A Machine Learning Model Based on Counterfactual Theory for Treatment Decision of Hepatocellular Carcinoma Patients.

Purpose: To predict the efficacy of patients treated with hepatectomy and transarterial chemoembolization (TACE) based on machine learning models using clinical and radiomics features. Patients and Methods: Patients with HCC whose first treatment was hepatectomy or TACE from June 2016 to July 2021 were collected in the retrospective cohort study. To ensure a causal effect of treatment effect and treatment modality, perfectly matched patients were obtained according to the principle of propensity score matching and used as an independent test cohort. Inverse probability of treatment weighting was used to control bias for unmatched patients, and the weighted results were used as the training cohort. Clinical characteristics were selected by univariate and multivariate analysis of cox proportional hazards regression, and radiomics features were selected using correlation analysis and random survival forest. The machine learning models (Death Results: A total of 418 patients with HCC who received either hepatectomy (n=267, mean age, 58 years ± 11 [standard deviation]; 228 men) or TACE (n=151, mean age, 59 years ± 13 [standard deviation]; 127 men) were recruited. After constructing the machine learning models Death Conclusion: The machine learning models could predict the outcomes difference between hepatectomy and TACE, and prognostic models could predict the overall survival for HCC patients.