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Browse 690 clinical trials for liver disease. Find studies that match your criteria and connect with research centers.
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NCT06741904
Preliminary studies have suggested that non-invasive methods can not only be applied to CSPH but also to predict the risk of decompensation in cirrhosis. However, there is a lack of clinical evidence, and more research is needed to provide such evidence. Especially in China, where the etiology of cirrhosis is prevalent, there is a large population of patients with hepatitis B cirrhosis undergoing antiviral treatment. Exploring the value of non-invasive methods in predicting decompensation events in these patients can not only expand the clinical application of non-invasive methods but also provide effective non-invasive screening and management strategies for patients with cirrhosis at different risk levels. Primary Objective: The main purpose of this study is to evaluate the predictive effectiveness of non-invasive methods (based on liver and spleen stiffness) for the occurrence of decompensation in chronic liver disease (CLD). Secondary Objectives: To establish different predictive models for the occurrence of decompensation in CLD and to assess their predictive effectiveness as non-invasive methods for decompensation in CLD. Approximately 2334 individuals will participate in this study at 17 different health care Setting.The study will last for 4 years, including 1 year of enrollment and 3 years of follow-up. Patients will be seen at 6-month intervals, and all examination results of patients as well as decompensation events, liver cancer, and death will be recorded.
NCT04802954
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.