Validation of the Toronto recurrence inference using machine-learning for post-transplant hepatocellular carcinoma model

Background: Organ shortages require prioritizing hepatocellular carcinoma (HCC) patients with the highest survival benefit for allografts. While traditional models like AFP, MORAL, and HALT-HCC are commonly used for recurrence risk prediction, the TRIUMPH model, which uses machine learning, has shown superior performance. This study aims to externally validate the model. Methods: The cohort included 2844 HCC patients who underwent liver transplantation at six international centers from 2000-2022. The TRIUMPH model utilized a regularized Cox proportional hazards approach with a penalty term for coefficient adjustment. Discrimination was assessed using the c-index, and clinical utility was evaluated via decision curve analysis. Results: The most common liver diseases are hepatitis C (49%) and hepatitis B (27%). At listing, 84% meets the Milan criteria, and 91% are within criteria at transplant. Median model for end-stage liver disease score is 10 (IQR:8–14), alpha-fetoprotein level 8 ng/mL (IQR:4–25), and tumor size 2 cm (IQR:1.1–3.0). Living donor grafts are used in 24% of cases. Recurrence rate is 9.1% with a median time to recurrence of 17.5 months. Recurrence-free survival rates at 1/3/5 years are 95.7%/89.5%/87.7%, respectively. The TRIUMPH model achieves the highest c-index (0.71), outperforming MORAL (0.61, p = 0.049) and AFP (0.61, p = 0.04), though not significantly better than HALT-HCC (0.67, p = 0.28). TRIUMPH shows superior clinical utility up to a threshold of 0.6. Conclusions: The TRIUMPH model demonstrates good accuracy and clinical utility in predicting post-transplant HCC recurrence. Its integration into organ allocation could improve transplantation outcomes.