©The Author(s) 2025.
World J Hepatol. Dec 27, 2025; 17(12): 111425
Published online Dec 27, 2025. doi: 10.4254/wjh.v17.i12.111425
Published online Dec 27, 2025. doi: 10.4254/wjh.v17.i12.111425
Table 2 Quality assessment of the included studies using the GradePRO tool
| Outcome | Number of studies | Study design | Risk of bias | Inconsistency | Indirectness | Imprecision | Certainty of evidence |
| AUC | 8 | Retro | Moderate | Low | Low | Moderate | Low |
| Accuracy | 5 | Retro | Moderate | High | Moderate | High | Very low |
| C-Index | 2 | Retro | Moderate | Moderate | Low | Moderate | Low |
| Sens/Spec | 5 | Retro | Moderate | Moderate | Low | Moderate | Low |
| PPV/NPV | 4 | Retro | Moderate-High | High | Moderate | High | Very low |
- Citation: Posa A, Lippi M, Barbieri P, Andreani EV, Iezzi R. Performance of artificial intelligence in predicting hepatocellular carcinoma recurrence after thermal ablation: A systematic review. World J Hepatol 2025; 17(12): 111425
- URL: https://www.wjgnet.com/1948-5182/full/v17/i12/111425.htm
- DOI: https://dx.doi.org/10.4254/wjh.v17.i12.111425