Multicenter deep learning model for pancreatic cancer detection using endoscopic ultrasound

Table 1 Baseline demographic and clinical characteristics of the study population, n (%)

Characteristics	Total cohort (n = 383)	Beijing Friendship Hospital (n = 302)	Beijing Tiantan Hospital (n = 81)
Age (years), mean ± SD	66.7 ± 12.5	67.2 ± 12.2	64.8 ± 13.1
Gender
Male	202 (52.7)	162 (53.6)	40 (49.4)
Female	181 (47.3)	140 (46.4)	41 (50.6)
Class distribution
Cancerous	225 (58.7)	181 (59.9)	44 (54.3)
Noncancerous	158 (41.3)	121 (40.1)	37 (45.7)
Lesion subtypes
Cancerous group
Pancreatic ductal adenocarcinoma	219 (57.2)	176 (58.3)	43 (53.1)
Pancreatic acinar cell carcinoma	6 (1.6)	5 (1.7)	1 (1.2)
Noncancerous group
Solid pseudopapillaryoma	72 (18.8)	56 (18.5)	16 (19.8)
Autoimmune pancreatitis	55 (14.4)	41 (13.6)	14 (17.3)
Neuroendocrine neoplasms	31 (8.1)	24 (7.9)	7 (8.6)

Table 2 Dual-center energy statistics for the two hospitals

Center	Mean	SD	Min	Max
Center A_0	0.061474	0.056234	0.008741	0.559571
Center A_1	0.047896	0.040044	0.009132	0.320019
Center B_0	0.021291	0.009237	0.007514	0.047186
Center B_1	0.023628	0.017217	0.009367	0.120883

Center A: Training data; Center B: External validation data; 0: Cancer category; 1: Noncancer category. Min: Minimum; Max: Maximum.

Table 3 Internal validation results

Methods	Precision (%) (95%CI)↑	Sensitivity (%) (95%CI)↑	Accuracy (%) (95%CI)↑	Specificity (%) (95%CI)↑	F1 score (%) (95%CI)↑
ResNet-50a	86.96 (82.17-90.95)	94.14 (91.47-97.49)	89.12 (86.41-92.05)	82.76 (77.43-88.30)	90.50 (87.76-93.16)
Swin_transformera	89.29 (83.64-92.31)	94.34 (90.48-96.97)	90.53 (86.92-92.82)	85.71 (79.89-90.64)	91.74 (88.02-93.65)
MedViTV2a	90.09 (85.32-93.24)	95.24 (91.54-97.66)	91.50 (87.95-93.85)	86.75 (81.71-91.33)	92.59 (89.20-94.48)
MCEUS-C2Net (ours)	93.46 (89.08-95.97)	97.09 (95.48-99.51)	94.51 (92.31-96.67)	91.14 (86.70-94.92)	95.24 (93.01-97.01)

^aP < 0.05 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. CI: Confidence interval.

Table 4 External validation results

Methods	Precision (%) (95%CI)↑	Sensitivity (%) (95%CI)↑	Accuracy (%) (95%CI)↑	Specificity (%) (95%CI)↑	F1 score (%) (95%CI)↑
ResNet-50b	84.57 (79.00-89.88)	74.00 (67.88-80.49)	78.65 (74.32-82.97)	84.12 (78.31-89.51)	78.93 (74.13-83.42)
Swin_transformerb	86.63 (81.87-91.24)	81.00 (75.26-86.26)	82.97 (78.92-86.49)	85.29 (79.88-90.30)	83.72 (79.49-87.35)
MedViTV2a	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. CI: Confidence interval.

Table 5 Ablation study results on the internal validation set

Methods	Precision (%) (95%CI)↑	Sensitivity (%) (95%CI)↑	Accuracy (%) (95%CI)↑	Specificity (%) (95%CI)↑	F1 score (%) (95%CI)↑
GFEb	86.85 (82.25-91.39)	88.10 (83.76-92.56)	86.41 (82.82-89.74)	84.44 (79.28-89.64)	87.47 (83.99-90.70)
LFEb	88.73 (84.46-92.72)	90.00 (85.92-93.97)	88.46 (85.13-91.54)	86.67 (81.82-91.33)	89.36 (86.15-92.38)
LFE + CSAb	90.05 (85.86-93.91)	90.48 (86.57-94.45)	89.49 (86.41-92.56)	88.33 (83.60-92.90)	90.26 (87.12-93.15)
LFE + GFEa	90.09 (85.32-93.24)	95.24 (91.54-97.66)	91.50 (87.95-93.85)	86.75 (81.71-91.33)	92.59 (89.20-94.48)
MCEUS-C2Net (ours)	93.46 (89.08-95.97)	97.09 (95.48-99.51)	94.51 (92.31-96.67)	91.14 (86.70-94.92)	95.24 (93.01-97.01)

^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.

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.