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NCT05200195 Clinical Trial

Deep Learning Model for the Prediction of Post-LT HCC Recurrence

Liver Cancer Clinical Trial

— TRAIN-AI  

Official title:
Development and Validation of a Deep Learning Model for the Prediction of Hepatocellular Cancer Recurrence After Transplantation: The Time-Radiological Response- AlphafetoproteIN-Artificial Intelligence Model

Clinical Trial Details — Status: Completed

Administrative data
NCT number NCT05200195
Other study ID # #004
Secondary ID
Status Completed
Phase
First received
Last updated
Start date January 15, 2020
Est. completion date March 15, 2022

Study information
Verified date June 2022
Source European Hepatocellular Cancer Liver Transplant Group
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

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.

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.

Recruitment information / eligibility
Status Completed
Enrollment 4026
Est. completion date March 15, 2022
Est. primary completion date December 15, 2021
Accepts healthy volunteers No
Gender All
Age group 18 Years and older
Eligibility 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)

Study Design

Related Conditions & MeSH terms

Intervention

Procedure:
• Liver transplantation
Deceased or living donor liver transplantation for the cure of hepatocellular cancer on cirrhosis

Locations
Country Name City State
Italy Quirino Lai Rome RM

Sponsors (1)
Lead Sponsor Collaborator
European Hepatocellular Cancer Liver Transplant Group

Country where clinical trial is conducted

Italy, 

Outcome
Type Measure Description Time frame Safety issue
Primary Post-transplant HCC recurrence Intra- and/or extrahepatic recidivism of HCC after liver transplantation 5 years from liver transplantation
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