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Identification of Image Phenotypes to Predict Recurrence After Resection of Hepatocellular Carcinoma (LIVERIBIOPSY)

10. februar 2022 oppdatert av: Assistance Publique - Hôpitaux de Paris

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.

Studieoversikt

Status

Fullført

Forhold

Intervensjon / Behandling

Detaljert beskrivelse

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

Observasjonsmessig

Registrering (Faktiske)

100

Kontakter og plasseringer

Denne delen inneholder kontaktinformasjon for de som utfører studien, og informasjon om hvor denne studien blir utført.

Studiesteder

  • Frankrike
      • Villejuif, Frankrike, 94800
        • Paul Brousse Hospital

Deltakelseskriterier

Forskere ser etter personer som passer til en bestemt beskrivelse, kalt kvalifikasjonskriterier. Noen eksempler på disse kriteriene er en persons generelle helsetilstand eller tidligere behandlinger.

Kvalifikasjonskriterier

Alder som er kvalifisert for studier

18 år og eldre (Voksen, Eldre voksen)

Tar imot friske frivillige

Nei

Kjønn som er kvalifisert for studier

Alle

Prøvetakingsmetode

Ikke-sannsynlighetsprøve

Studiepopulasjon

Data of patients who has hepatectomy (resection R0) for an HCC treatment will be collected from January 2011 to December 2019.

Beskrivelse

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

Studieplan

Denne delen gir detaljer om studieplanen, inkludert hvordan studien er utformet og hva studien måler.

Hvordan er studiet utformet?

Designdetaljer

Hva måler studien?

Primære resultatmål

Resultatmål
Tiltaksbeskrivelse
Tidsramme
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.
Tidsramme: 2 years
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.
2 years

Sekundære resultatmål

Resultatmål
Tiltaksbeskrivelse
Tidsramme
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.
Tidsramme: 2 years
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.
2 years

Andre resultatmål

Resultatmål
Tiltaksbeskrivelse
Tidsramme
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.
Tidsramme: 1 year
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.
1 year

Samarbeidspartnere og etterforskere

Det er her du vil finne personer og organisasjoner som er involvert i denne studien.

Sponsor

Assistance Publique - Hôpitaux de Paris

Samarbeidspartnere

Median Technologies

Etterforskere

  • Hovedetterforsker: Maïté LEWIN, Professor, Paul Brousse Hospital

Studierekorddatoer

Disse datoene sporer fremdriften for innsending av studieposter og sammendragsresultater til ClinicalTrials.gov. Studieposter og rapporterte resultater gjennomgås av National Library of Medicine (NLM) for å sikre at de oppfyller spesifikke kvalitetskontrollstandarder før de legges ut på det offentlige nettstedet.

Studer hoveddatoer

Studiestart (Faktiske)

28. januar 2021

Primær fullføring (Faktiske)

28. januar 2022

Studiet fullført (Faktiske)

9. februar 2022

Datoer for studieregistrering

Først innsendt

20. desember 2021

Først innsendt som oppfylte QC-kriteriene

10. februar 2022

Først lagt ut (Faktiske)

11. februar 2022

Oppdateringer av studieposter

Sist oppdatering lagt ut (Faktiske)

11. februar 2022

Siste oppdatering sendt inn som oppfylte QC-kriteriene

10. februar 2022

Sist bekreftet

1. februar 2022

Mer informasjon

Begreper knyttet til denne studien

Nøkkelord

Ytterligere relevante MeSH-vilkår

Andre studie-ID-numre

  • APHP191113

Plan for individuelle deltakerdata (IPD)

Planlegger du å dele individuelle deltakerdata (IPD)?

NEI

Legemiddel- og utstyrsinformasjon, studiedokumenter

Studerer et amerikansk FDA-regulert medikamentprodukt

Nei

Studerer et amerikansk FDA-regulert enhetsprodukt

Nei

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