Clinical trial · Observational
Validation of the TRAIN-AI for the Risk of HCC Recurrence After Liver Transplantation
Validation of the TRAIN-AI Score for the Prediction of Hepatocellular Carcinoma Recurrence After Liver Transplantation
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (3)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Deep Learning Model | — | UNRESOLVED | — |
| Hepatocellular Carcinoma (HCC) | Hepatocellular Carcinoma | ONTOLOGY_EXACT | 0.85 |
| Liver Transplantation | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| liver transplantation | Procedure | — | UNRESOLVED |
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- HCC recurrence
- timeFrame
- The final follow-up date was December 31, 2023.
- description
- HCC recurrence was defined as any hepatic or extra-hepatic tumor reappearance after LT, with recurrence time calculated from LT to detection.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 75 Years
Show eligibility criteria text
Inclusion Criteria: * Eligible participants were adult patients listed and transplanted with a primary diagnosis of HCC between January 2003 and December 2018. Exclusion Criteria: * incidentally discovered HCC in the explanted liver; * retransplantation or multivisceral transplantation; * tumors misclassified as HCC on radiological assessment (e.g., cholangiocarcinoma, mixed HCC-cholangiocarcinoma); * incomplete data for calculating the TRAIN-AI score.
References
Publications (1)
- RESULTLai Q, De Stefano C, Emond J, Bhangui P, Ikegami T, Schaefer B, Hoppe-Lotichius M, Mrzljak A, Ito T, Vivarelli M, Tisone G, Agnes S, Ettorre GM, Rossi M, Tsochatzis E, Lo CM, Chen CL, Cillo U, Ravaioli M, Lerut JP; EurHeCaLT and the West-East LT Study Group. Development and validation of an artificial intelligence model for predicting post-transplant hepatocellular cancer recurrence. Cancer Commun (Lond). 2023 Dec;43(12):1381-1385. doi: 10.1002/cac2.12468. Epub 2023 Oct 30. No abstract available. PMID 37904670