Clinical trial · Observational
Identification of Image Phenotypes to Predict Recurrence After Resection of Hepatocellular Carcinoma
- 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)
Tumor recurrence, which occurs in 70% of patients with HCC within 5 years after hepatic resection, is a major cause of post-resection-death. This recurrence can be true recurrence (intrahepatic metastases), which occurs sooner than 2 years later, or it can be due to the development of de-novo tumors at least 2 years later. Despite this high rate of tumor recurrence, no anti-recurrence adjuvant therapies are currently recommended. Imaging phenomics is the systematic, large scale extraction of imaging features for the characterization and classification of disease phenotypes. Combining imaging and tissue phenomics could be a solution to predict HCC recurrence. With the emergence of molecular therapies and immunotherapies, identifying patients with HCC at high risk of post-resection recurrence would help determine additional therapeutic and management strategies in clinical practice.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| CT Scans Prior to Surgery With a Least 2 Years of Follow-up | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Non intervention | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- The main objective of this work is to identify biomarkers from CT scan (non-invasive imaging phenotypes from radiological images) which have a prognostic value for an early recurrence in patients with hepatocellular cancer.
- timeFrame
- 2 years
- description
- The primary endpoint will be built using machine learning method to obtain prediction of recurrence within 2 years. The Recurrence Free survival (RFS) within two years will be the reference outcome to evaluate the prognostic of the patients.
Secondary outcomes (1)
- measure
- Identify biomarkers from CT scan (non-invasive imaging phenotypes from radiological images) which have a prognostic value for a tardive recurrence in patients with hepatocellular cancer.
- timeFrame
- 2 years
- description
- A secondary endpoint which will be built using machine learning method to obtain prediction of recurrence after 2 years. The Recurrence Free survival (RFS) after two years will be the reference outcome to evaluate the prognostic of the patients.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * Age ≥ 18 years old * Patients who underwent surgery and have R0 resection after 2010 * Multiphase CT scans with contrast media should be performed within 2 months prior to surgical intervention * At least 2 years of follow-up data on intrahepatic recurrence Exclusion Criteria: * Previous HCC treatment * Combination of other anti-cancer treatment * Other malignancies * Patient expressly expressing opposition to the exploitation of their data as defined by the project * Protected adults
References
Publications (0)
Data not yet available