Artificial intelligence-based model for the recurrence of hepatocellular carcinoma after liver transplantation

Background: Artificial intelligence-based models might improve patient selection for liver transplantation in hepatocellular carcinoma. The objective of the current study was to develop artificial intelligence-based deep learning models and determine the risk of recurrence after living donor liver transplantation for hepatocellular carcinoma. Methods: The study was a single-center retrospective cohort study. Patients who underwent living donor liver transplantation for hepatocellular carcinoma were divided into training and validation cohorts (n 1/4 192). The deep learning models were used to stratify patients in the training cohort into low- and highrisk groups, and 5-year recurrence-free survival was assessed in the validation cohort. Results: The median follow-up period was 59.1 (33.9-72.4) months. The artificial intelligence model (pretransplant factors) had an area under the curve of 0.86 in the training cohort and 0.71 in the validation cohort. The largest tumor diameter and alpha-fetoprotein level had the greatest Shapley Additive exPlanations values for recurrence (>0.4). The 5-year recurrence-free survival rates in the low- and highrisk groups were 92.6% and 45% (P < .001). In the second artificial intelligence model (pretransplant factors & thorn; grade), the area under the curve for the validation cohort was 0.77, with 5-year recurrence-free survival rates of 96% and 30% in the low- and high-risk groups (P < .001). None of the low-risk patients outside the Milan and University of California San Francisco Criteria had recurrence during follow-up. Conclusions: The artificial intelligence-based hepatocellular carcinoma transplant recurrence models might improve patient selection for liver transplantation. (c) 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.