Table 6.
Predictive accuracy of machine algorithm models for liver cancer outcomes
| Prediction Target | Author | Machine algorithm | AUC/C-index |
|---|---|---|---|
| PFS | Bartnik [38] | Deep Learning | - |
| PFS | Quan [45] | Deep Learning | 0.826 |
| Treatment Response | Muller [37] | CNN | - |
| Treatment Response | Peng [42] | Deep Learning | 0.994 |
| Treatment Response | Xu [39] | DLRN | 0.988 |
| CR Prediction | Marinelli [46] | XGBoost | 0.89 |
| OS | Ma [41] | RF | 0.91 |
| OS | Sato [60] | Transformer Model | - |
| LTP | Ren [64] | CatBoost | 0.898 |
| LTP | Shahveranova [66] | - | 0.981 |
PFS: Progression-Free Survival; LTP: Local Tumor Progression; CNN: Convolutional Neural Network.