Chronic Hepatitis C Virus Infection, Extrahepatic Disease and the Impact of New Direct-Acting Antivirals.

Chronic hepatitis C virus infection is an important cause of liver cirrhosis, hepatocellular carcinoma and death. Furthermore, it is estimated that about 40-70% of patients develop non-hepatic alterations in the course of chronic infection. Such manifestations can be immune-related conditions, lymphoproliferative disorders and metabolic alterations with serious adverse events in the short and long term. The introduction of new Direct-Acting Antivirals has shown promising results, with current evidence indicating an improvement and remission of these conditions after a sustained virological response.

From prediction to prevention: Machine learning revolutionizes hepatocellular carcinoma recurrence monitoring.

In this editorial, we comment on the article by Zhang et al entitled Development of a machine learning-based model for predicting the risk of early postoperative recurrence of hepatocellular carcinoma. Hepatocellular carcinoma (HCC), which is characterized by high incidence and mortality rates, remains a major global health challenge primarily due to the critical issue of postoperative recurrence. Early recurrence, defined as recurrence that occurs within 2 years posttreatment, is linked to the hidden spread of the primary tumor and significantly impacts patient survival. Traditional predictive factors, including both patient- and treatment-related factors, have limited predictive ability with respect to HCC recurrence. The integration of machine learning algorithms is fueled by the exponential growth of computational power and has revolutionized HCC research. The study by Zhang et al demonstrated the use of a groundbreaking preoperative prediction model for early postoperative HCC recurrence. Chall-enges persist, including sample size constraints, issues with handling data, and the need for further validation and interpretability. This study emphasizes the need for collaborative efforts, multicenter studies and comparative analyses to validate and refine the model. Overcoming these challenges and exploring innovative approaches, such as multi-omics integration, will enhance personalized oncology care. This study marks a significant stride toward precise, effi-cient, and personalized oncology practices, thus offering hope for improved patient outcomes in the field of HCC treatment.

Breaking new ground: MASLD vs. MAFLD-which holds the key for risk stratification?

BACKGROUND: The classification and nomenclature of non-alcoholic fatty liver disease (NAFLD) has been the subject of ongoing debate in the medical community. Through the introduction of metabolic dysfunction-associated fatty liver disease (MAFLD) and the later release of metabolic dysfunction-associated steatotic liver disease (MASLD), the limitations associated with NAFLD are intended to be addressed. Both terminologies incorporate the metabolic component of the disease by providing diagnostic criteria that relies on the presence of underlying metabolic risk factors. MATERIALS AND METHODS: An epidemiologic cross-sectional study of individuals who had undergone abdominal ultrasound and vibration-controlled transient elastography (VCTE) as part of a routine check was performed. We evaluated clinical, anthropometric, and biochemical variables to determine the metabolic profile of each subject. RESULTS: The study included a total of 500 participants, 56.8% (n = 284) males and 43.2% (n = 216) females, with a mean age of 49 ± 10 years. 59.4% (n = 297) were diagnosed with MAFLD and MASLD, 10.2% (n = 51) were diagnosed only with MASLD and 30.4% (n = 152) were not diagnosed with either MAFLD or MASLD. The differences in prevalence were mainly based on the detection of individuals with a BMI < 25 kg/m2, where MASLD captures the largest number (p < 0.001). CONCLUSIONS: Although MASLD has a higher capture of lean patients compared to MAFLD, patients with MAFLD and MASLD have a worse metabolic profile than those with only MASLD. Our results provide evidence that MAFLD better identifies patients likely to have a higher risk of liver fibrosis and of disease progression.