Deep Learning Model for the Prediction of Post-LT HCC Recurrence (TRAIN-AI)

European Hepatocellular Cancer Liver Transplant Group

Status: Completed

Conditions: Recurrent Cancer; Liver Transplant Disorder; Liver Cancer

Treatments: Procedure: Liver transplantation

Study type: Observational

Funder types: Other

Identifiers

NCT05200195

#004

Details and patient eligibility

About

Identifying patients at high risk for recurrence of hepatocellular carcinoma (HCC) after liver transplantation (LT) represents a challenging issue. The present study aims to develop and validate an accurate post-LT recurrence prediction calculator using the machine learning method.

Full description

In 1996, the introduction of the Milan criteria (MC) strongly modified the selection process of hepatocellular cancer (HCC) patients waiting for liver transplantation (LT). Many attempts to widen MC have been proposed. Initially, exclusively morphology-based (nodules number and target lesion diameter) criteria were created. In the last years, extended criteria also based on biological parameters have been added. Among the most adopted biology-based features, the levels of different tumor markers, liver function parameters like the model for end-stage liver disease (MELD), the radiological response after neo-adjuvant therapies, and the length of waiting-time (WT) can be reported.

Unfortunately, all the proposed models showed suboptimal prediction abilities for the risk of post-LT recurrence. Such impairment was derived from the limitations of the standard statistical methods to account for many variables and their non-linear interactions. Therefore, developing a model based on Artificial Intelligence (AI) represents an attractive way to improve prediction ability.

Thus, the investigators hypothesize that an AI model focused on an accurate post-transplant HCC recurrence prediction should improve our ability to pre-operatively identify patients with different classes of risk for HCC recurrence after transplant.

This study aims to develop an AI-derived prediction model combining morphology and biology variables. A Training Set derived from an International Cohort was adopted for doing this. A Test Set derived from the same International Cohort and a Validation Cohort were adopted for the internal and external validation, respectively. A user-friendly web calculator was also developed.

Enrollment: 4,026 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Consecutive adult (≥18 years) patients enlisted and transplanted with the primary diagnosis of HCC during the period 2000-2018.

Exclusion criteria

• Patients with HCC diagnosed only at pathological examination (incidental HCC)
• Patients with mixed hepatocellular-cholangiocellular cancer misdiagnosed as HCC
• Patients with cholangiocellular cancer misdiagnosed as HCC
• Patients dying early after LT (≤ one month)

Trial design

4,026 participants in 3 patient groups

International Cohort Training Set

Description:

The Training Set of the International Cohort (N=3,670) was composed of the 80% (n=2936) HCC patients transplanted from 2000 to 2018 across 17 centers in Europe and Asia.

Treatment:

Procedure: Liver transplantation

International Cohort Test Set

Description:

The Test Set of the International Cohort (N=3,670) was composed of the 20% (n=734) HCC patients transplanted from 2000 to 2018 across 17 centers in Europe and Asia.

Treatment:

Procedure: Liver transplantation

Validation Cohort

Description:

The external Validation Cohort was composed of 356 HCC patients transplanted at the Columbia University, New York, during the period 2000-2018.

Treatment:

Procedure: Liver transplantation