Impact of HCV Treatment on Recurrence of Hepatocarcinoma After Liver Transplantation.

The treatment of hepatitis C virus (HCV) has been a revolution in hepatology. Since the beginning of transplantation, liver cirrhosis and hepatocarcinoma on HCV cirrhosis has been the main etiology of liver transplantation. We set out to analyze the impact that C virus treatment has had on liver transplantation. To do so, we divided our cohort into 2 periods, one before virus treatment (from 2000-2014) and one after the onset of treatment (2014-2020). Taking into account this differentiation, we analyzed the percentage of patients transplanted for hepatocarcinoma over cirrhotic liver by HCV in both groups. Among the patients transplanted for HCV, we analyzed whether there were differences in hepatocarcinoma recurrences according to their serologic status at the time of transplantation.

Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review.

Although artificial intelligence (AI) was initially developed many years ago, it has experienced spectacular advances over the last 10 years for application in the field of medicine, and is now used for diagnostic, therapeutic and prognostic purposes in almost all fields. Its application in the area of hepatology is especially relevant for the study of hepatocellular carcinoma (HCC), as this is a very common tumor, with particular radiological characteristics that allow its diagnosis without the need for a histological study. However, the interpretation and analysis of the resulting images is not always easy, in addition to which the images vary during the course of the disease, and prognosis and treatment response can be conditioned by multiple factors. The vast amount of data available lend themselves to study and analysis by AI in its various branches, such as deep-learning (DL) and machine learning (ML), which play a fundamental role in decision-making as well as overcoming the constraints involved in human evaluation. ML is a form of AI based on automated learning from a set of previously provided data and training in algorithms to organize and recognize patterns. DL is a more extensive form of learning that attempts to simulate the working of the human brain, using a lot more data and more complex algorithms. This review specifies the type of AI used by the various authors. However, well-designed prospective studies are needed in order to avoid as far as possible any bias that may later affect the interpretability of the images and thereby limit the acceptance and application of these models in clinical practice. In addition, professionals now need to understand the true usefulness of these techniques, as well as their associated strengths and limitations.