Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Background and Objectives: To investigate the association between metabolic syndrome (MetS) and hearing impairment (HI) in elderly Korean adults using a large-scale cohort data.Subjects and Method Data was collected on 729664 adults who underwent at least one health screening between 2009 and 2012 from the Korea National Health Insurance Service-Senior Cohort. The study utilized a Cox proportional hazards regression model to assess the relationship between MetS and the risk of developing HI. The results were presented as an adjusted hazard ratio (aHR) with 95% confidence intervals (CI). All subjects were monitored until the occurrence of HI, death, or December 31, 2019. Results: A total of 10102 cases of HI were identified during the follow-up period of 4920397 person-years. In the crude model, MetS was linked to an increased risk of developing HI (HR, 1.14; 95% CI, 1.10-1.19). No significant association was found after adjusting for potential confounding factors (aHR, 1.03; 95% CI, 0.99-1.08). Abnormal triglyceride (TG) levels were associated with a higher risk of HI compared to normal TG levels (aHR, 1.07; 95% CI, 1.03-1.12). In addition, the risk of HI increased as TG levels increased (>150 mg/dL). Conclusion Abnormalities in TG levels were independently associated with an increased risk of HI among elderly adults. Management of TG levels may protect against the risk of incident HI.