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Table 5 Characteristics of ML-based predictive model of HCC patients after TACE

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 Conclusion
Abajian [77], 2018 Retrospective Single center 36 RF Responders or non-responders RF model combined with MRI parameters may be predicted tumor response of post-TACE HCC
Morshid [79], 2019 Retrospective Single center 105 RF TACE-susceptible or TACE-refractory 0.733 The accuracy of RF model using a combination of clinical parameters plus quantitative image features was higher than the RF model based on the clinical parameters alone, in the study of predicting HCC response to TACE
Mähringer-Kunz [76], 2020 Retrospective Single center 282 ANN 1-year survival V: 0.77 ± 0.13
D: 0.83 ± 0.06
The ANN model had a promising performance at predicting HCC patient survival after TACE and outperformed the traditional scoring systems
Peng [25], 2020 Retrospective Multicenter 798 CNN CR, PR, SD, PD D: 0.97 (CR)
0.96 (PR)
0.95 (SD)
0.96 (PD)
V: 0.98 (CR)
0.96 (PR)
0.95 (SD)
0.94 (PD)
The CNN model presented a good performance for predicting the outcome of TACE
Liu [78], 2020 Retrospective Single center 138 CNN
ORR D: 0.98 (CNN)
0.84 (SVM1)
0.82 (SVM2)
V: 0.93 (CNN)
0.80 (SVM1)
0.81 (SVM2)
CNN is better in predicting treatment response over SVM in HCC patients treated with TACE
  1. *SVM1: radiomics-based time-intensity curve of CEUS model using SVM; #SVM2: radiomics-based B-Mode images model using SVM
  2. ML machine learning, HCC hepatocellular carcinoma, TACE transarterial chemoembolization, AUC area under the curve, RF random forest, MRI magnetic resonance imaging, ANN artificial neural network, D development cohort, V validation cohort, CNN convolutional neural network, CR complete response, PR partial response, SD stable disease, PD progressive disease, SVM support vector machine, ORR objective response rate, CEUS contrast-enhanced ultrasound