2Net (ours)	91.88 (87.75-95.26)	90.50 (86.47-94.23)	90.59 (87.84-93.51)	90.59 (85.98-94.51)	91.18 (88.32-93.77)

^aP < 0.05 vs MCEUS-C2Net.

^bP < 0.01 vs MCEUS-C2Net.

Accuracy, precision, sensitivity, specificity, and F1 score are presented as (%). 95% confidence interval were calculated using the bootstrapping method with 1000 resamples. Statistical significance was evaluated using McNemar’s test, and all P values were adjusted for multiple comparisons using the Benjamini-Hochberg false discovery rate procedure. CSA: Channel self-attention; GFE: Global feature extraction; LFE: Local feature extraction; CI: Confidence interval.