New prognostic model for liver transplantation outcomes in hepatocellular carcinoma

Abstract

Background. Liver transplantation remains a priority treatment option for hepatocellular carcinoma in the presence of liver cirrhosis; yet precise outcome prediction post-operation continues to be a complex challenge. Existing prognostic model often overlook patient age and donor type. Enhanced models that incorporate these parameters can improve prediction accuracy and treatment efficacy, which is critically important in the dynamically evolving field of transplantation.Objective. The aim of this study is to develop a prognostic model for liver transplantation outcomes in patients with hepatocellular carcinoma and liver cirrhosis.Material and methods. This retrospective study included 69 patients with hepatocellular carcinoma on the background of liver cirrhosis who underwent liver transplantation between May 2010 and December 2022. Of these, 42 patients (61%) received organs from living donors, and 27 (39%) from deceased donors. The study involved analysis of alpha-fetoprotein levels in blood, as well as assessment of radiological (maximum tumor nodule size, number of nodules) and histological parameters (maximum tumor nodule size, number of nodules, presence of vascular invasion). Cox regression model was used to predict recurrence-free survival, and the results for five-year recurrence-free survival, recipient age, and donor type were reused in the Cox model to predict overall survival.Results. Four models for predicting recurrence-free survival and overall survival based on histological and radiological data were developed, demonstrating high prognostic value with C-indexes on training/test data of 0.76/1; 0.73/1; 0.78/0.8; 0.6/0.8 respectively. All models showed recurrence-free survival prediction accuracy comparable to the Milan criteria. The model outcomes are available as a calculator on the website https://nadit.ru/calculate_HCC.Conclusion. The developed prognostic models are vital tools for personalized outcome prediction after liver transplantation for hepatocellular carcinoma. To enhance the accuracy of these models, further amalgamation and validation of data from various medical centers, as well as open scientific collaboration, are necessary.