Phenotyping Liver Cancer Registry

NCT04681274 | Completed

• 1 other identifier: APHP191046
• Study Type: observational
• Target: 2,429
• Locations: 1 country
• Sites: 1

Brief Summary

The purpose of this study is the development of a content-based image retrieval (CBIR) platform, where validation studies will be conducted for liver disease subtyping and hepatocellular carcinoma (HCC) phenotyping on images for use as diagnostic and prognostic markers of outcome in conjunction with large scale data registries and advanced predictive machine learning methodologies. The proposed objectives will deliver one or more fit-for-purpose non-invasive imaging-based methodologies to evaluate the presence, activity and type of HCC in clinical practice.

Conditions

Hepatocellular Carcinoma

Keywords

Liver cancer; Hepatocellular carcinoma; Phenotype; Clinical data registry; Diagnostic; Prediction; Retrospective; Artificial intelligence

Outcome Measures

Primary Outcomes (1)

• Specific tumor phenotypes

  accurately identify the specific tumor phenotypes to better diagnose and predict patient outcome in hepatocellular carcinoma

  2 years

Secondary Outcomes (4)

• Hepatocellular carcinoma detection and characterization

  2 years

• Repeatability and reproducibility

  2 years

• Imaging phenotypes qualification

  2 years

• Small lesion detection and characterization

  2 years

Study Arms (1)

patient with hepatocellular carcinoma

Phenotype signature database building Image features extraction and clustering

Device: Image features extraction and clustering; Device: Phenotype signature database building

Interventions

Image features extraction and clustering DEVICE

The image processing operations required for local content-based image feature extraction consist of two main tasks: 1) tiling the images in smaller VOIs, typically a small cube, whose size depends on the modality, on the image resolution and on the purpose of the content-based query, and 2) performing feature extraction operations on the VOIs. The Feature Extraction Engine performs totally unsupervised, automatic and asynchronous extractions of features from the images, organizes and indexes them in a no-SQL database based on unique similarity metric. The results of this phase are a series of clusters of phenotype signatures.

patient with hepatocellular carcinoma

Phenotype signature database building DEVICE

Since the clusters are self-organizing their pathophysiological meaning is not readily apparent and requires further analysis. The characterization of each cluster is performed by analyzing representative samples and their respective correlation with histopathology results. After a series of iterations, the clusters are organized to correlate with distinct tissue subtypes identified by their signature similarity. The final number of clusters is not known a priori and depends on the heterogeneity of the underlying imaging phenotypes.

patient with hepatocellular carcinoma

Eligibility Criteria

• Age: 18 Years - 100 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Participants who were at risk of developing a liver disease that justified performing CT scan or MRI with contrast media and that had a liver biopsy, a tumor resection or a transplantation following imaging will be included in the protocol

You may qualify if:

• Patients with visual liver disease who:
• Have a lesion visualized on CT scans / MRI with histological confirmation (surgical resection, biopsy, transplant).
• With a CT scan / MRI performed within 6 months prior to biopsy, surgical or transplant intervention.

You may not qualify if:

• Patients that had CT scans / MRI taken more than 6 months prior to surgical intervention

Sponsors & Collaborators

• Assistance Publique - Hôpitaux de Paris: lead

Study Sites (1)

Assistance Publique - Hôpitaux de Paris (AP-HP) Groupe Hospitalier La Pitié-Salpêtrière

Paris, Île-de-France Region, 75013, France

Location

MeSH Terms

Conditions

Carcinoma, Hepatocellular; Liver Neoplasms; Disease

Interventions

Cluster Analysis

Condition Hierarchy (Ancestors)

Adenocarcinoma; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Digestive System Diseases; Liver Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Intervention Hierarchy (Ancestors)

Statistics as Topic; Epidemiologic Methods; Investigative Techniques; Health Care Evaluation Mechanisms; Quality of Health Care; Health Care Quality, Access, and Evaluation; Public Health; Environment and Public Health

Study Officials

• Olivier Lucidarme, MD

  Assitance Publique - Hôpitaux de Paris

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: December 18, 2020
• First Posted: December 23, 2020
• Study Start: August 31, 2020
• Primary Completion: October 1, 2022
• Study Completion: December 31, 2022
• Last Updated: May 16, 2023

Record last verified: 2023-05

Data Sharing

• IPD Sharing: Will not share

Locations

FR (1)