Risk Stratification of Hepatocarcinogenesis Using a Deep Learning Based Clinical, Biological and Ultrasound Model in High-risk Patients (STARHE)

December 12, 2023 updated by: IHU Strasbourg

By 2030, hepatocellular carcinoma (HCC) will become the second leading cause of cancer-related death, accounting for more than one million deaths per year according to the World Health Organization.

To this date, screening for hepatocellular carcinoma in France remains uniform for all patients, based solely on a liver ultrasound every 6 months. This strategy has three main limitations: lack of personalisation, low compliance, relatively poor performance of the ultrasound.

Risk stratification models have been developed for chronic hepatitis C, alcoholic cirrhosis and non-alcoholic steatohepatitis (NASH) including clinical and biological parameters but no analysis of the liver parenchyma which is the physiopathological substrate of hepatocarcinogenesis.

The advent of new artificial intelligence techniques could revolutionize the approach and lead to a personalised radiological screening strategy.

Deep learning, a subclass of machine learning, is a popular area of research that can help humans performing certain tasks by automatically identifying new image features not defined by humans.

The hypothesis of this study is that the non-tumor cirrhotic liver parenchyma is rich in structural information reflecting the severity of the hepatopathy, its carcinological risk and the process of hepatocarcinogenesis. Its analysis combined with clinical and biological data, which have already been studied to stratify the risk of hepatocarcinogenesis, will allow to define a very high-risk population, particularly in the context of Hepatitis C Virus (HCV) eradication and Hepatitis B Virus (HBV) control.

Consequently, this study proposes to design prospectively a deep learning model for stratification of the risk of hepatocarcinogenesis by including clinical, biological and radiological ultrasound parameters.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

By 2030, hepatocellular carcinoma (HCC) will become the second leading cause of cancer-related death, accounting for more than one million deaths per year according to the World Health Organization.

To this date, screening for hepatocellular carcinoma in France remains uniform for all patients, based solely on a liver ultrasound every 6 months. This scheme has the advantage of associating an acceptable cost-effectiveness ratio and, above all, of obtaining an increased overall survival. However, this strategy has three main limitations: lack of personalisation, low compliance, relatively poor performance of the ultrasound.

Risk stratification models have been developed for chronic hepatitis C, alcoholic cirrhosis and non-alcoholic steatohepatitis (NASH) including clinical (age, sex, body mass index and diabetes) and biological (ASAT/ALAT, platelets, albumin) parameters. However, they didn't include analysis of the liver parenchyma which is the physiopathological substrate of hepatocarcinogenesis. In the 1990s, several authors studied the incidence of hepatocellular carcinoma according to the liver echostructure. They agreed on the over-risk represented by a nodular heterogeneous echostructure with an estimated rate ratio of up to 20.

However, all these results have not yet led to a personalised radiological screening strategy. The advent of new artificial intelligence techniques could revolutionize the approach.

Deep learning, a subclass of machine learning, is a popular area of research that can help humans performing certain tasks. Unlike radiomics, deep learning can automatically identify new image features not defined by humans.

The hypothesis of this study is that the non-tumor cirrhotic liver parenchyma is rich in structural information reflecting the severity of the hepatopathy, its carcinological risk and the process of hepatocarcinogenesis. Its analysis combined with clinical and biological data, which have already been studied to stratify the risk of hepatocarcinogenesis, will allow to define a very high-risk population, particularly in the context of Hepatitis C Virus (HCV) eradication and Hepatitis B Virus (HBV) control.

Consequently, this study proposes to design prospectively a deep learning model for stratification of the risk of hepatocarcinogenesis by including clinical, biological and radiological ultrasound parameters. The primary objective of the study is to identify a population at very high risk of developing hepatocarcinoma in order to propose different screening modalities to the patients most at risk.

This clinical study will include patients aged over 18 years referred by their hepatologist in the framework of ultrasound screening according to the European Association for the Study of the Liver (EASL) recommendations for hepatocellular carcinoma screening, except for non-cirrhotic HBV liver disease: non-cirrhotic F3-stage liver disease from any cause based on individual risk assessment for hepatocarcinoma; cirrhosis from any cause, non-viral or virologically cured (HCV) or controlled (HBV). Patients with a history of treated hepatocellular carcinoma will be excluded.

Two groups of patients will be constituted prospectively: group 1 will include patients with a diagnosis of hepatocellular carcinoma greater than 1 cm (reference diagnostic standards: radiological or histological). These patients will therefore correspond to a very high-risk; Group 2 will include patients without hepatocellular carcinoma, thus corresponding to a lower risk. A 1 year-interval ultrasound will be performed in patients of group 2 to confirm the absence of new nodule in the year following inclusion. The proportion of new hepatocellular carcinoma should not exceed 3%.

The data collected will be clinical, biological, elastographic and ultrasonic parameters.

A Deep Learning model using a deep convolutional neural network architecture will be developed on Python using these data.

On a total of 7 investigation sites, 300 patients (equitably distributed between the two groups) will be included in the training/validation cohort and 100 patients (equitably distributed between the two groups) in the test cohort. These numbers are calculated from ultrasound studies reporting a rate ratio of HCC risk of up to 20 in case of macronodular ultrasound pattern and Deep Learning requirements (large numbers needed).

The training/validation and test cohorts will be from external and independent centres.

The diagnostic performance of the model will be estimated by Area Under the Curve (AUC), sensitivity, specificity and F1-score (95% confidence intervals) on the test cohort.

Study Type

Interventional

Enrollment (Estimated)

400

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • France
      • Angers, France, 49100
        • Recruiting
        • Chu Angers
        • Contact:
          • Anita PAISANT
        • Sub-Investigator:
          • Clémence CANIVET
      • Bobigny, France, 93000
        • Recruiting
        • Hôpital Avicenne
        • Contact:
          • Olivier SEROR
        • Sub-Investigator:
          • Pierre NAHON
      • Clichy, France, 92110
        • Recruiting
        • Hopital Beaujon
        • Contact:
          • Riccardo SARTORIS
        • Sub-Investigator:
          • Pierre-Emmanuel RAUTOU
      • Lyon, France, 69003
        • Recruiting
        • Hospices Civils de Lyon, Hôpital Edouard Herriot
        • Contact:
          • Laurent MILOT
      • Lyon, France, 69317
        • Recruiting
        • Groupement Hospitalier Nord, Hôpital de la Croix-Rousse
        • Contact:
          • Agnès RODE
        • Sub-Investigator:
          • Philippe MERLE
      • Montpellier, France, 34090
        • Recruiting
        • CHU Montpellier
        • Contact:
          • Christophe CASSINOTTO
        • Sub-Investigator:
          • José URSIC-BEDOYA

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Description

Inclusion Criteria:

  • Men or women over 18 years of age.
  • Patients referred by their hepatologist within the framework of ultrasound screening according to the EASL hepato-cellular carcinoma screening recommendations.
  • Non-cirrhotic F3 hepatopathy of any cause according to an individual assessment of the risk of hepatocarcinoma.
  • Cirrhosis from any cause, non viral or virologically cured (HCV) or controlled (HBV).
  • Patient with hepatopathy proven by histological evidence or confirmed by an expert committee based on clinical, biological, ultrasound (hepato-cellular insufficiency, portal hypertension) and elastographic criteria.
  • Patient able to receive and understand the information relating to the study and to give his/her written informed consent.
  • Patient affiliated to the French social security system.

Exclusion Criteria:

  • History of hepatocarcinoma
  • Patient with non-cirrhotic viral B hepatopathy or uncontrolled (HBV) or uncured (HCV) viral cirrhosis.
  • Patient under protection of justice, guardianship or trusteeship.
  • Patient in a situation of social fragility.
  • Patient subject to legal protection or unable to express consent

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Diagnostic
  • Allocation: Non-Randomized
  • Interventional Model: Parallel Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: High risk group
Patients with hepatocellular carcinoma greater than 1 cm in size. All patients from an ultrasound screening programme who have been diagnosed with a nodule larger than 1 cm and referred to our centres will be included in this group. They will then be excluded of this group if the diagnosis of hepatocellular carcinoma is not retained according to the radiological or histological reference diagnostic standards (gold standard).
Other: Video acquisition
One to three video acquisitions of 10 seconds will be carried out via the intercostal route. Data acquisition will be standardized according to a mandatory protocol and previously recorded in each ultrasound machine (cross shots, harmonic, filter, depth, focal length, mechanical index, etc.).
Experimental: Low risk group
Patients without hepatocellular carcinoma. A 1-year interval ultrasound will be performed to confirm the absence of new nodule in the year following inclusion.
Other: Video acquisition
One to three video acquisitions of 10 seconds will be carried out via the intercostal route. Data acquisition will be standardized according to a mandatory protocol and previously recorded in each ultrasound machine (cross shots, harmonic, filter, depth, focal length, mechanical index, etc.).

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Stratification of the risk of hepatocarcinogenesis in high-risk patients by a deep learning-based cross-analysis.
Time Frame: 12 months
Deep Learning-based cross-analysis of clinical, biological, elastographic and ultrasonic (non-tumor liver parenchyma) parameters
12 months

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Development of a new screening strategy by a deep learning-based cross-analysis
Time Frame: 12 months
Deep Learning-based cross-analysis of clinical, biological, elastographic and ultrasonic (non-tumor liver parenchyma) parameters
12 months
Development of an algorithm to identify patients at risk of multifocal and diffuse forms by a deep learning-based cross-analysis
Time Frame: 12 months
Deep Learning-based cross-analysis of clinical, biological, elastographic and ultrasonic (non-tumor liver parenchyma) parameters
12 months
Characterization of the nodules detected on ultrasound by a deep learning-based cross-analysis
Time Frame: 12 months
Deep Learning-based cross-analysis of clinical, biological, elastographic and ultrasonic (non-tumor liver parenchyma) parameters
12 months
Characterization of the interface of the nodules with the adjacent hepatic parenchyma by a deep learning-based cross-analysis
Time Frame: 12 months
Deep Learning-based cross-analysis of clinical, biological, elastographic and ultrasonic (non-tumor liver parenchyma) parameters
12 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

IHU Strasbourg

Investigators

  • Principal Investigator: Jérémy DANA, MD, IHU Strasbourg

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

September 1, 2021

Primary Completion (Estimated)

September 1, 2024

Study Completion (Estimated)

September 1, 2025

Study Registration Dates

First Submitted

March 10, 2021

First Submitted That Met QC Criteria

March 16, 2021

First Posted (Actual)

March 17, 2021

Study Record Updates

Last Update Posted (Estimated)

December 13, 2023

Last Update Submitted That Met QC Criteria

December 12, 2023

Last Verified

December 1, 2023

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 20-008

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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