Identification of Image Phenotypes to Predict Recurrence After Resection of Hepatocellular Carcinoma
NCT ID: NCT05235490
Last Updated: 2022-02-11
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
100 participants
OBSERVATIONAL
2021-01-28
2022-02-09
Brief Summary
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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.
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Detailed Description
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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.
Conditions
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Study Design
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COHORT
RETROSPECTIVE
Interventions
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Non intervention
Data study with inclusion of patients and retrospective clinical data collection, combining :
* Proofreading by radiologist of the CT scan performed (within 2 months prior to surgical intervention)
* Proofreading by iBiopsy® of the CT scan performed (within 2 months prior to surgical intervention)
* Proofreading of tumor sample slides by pathologists
* Patients follow-up (imaging, clinical)
* Recurrence-free survival
Eligibility Criteria
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Inclusion Criteria
* 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
* 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
18 Years
ALL
No
Sponsors
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Median Technologies
INDUSTRY
Assistance Publique - Hôpitaux de Paris
OTHER
Responsible Party
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Principal Investigators
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Maïté LEWIN, Professor
Role: PRINCIPAL_INVESTIGATOR
Paul Brousse Hospital
Locations
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Paul Brousse Hospital
Villejuif, , France
Countries
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Other Identifiers
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APHP191113
Identifier Type: -
Identifier Source: org_study_id
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