Copyright: ©Author(s) 2026.
Artif Intell Gastroenterol. Aug 8, 2026; 7(2): 116057
Published online Aug 8, 2026. doi: 10.35712/aig.v7.i2.116057
Published online Aug 8, 2026. doi: 10.35712/aig.v7.i2.116057
Table 7 Artificial intelligence-supported decision algorithm
| Predicted parameter | Threshold | Suggested clinical action |
| Predicted PHLF risk | > 20%-25% | Avoid major hepatectomy; consider LVD or two-stage strategies |
| 10%-20% | Prefer LVD over PVE; optimize FLR function, nutrition | |
| < 10% | PVE adequate; proceed with resection after hypertrophy | |
| Predicted hypertrophy insufficient after PVE (model forecast) | < 25% expected growth | Prefer primary LVD |
| Tumor progression risk during waiting period | High | Consider ALPPS or accelerated LVD |
| Functional FLR prediction (radiomics/MRI) | Below functional cutoffs | Avoid ALPPS; prefer staged or non-surgical options |
- Citation: Agrawal H, Gupta N, Tanwar H. Artificial intelligence in expanding hepatic resection boundaries: Integrating portal flow modulation and regenerative strategies. Artif Intell Gastroenterol 2026; 7(2): 116057
- URL: https://www.wjgnet.com/2644-3236/full/v7/i2/116057.htm
- DOI: https://dx.doi.org/10.35712/aig.v7.i2.116057