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

In this editorial, we comment on the article by Zhang 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.

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.