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 hepatectomy and DeathTACE) TACE ) were constructed to predict the probability of patient death after treatment (hepatectomy and TACE) by combining clinical and radiomics features, and an optimal treatment regimen was recommended. In addition, a prognostic model was constructed to predict the survival time of all patients. 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 hepatectomy and DeathTACE, TACE , patients were divided into the hepatectomy-preferred and TACE-preferred groups. In the hepatectomy-preferred group, hepatectomy had a significantly prolonged survival time than TACE (training cohort: P < 0.001; testing cohort: P < 0.001), and vise versa for the TACE-preferred group. In addition, the prognostic model yielded high predictive capability for overall survival. 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.