Copyright: ©Author(s) 2026.
World J Gastrointest Surg. May 27, 2026; 18(5): 119310
Published online May 27, 2026. doi: 10.4240/wjgs.v18.i5.119310
Published online May 27, 2026. doi: 10.4240/wjgs.v18.i5.119310
Table 2 Analysis of differences in tumor burden and the distance from the tumor’s lowest border to the anal verge measurements by different methods
| MD | SD | t | P value | ICC (95%CI) | |
| Maximum tumor diameter (cm) | |||||
| Pathology-AI-3D | -0.602 | 0.510 | -7.367 | < 0.001 | 0.921 |
| AI-3D-CT | -0.106 | 1.400 | -0.471 | 0.640 | 0.482 |
| Pathology-CT | -0.708 | 1.327 | -3.332 | 0.002 | 0.518 |
| Maximum tumor cross-sectional area (cm2) | |||||
| Pathology-AI-3D | -0.150 | 4.031 | -0.233 | 0.817 | 0.846 |
| AI-3D-CT | 5.430 | 5.076 | 6.680 | < 0.001 | 0.517 |
| Pathology-CT | 5.280 | 6.898 | 4.780 | < 0.001 | 0.407 |
| DTAV (cm) | |||||
| AI-3D-colonoscopy | 2.079 | 6.415 | 2.024 | 0.050 | 0.907 |
- Citation: Wei JM, Chen SX, He T, Wang JF. Application of artificial intelligence-driven three-dimensional imaging in preoperative planning for rectosigmoid colon cancer. World J Gastrointest Surg 2026; 18(5): 119310
- URL: https://www.wjgnet.com/1948-9366/full/v18/i5/119310.htm
- DOI: https://dx.doi.org/10.4240/wjgs.v18.i5.119310