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©The Author(s) 2021.
World J Gastrointest Oncol. Nov 15, 2021; 13(11): 1599-1615
Published online Nov 15, 2021. doi: 10.4251/wjgo.v13.i11.1599
Published online Nov 15, 2021. doi: 10.4251/wjgo.v13.i11.1599
Ref. | Application task | Study design | Imaging modality | Radiomics features | Algorithm | Sample size | Training set | Test/validation set | Performance |
Liu et al[29], 2021 | Differentiation of cHCC-CC from HCC and CC | Retrospective, single-center | CT, MRI | 1419 | SVM | 85 patients with HCC (37), cHCC-CC (24) and CC (24) | 85 | NA | Excellent performance for differentiation of HCC from non-HCC (AUC: 0.79-0.81 in MRI, AUC: 0.71-0.81 in CT) |
Nie et al[32], 2020 | Differentiation of HCA from HCC | Retrospective, two-institutes | CT | 3768 | mRMR, LASSO | 131 patients with HCC (85) and HCA (46) | 93 | 38 | Favorable performance (AUC: 0.96 in training set, AUC: 0.94 in test set) |
Wu et al[33], 2019 | Pathological grade of HCC | Retrospective, single-center | MRI | 656 | LASSO | 170 patients with HCCs | 125 | 45 | Radiomics signature model outperformed the clinical factors-based model; the combined model achieved the best performance (AUC: 0.80) |
Mao et al[38], 2020 | Pathological grade of HCC | Retrospective, single-center | CT | 3376 | RFE, XGBoost | 297 patients with HCCs | 237 | 60 | The radiomics signatures combined with clinical factors significantly achieved the best performance (AUC: 0.8014) |
Xu et al[43], 2019 | Preoperative prediction of MVI in HCC | Retrospective, single-center | CT | 7260 | Ref-SVM, Multivariable logistic regression | 495 patients with HCC | 300 | 145 (test); 50 (validation) | Good performance (AUC: 0.909 in the training/validation set, AUC: 0.889 in the test set) |
Chong et al[47], 2021 | Preoperative prediction of MVI in HCC | Retrospective, single-center | MRI | 854 | LASSO, RF, logistic regression | 356 patients with HCCs ≤ 5 cm | 250 | 106 | AUC: 0.920 using RF; AUC: 0.879 using logistic regression (in validation set) |
Fu et al[54], 2019 | Assistant in optimal treatment choices of HCC between LR and TACE | Retrospective, multi-center (5 institutions) | MRI | 708 | LASSO, Akaike information criterion | 520 patients with HCC | 302 | 218 | Good discrimination and calibrations for 3-year PFS (AUC: 0.80 in training set, AUC: 0.75 in validation set); threshold ≤ -5.00: suggesting LR, threshold > -5.00: suggesting TACE |
Sun et al[56], 2020 | Predicting the outcome of TACE for unresectable HCC | Retrospective, single-center | MRI | 3376 | LASSO, multivariable logistic regression | 84 patients with BCLC B stage HCC | 67 | 17 | The radiomics signatures combined with clinical factors significantly achieved the best performance (AUC: 0.8014) |
Ji et al[66], 2020 | Predicting early recurrence after LR | Retrospective, multi-center (3 institutions) | CT | 846 | LASSO-Cox regression | 295 patients with HCC | 177 (Institution 1) | 118 (Institution 2 and 3, external validation) | Better prognostic ability (C-index: 0.77, P < 0.05), lower prediction error (integrated brier score: 0.14), and better clinical usefulness than rival models and staging systems |
Zhao et al[67], 2020 | Predicting early recurrence after LR | Retrospective, single-center | MRI | 1146 | LASSO, stepwise and multivariable logistic regression | 113 patients with HCC | 78 | 35 | The nomogram integrating the Rad score and clinicopathologic-radiologic risk factors showed better discrimination and clinical utility (AUC: 0.873) |
Wang et al[75], 2020 | Predicting 5-year survival after LR | Retrospective, multi-center (2 institutions) | MRI | 3144 | RF, multivariate logistic regression | 201 patients with HCC | 160 | 41 (five-fold cross-validation) | The model incorporating the radiomics signature and clinical risk factors obtained good calibration and satisfactory discrimination (AUC: 0.9804 in training set, AUC: 0.7578 in validation set) |
Song et al[76], 2020 | Predicting RFS after TACE | Retrospective, single-center | MRI | 396 | LASSO-Cox regression, multivariate Cox regression | 184 patients with HCC | 110 | 74 | The model using the radiomics signature with the clinical-radiological risk factors showed the best performance (C-index: 0.802) |
- Citation: Yao S, Ye Z, Wei Y, Jiang HY, Song B. Radiomics in hepatocellular carcinoma: A state-of-the-art review. World J Gastrointest Oncol 2021; 13(11): 1599-1615
- URL: https://www.wjgnet.com/1948-5204/full/v13/i11/1599.htm
- DOI: https://dx.doi.org/10.4251/wjgo.v13.i11.1599