Table 2 Characteristics of ML-based predictive model of HCC patients after hepatectomy
Author | Study type | No. of patients | Model | Outcomes | AUC/C-index | Conclusion |
|---|---|---|---|---|---|---|
Hamamoto [43], 1995 | Retrospective Single center | 65 | ANN | Death | – | In the study for predicting the died of hepatic dysfunction, ANN predicted the outcome of 11 patients in the validation group and achieved the accuracy of 100% |
Ho [44], 2012 | Retrospective Multicenter | 427 | ANN and DT | 1,3,5-year DFS | D: 0.977 and 0.734 (1-year) 0.989 and 0.825 (3-year) 0.963 and 0.675 (5-year) V: 0.777 and 0.718 (1-year) 0.774 and 0.561 (3-year) 0.864 and 0.627 (5-year) | The ANN outperforms DT in predicting DFS in post-surgical HCC patients |
Xu [48], 2012 | Retrospective Multicenter | 336 | SVM | RR | – | The SVM based on IHC features could identify HCC patients who are easily recurrence after surgery, and the predictive accuracy of SVM was 66.5% |
Chiu [45], 2013 | Retrospective Multicenter | 434 | ANN | 1,3,5-year survival | D: 0.980 (1-year) 0.989 (3-year) 0.993 (5-year) V: 0.875 (1-year) 0.798 (3-year) 0.810 (5-year) | The ANN model can process a greater number of predictors and had better accuracy than the traditional LR model |
Qiao [46], 2014 | Prospective Multicenter | 725 | ANN | 5-year survival | D: 0.855 V: 0.829 | The ANN model outperforms both Cox and other staging systems in predicting survival in HCC patients who have received surgical resection |
Cai [36], 2015 | Retrospective Single center | 299 | BN | 10-month survival | – | The BN model had 67.2% of accuracy to classify the survival time of post-surgical HCC patients |
Akai [49], 2018 | Retrospective Single center | 127 | RSF | DFS, OS | 0.611 0.701 | RSF can predict the individual risk for each patient on DFS and OS |
Wang [23], 2019 | Retrospective Single center | 167 | DCNN | RR | 0.825 | Combined clinical information and radiomics features can effectively predict early recurrence of HCC patients |
Kim [50], 2019 | Retrospective Single center | 167 | RSF1* RSF2** RSF3*** | Early recurrence Lately recurrence | Early recurrence: 0.671(RSF1) 0.679(RSF2) 0.707(RSF3) Early recurrence: 0.737(RSF1) 0.622(RSF2) 0.716(RSF3) | Compared to another two RSF models, combined clinicopathologic-radiomic RSF model achieved the highest predictive power for the recurrence within 2Â years after surgery of HCC, and has fair predictive performance for lately recurrence |
Xu [19], 2019 | Retrospective Multicenter | 1139 | SVM RF BN | RR | – | The accuracy of SVM, RF and BN model was 0.46, 0.48 and 0.56, respectively, in validation group form another independent institution. The BN model could contribute to HCC recurrence research |
Mai [47], 2020 | Retrospective Single center | 353 | ANN | PHLF | 0.880(D) 0.876(V) | The risk of severe PHIF in HCC patients after surgery based on ANN model, can be accurately divided into 3 groups |
Saillard [55], 2020 | Retrospective Multicenter | 522 | CNN1# CNN2## | OS | D: 0.75(CNN1) 0.78(CNN2) V: 0.68(CNN1) 0.70(CNN2) | Two CNN models based on histological features form WSIs performed well for predicting OS of HCC patients after surgery, and both CNN models outperformed the CS that the score included the relevant clinical, biological and pathological features |
Schoenberg [51], 2020 | Retrospective Single center | 180 | RF | DFS | D: 0.766(0.627–0.904) V: 0.788(0.658–0.919) | RF model based on clinical and laboratory variables, can accurately predict DFS after surgery of HCC |
Wang [52], 2020 | Retrospective Multicenter | 201 | RF | 5-year survival | D: 0.980 V: 0.758 | RAD model integrated with RF in a valid method to predict 5-year survival of post-operative HCC patients |
Liao [53], 2020 | Retrospective Multicenter | 645 | RF | 1,3,5-Y survival | V1: 0.626(1-year) 0.658(3-year) 0.581(5-year) V2: 0.600(1-year) 0.595(3-year) 0.566(5-year) | RF model based on 46 histopathplogical features, was able to stratify post-surgical patients of HCC into long and short-term groups. And the RF model showed similar accuracy with TNM staging systems |
Saito [54], 2020 | Retrospective Multicenter | 158 | SVM | RR | – | The SVM model based on digital pathological images has the accuracy of 89.9% for prediction of HCC recurrence after surgery |