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

NCT04802954 | Completed

• 1 other identifier: 20-008
• Study Type: interventional
• Target: 400
• Locations: 1 country
• Sites: 6

Brief 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.

Conditions

Hepatocellular Carcinoma; Chronic Liver Disease

Keywords

Hepatocellular Carcinoma; Cirrhosis; Deep Learning; Convolutional Neural Network; Ultrasound

Outcome Measures

Primary Outcomes (1)

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

  Deep Learning-based cross-analysis of clinical, biological, elastographic and ultrasonic (non-tumor liver parenchyma) parameters

  12 months

Secondary Outcomes (4)

• Development of a new screening strategy by a deep learning-based cross-analysis

  12 months

• Development of an algorithm to identify patients at risk of multifocal and diffuse forms by a deep learning-based cross-analysis

  12 months

• Characterization of the nodules detected on ultrasound by a deep learning-based cross-analysis

  12 months

• Characterization of the interface of the nodules with the adjacent hepatic parenchyma by a deep learning-based cross-analysis

  12 months

Study Arms (2)

High risk group

EXPERIMENTAL

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

Low risk group

EXPERIMENTAL

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

Interventions

Video acquisition OTHER

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.).

High risk group; Low risk group

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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.

You may not qualify if:

• 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

Sponsors & Collaborators

• IHU Strasbourg: lead

Study Sites (6)

CHU Angers

Angers, 49100, France

Location

Hôpital Avicenne

Bobigny, 93000, France

Location

Hôpital Beaujon

Clichy, 92110, France

Location

Hospices Civils de Lyon, Hôpital Edouard Herriot

Lyon, 69003, France

Location

Groupement Hospitalier Nord, Hôpital de la Croix-Rousse

Lyon, 69317, France

Location

CHU Montpellier

Montpellier, 34090, France

Location

Related Publications (14)

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• Ioannou GN, Green P, Kerr KF, Berry K. Models estimating risk of hepatocellular carcinoma in patients with alcohol or NAFLD-related cirrhosis for risk stratification. J Hepatol. 2019 Sep;71(3):523-533. doi: 10.1016/j.jhep.2019.05.008. Epub 2019 May 28.

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  PMID: 30739718 BACKGROUND

• Dana J, Meyer A, Paisant A, Rode A, Sartoris R, Seror O, Cassinotto C, Milot L, Gregory J, Coeur J, Lebigot J, Schembri V, Villeret F, Takeda AN, Ronot M, Vilgrain V, Baumert TF, Gallix B, Padoy N, Nahon P. Improving risk stratification and detection of early HCC using ultrasound-based deep learning models. JHEP Rep. 2025 Jul 5;7(10):101510. doi: 10.1016/j.jhepr.2025.101510. eCollection 2025 Oct.

  PMID: 40980161 DERIVED

MeSH Terms

Conditions

Carcinoma, Hepatocellular; Fibrosis

Condition Hierarchy (Ancestors)

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

Study Officials

• Jérémy DANA, MD

  IHU Strasbourg

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 10, 2021
• First Posted: March 17, 2021
• Study Start: September 1, 2021
• Primary Completion: February 14, 2024
• Study Completion: February 14, 2024
• Last Updated: December 18, 2024

Record last verified: 2023-12

Data Sharing

• IPD Sharing: Will not share

Locations

FR (6)