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Table 4 Characteristics of ML-based predictive model after RFA

From: Current updates in machine learning in the prediction of therapeutic outcome of hepatocellular carcinoma: what should we know?

Author Study type No. of patients Modality Model Outcomes AUC Conclusion
Liang [69], 2014 Retrospective Single center 83 US guided SVM RR 0.69 The SA + RF SVM method had the best accuracy for predicting high-risk recurrent patients
Wu [70], 2017 Retrospective Single center 431 CT guided MLP 1,2-year DFS D: 0.94 (1-year)
0.88 (2-year)
V: 0.77 (1-year)
0.72 (2-year)
The MLP-based model with 15 clinical HCC relevant features achieved satisfactory prediction performance for 1-year DFS
  1. ML machine learning, RFA radiofrequency ablation, AUC area under the curve, US ultrasound, SVM support vector machine, RR recurrence rate, SA simulated annealing algorithm, RF random forest, CT computed tomography, MLP multilayer perceptron, DFS disease free survival, D development cohort, V validation cohort