Copyright: ©Author(s) 2026.
World J Gastrointest Oncol. Sep 15, 2026; 18(9): 119889
Published online Sep 15, 2026. doi: 10.4251/wjgo.119889
Published online Sep 15, 2026. doi: 10.4251/wjgo.119889
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) |
- Citation: Yu XY, Ye JQ, He Z, He Q. Multicenter deep learning model for pancreatic cancer detection using endoscopic ultrasound. World J Gastrointest Oncol 2026; 18(9): 119889
- URL: https://www.wjgnet.com/1948-5204/full/v18/i9/119889.htm
- DOI: https://dx.doi.org/10.4251/wjgo.119889