Validation of the TRAIN-AI Score for the Prediction of Hepatocellular Carcinoma Recurrence After Liver Transplantation

Liver transplantation (LT) is the best treatment option for patients with early stages of hepatocellular carcinoma (HCC).1 However, the use of LT depends on maintaining a balance between the risk of post-transplant recurrence or HCC-related death and the equitable distribution of organ donors.2-5 Current selection criteria aim to avoid transplant futility by excluding patients from LT who are at a high risk of tumor recurrence. Selecting patients within the Milan criteria has been shown to provide excellent patient outcomes.6,7 However, these criteria have been challenged by other series showing equivalent outcomes for patients transplanted with a greater tumor burden. A combination of morphologic (i.e., tumor number and size) and biological features has been recently proposed with the intent to implement the patient selection process.8,9 Machine learning represents a statistical tool that can leverage the prognostic abilities of a many clinically available variables. Recently, the TRAIN-AI has been proposed, and a post-transplant HCC recurrence risk calculator using machine learning based on the TRAIN-AI score is available.10 We are seeking to explore the generalizability of this machine learning model to other institutions through a validation study.

Study aims and objective:

The primary objective of this study will be to validate our previously reported TRAIN-AI score using external datasets from other HCC centers.

Study design and methodology:

Validate the TRAIN-AI model by comparing it to other available recurrence risk algorithms on a held-out test set. TRAIN-AI will be compared with Milan Criteria, San Francisco Criteria, Up-to-Seven Criteria, TBS, Metroticket 2.0 Score, HALT-HCC Score, AFP-French model, 5-5-500 Role, NYCA Score, and TRAIN Score.