Identification of Image Phenotypes to Predict Recurrence After Resection of Hepatocellular Carcinoma (LIVERIBIOPSY)
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.
Studie Overzicht
Toestand
Interventie / Behandeling
Gedetailleerde beschrijving
Hepatocellular carcinoma (HCC) is among the most lethal and prevalent cancers in the human population and it is now the third leading cause of cancer deaths worldwide, with over 500,000 people affected. Because of the high recurrence rate after curative hepatectomy, accurate prognostic assessment in HCC patients are quite important. With the emergence of molecular therapies and immunotherapies, the identification of patients at high or low risk for recurrence after hepatic resection would help determine additional therapeutic and management strategies in clinical practice. Although many immunohistochemical markers have been reported to have a prognostic value for HCC patients, there is no consensus on how these markers could add prognostic value to the clinical parameters.
In the initial step of biomarker discovery, no specific sample size is provided, however to test hypothesis, 100 patients are required.
This first study will potentially be followed by a second similar study promoted by the same investigators to increase the statistical power to improve the classification tool according to the patient's future.
Period covered by the data collection: 2011-2019 / Duration data collection: 1 year.
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.
The secondary endpoint are following :
- 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.
- A secondary endpoint will be the correlation between biomarker from CT scan and pathological biomarkers As the spectrum of HCC disease is very large, many patients to conduct conclusive validation studies for diagnostic and prognostic relevance need to be obtained.
Overall, each specific-read out endpoint will include a sample size calculation and - if appropriate - a power analysis specific to the objective of this study.
During training, phenotyping system performance assessment will be done to guide the calculation of the sample size for the validation.
Studietype
Inschrijving (Werkelijk)
Contacten en locaties
Studie Locaties
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Villejuif, Frankrijk, 94800
- Paul Brousse Hospital
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Deelname Criteria
Geschiktheidscriteria
Leeftijden die in aanmerking komen voor studie
Accepteert gezonde vrijwilligers
Geslachten die in aanmerking komen voor studie
Bemonsteringsmethode
Studie Bevolking
Beschrijving
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
Studie plan
Hoe is de studie opgezet?
Ontwerpdetails
Wat meet het onderzoek?
Primaire uitkomstmaten
Uitkomstmaat |
Maatregel Beschrijving |
Tijdsspanne |
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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.
Tijdsspanne: 2 years
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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.
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2 years
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Secundaire uitkomstmaten
Uitkomstmaat |
Maatregel Beschrijving |
Tijdsspanne |
|---|---|---|
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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.
Tijdsspanne: 2 years
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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.
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2 years
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Andere uitkomstmaten
Uitkomstmaat |
Maatregel Beschrijving |
Tijdsspanne |
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To correlate the imaging signatures predictive of recurrence with the cell population molding of tissue microenvironment (TME) and the tumor biology using tissue assessment as reference.
Tijdsspanne: 1 year
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Correlation between biomarker from CT scan and nodule size, nodule differentiation (grade OMS), nodule capsule, macroscopie invasion, microscopic vascular invasion, macrotrabecular sub-type, satellite nodule, staging.
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1 year
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Medewerkers en onderzoekers
Medewerkers
Onderzoekers
- Hoofdonderzoeker: Maïté LEWIN, Professor, Paul Brousse Hospital
Studie record data
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Studie start (Werkelijk)
Primaire voltooiing (Werkelijk)
Studie voltooiing (Werkelijk)
Studieregistratiedata
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Eerst ingediend dat voldeed aan de QC-criteria
Eerst geplaatst (Werkelijk)
Updates van studierecords
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Laatste update ingediend die voldeed aan QC-criteria
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Meer informatie
Termen gerelateerd aan deze studie
Trefwoorden
Aanvullende relevante MeSH-voorwaarden
- Ziekten van het spijsverteringsstelsel
- Pathologische processen
- Neoplasmata per histologisch type
- Neoplasmata
- Neoplasmata per site
- Adenocarcinoom
- Neoplasmata, glandulair en epitheel
- Ziekte attributen
- Neoplasmata van het spijsverteringsstelsel
- Lever Ziekten
- Lever neoplasmata
- Carcinoom
- Carcinoom, hepatocellulair
- Herhaling
Andere studie-ID-nummers
- APHP191113
Plan Individuele Deelnemersgegevens (IPD)
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Informatie over medicijnen en apparaten, studiedocumenten
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