Calleja Rafael, Aguilera Eva, Durán Manuel, Pérez de Villar José Manuel, Padial Ana, Luque-Molina Antonio, Ayllón María Dolores, López-Cillero Pedro, Ciria Rubén, Briceño Javier
Hepatobiliary and Liver Transplantation Surgery Department, Reina Sofía University Hospital, Córdoba, Spain.
Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Córdoba, Córdoba, Spain.
Transl Gastroenterol Hepatol. 2024 Aug 21;9:72. doi: 10.21037/tgh-24-24. eCollection 2024.
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.
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.
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.
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.
肝移植是肝细胞癌（HCC）患者的金标准治疗方法。由于可用器官与受者之间的不平衡，当前的分配系统面临一个复杂的问题。HCC患者的优先排序仍存在争议，导致在获得移植机会方面可能存在差异。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患者在等待名单上退出的风险。已经提出了几种统计和机器学习（ML）模型来预测等待名单退出情况，纳入了与肿瘤和患者因素、潜在肝病以及等待名单时间相关的变量。本叙述性综述旨在总结关于HCC等待名单退出不同预测模型的证据。
考虑截至2023年12月25日发表的所有文章。排除未基于使用传统统计方法或ML模型的预测模型的文章。
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，影响等待名单退出。文献中的大多数文章基于统计模型。ML和统计模型都可能提供有前景的结果，但它们目前的应用有限。已经进行了几次尝试以找到对HCC患者等待名单退出风险进行分层的最佳模型。然而，迄今为止，所探索的模型均未得到实施。HCC受者的分配仍基于补充评分系统或地理标准。
在未来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。
