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Table 3 Characteristics of ML-based predictive model of HCC patients after transplantation

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 Model Outcomes AUC/C-statistics Conclusion
Marsh [58], 1997 Retrospective Single center 214 ANN 1,2,3-year RR D: 0.962 ± 0.01 (1-year)
0.944 ± 0.05 (2-year)
0.952 ± 0.04 (3-year)
V: 0.962 ± 0.043 (1-year)
0.966 ± 0.025 (2-year)
0.971 ± 0.034 (3-year)
The ANN model can identify post-LT HCC patients with or without recurrence
Marsh [59], 2003 Retrospective Single center 214 ANN 1,2,3-year RR 0.98 (1-year)
0.95 (2-year)
0.96 (3-year)
The ANN has genotyping as input parameter, which is possible to predict recurrence risk of post-LT HCC
Rodriguez-Luna [60], 2005 Retrospective Single center 19 ANN Recurrence This study validates the result conducted by Marsh et al., which the model had the discrimination power of 89.5%
Zhang [61], 2012 Retrospective Single center 290 MLP 1,2,5-year survival 0.909 (1-year)
0.888 (2-year)
0.845 (5-year)
The MLP model had high accuracy to predict post-transplant mortality risk for HCC recipients
Nam [24], 2020 Retrospective Multicenter 563 DNN Recurrence 0.75 The DNN model showed promising predictive performance and outperformed other traditional predictive model to predict HCC recurrence after LT
  1. ML machine learning, HCC hepatocellular carcinoma, AUC area under the curve, ANN artificial neural network, RR recurrence rate, D development cohort, V validation cohort, LT liver transplantation, MLP multilayer perceptron, DNN deep neural network