Predicting waitlist dropout in hepatocellular carcinoma: a narrative review.

IF 3.8 Q2 GASTROENTEROLOGY & HEPATOLOGY Translational gastroenterology and hepatology Pub Date : 2024-08-21 eCollection Date: 2024-01-01 DOI:10.21037/tgh-24-24
Rafael Calleja, Eva Aguilera, Manuel Durán, José Manuel Pérez de Villar, Ana Padial, Antonio Luque-Molina, María Dolores Ayllón, Pedro López-Cillero, Rubén Ciria, Javier Briceño
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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 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.

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肝细胞癌候补患者退出候补名单的预测:叙述性综述。
背景和目的:肝移植是治疗肝细胞癌(HCC)患者的金标准。由于可用器官和受体之间的不平衡,目前的分配系统面临着一个复杂的问题。如何确定 HCC 患者的优先顺序仍存在争议,这可能会导致移植机会的不均等。肿瘤大小、甲胎蛋白(AFP)水平、终末期肝病模型(MELD)评分以及对局部区域治疗(LRT)的反应等因素有助于确定 HCC 患者的候选名单退出风险。目前已提出了几种统计和机器学习(ML)模型来预测候选患者退出候选名单的风险,其中包含了与肿瘤和患者因素、潜在肝病和候选时间相关的变量。本叙事性综述旨在总结有关HCC候诊退出的不同预测模型的证据:方法:对截至2023年12月25日发表的所有文章进行了研究。主要内容和研究结果:肿瘤大小、AFP水平、MELD评分和LRT反应等因素已被证明会影响这些患者的疾病进展,并影响候选名单的退出。文献中的大多数文章都基于统计模型。ML 模型和统计模型都可能提供有前景的结果,但目前其应用还很有限。为了找到对 HCC 患者退出候选名单的风险进行分层的最佳模型,人们进行了多次尝试。然而,迄今为止,所探索的模型还没有一个得到实施。HCC患者的分配仍以辅助评分系统或地理标准为基础:结论:在未来的研究中,改进方法和数据库对于为临床医生获得准确可靠的模型至关重要。这是实现真正适用性的唯一途径。
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