Risk Stratification of Hepatocarcinogenesis Using a Deep Learning Based Clinical, Biological and Ultrasound Model in High-risk Patients
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 400
- 试验地点
- 12
- 主要终点
- Stratification of the risk of hepatocarcinogenesis in high-risk patients by a deep learning-based cross-analysis.
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Non Randomized
- 干预模型
- Parallel
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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.
排除标准
- •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
结局指标
主要结局
Stratification of the risk of hepatocarcinogenesis in high-risk patients by a deep learning-based cross-analysis.
时间窗: 12 months
Deep Learning-based cross-analysis of clinical, biological, elastographic and ultrasonic (non-tumor liver parenchyma) parameters
次要结局
- 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)