Abstract

The clinical and follow-up data of 118 patients after hepatocellular carcinoma liver transplantation who met the three criteria were retrospectively analyzed. Predictive model scores were obtained using R3.4.3 software, and the cutoff values of the model were determined using the survival decision tree approach. Kaplan-Meier survival curves of the predictive model for patients after hepatocellular carcinoma liver transplantation under the three liver transplantation criteria were plotted and group-to-group differences were analyzed using log-rank tests. The predictive efficacy of the prediction model was examined using subject work characteristics curves. The support vector machine model is useful for meeting the up-to-seven criterion, the University of California, San Francisco criterion, and the Milan criterion. In addition to clinical and follow-up data, a pharmacologic perspective could provide important additional information for this study. Pharmacologic therapy is crucial in patients after liver transplantation, especially the use of anti-rejection and anti-cancer drugs. After liver transplantation, patients' liver function may be affected to varying degrees. The metabolic pathways of drugs may be altered, which is also important for developing personalized treatment plans and predicting survival. Different drugs may cause different side effects after liver transplantation, such as liver injury and abnormal kidney function. These side effects may affect patient survival rates and the prediction results of support vector machine model. Regular monitoring of drug concentrations and patients' drug responses can help to adjust the treatment regimen and thus improve survival and prognosis.