Risk Stratification of Hepatocarcinogenesis Using a Deep Learning Based Clinical, Biological and Ultrasound Model in High-risk Patients
Trial ID: NCT04802954
Status: COMPLETED
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-09-01
Completion date: 2024-02-14
Sponsor: IHU Strasbourg
Conditions: Hepatocellular Carcinoma; Chronic Liver Disease
Interventions: Video acquisition
Locations: CHU Angers; Hôpital Avicenne; Hôpital Beaujon; Hospices Civils de Lyon, Hôpital Edouard Herriot; Groupement Hospitalier Nord, Hôpital de la Croix-Rousse; CHU Montpellier
Countries: France; France; France; France; France; France
Enrollment: 400
Age range: 18 Years - None
Sex: ALL

Summary:
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.

Eligibility criteria:
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

Outcomes:
Stratification of the risk of hepatocarcinogenesis in high-risk patients by a deep learning-based cross-analysis.