Predicting waitlist dropout in hepatocellular carcinoma: a narrative review.

ABSTRACT:

Background and objective

Liver transplantation is the gold standard treatment for patients with hepatocellular carcinoma (HCC). Current allocation systems face a complex issue due to the imbalance between available organs and recipients. The prioritization of HCC patients remains controversial, leading to potential disparities in access to transplantation. Factors such as tumor size, alpha-fetoprotein (AFP) levels, Model of End-Stage Liver Disease (MELD) score, and response to locoregional therapy (LRT) contribute to determining waitlist dropout risk in HCC patients. Several statistical and machine learning (ML) models have been proposed to predict waitlist dropout, incorporating variables related to tumor and patient factors, underlying liver disease, and waitlist time. This narra

SUBMITTER: Calleja R 

PROVIDER: S-EPMC11535785 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature