Clinical trial NCT05200195: Deep Learning Model for the Prediction of Post-LT HCC Recurrence
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
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Liver Transplant Disorder
Liver Cancer
Recurrent Cancer
Keywords: artificial intelligence
deep learning
mathematical model
Lead sponsor: European Hepatocellular Cancer Liver Transplant Group
Sponsor class: OTHER
First posted: 2022-01-20
Enrollment: 4026
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: All the consecutive adult (≥18 years) patients enlisted and transplanted with the primary diagnosis of HCC during the period 2000-2018 in the 18 centers composing the International and the Validation Cohorts

Brief 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.

Detailed 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.

Eligibility criteria: 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)

Primary outcomes:
measure: Post-transplant HCC recurrence
timeFrame: 5 years from liver transplantation
Secondary outcomes:
Other outcomes:
Locations:
city: Rome
country: Italy
facility: Quirino Lai
state: RM
Study officials:
affiliation: University of Roma La Sapienza
name: Quirino Lai, MD PhD
role: PRINCIPAL_INVESTIGATOR
References: