Predicting waitlist dropout in hepatocellular carcinoma: a narrative review.

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 narrative review aims to summarize the evidence regarding different prediction models of HCC waitlist dropout.

METHODS

All published articles up to December 25, 2023, were considered. Articles not based on prediction models using conventional statistical methods or ML models were excluded.

KEY CONTENT AND FINDINGS

Factors such as tumor size, AFP levels, MELD score, and LRT response have been shown to impact disease progression in these patients, influencing waitlist dropout. Most articles in the literature are based on statistical models. Both ML and statistical models may offer promising results, but their application is currently limited. Several attempts have been made to find the best model to stratify the risk of waitlist dropout in HCC patients. However, to date, none of the explored models have been implemented. The allocation of HCC recipients is still based on supplementary scoring systems or geographical criteria.

CONCLUSIONS

Improving methodology and databases in future research is essential to obtain accurate and reliable models for clinicians. This is the only way to achieve real applicability.