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420 active trials for Hepatocellular Carcinoma

Perception of the Doctor/Patient Relationship, Disease and Treatment Among Physicians and Their Patients Treated With Systemic Therapy for Hepatocellular Carcinoma "PERCEPTION1"

atients with cancer face difficult choices that require balancing competing priorities such as survival, functional capacity and symptom relief. Most patients with advanced cancer (>80%) expect their sensitive discussions with physicians about prognosis and treatment choices, in order to be involved in the decision making process. Nevertheless, this kind of discussion is frequently lacking. Consequently, patients often have a biased view of their own prognosis such as an underestimation of disease severity, or unrealistic expectations for cure. Patients with advanced hepatocellular carcinoma (HCC) may be treated with systemic therapies which may prolong survival, but are not curative. Patients with advanced HCC often report expectations for survival and treatment-related side-effects that differ from their treating physician. Accordingly, communication on prognostic and treatment choices is essential to obtain an accurate understanding of the disease that allows patients to make informed decisions. To the best of our knowledge, a thorough evaluation of the physician-patient communication quality has never been performed in advanced HCC patients. The aim of our study, is to assess the perception of the expected prognosis, the treatment side-effects; by the patient and by his investigator during the first consultation before the initiation

Start: June 2021
Risk Stratification of Hepatocarcinogenesis Using a Deep Learning Based Clinical, Biological and Ultrasound Model in High-risk Patients

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.

Start: May 2021