Background/Objectives: Hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths rising worldwide. This is leading to an increased demand for liver transplantation (LT), the most effective treatment for HCC in its initial stages. However, current patient selection criteria are limited in predicting recurrence and raise ethical concerns about equitable access to care. This study aims to enhance patient selection by refining the HepatoPredict (HP) tool, a machine learning-based model that combines molecular and clinical data to forecast LT outcomes. Methods: The updated HP algorithm was trained on a two-center dataset and assessed against standard clinical criteria. Its prognostic performance was evaluated through accuracy metrics, with additional analyses considering tumor heterogeneity and potential sampling bias. Results: HP outperformed all clinical criteria, particularly regarding negative predictive value, addressing critical limitations in existing selection strategies. It also demonstrated improved differentiation of recurrence-free and overall survival outcomes. Importantly, the prognostic accuracy of HP remained largely unaffected by intra-nodule and intra-patient heterogeneity, indicating its robustness even when biopsies were taken from smaller or non-dominant nodules. Conclusions: These findings support the usage of HP as a valuable tool for optimizing LT candidate selection, promoting fair organ allocation and enhancing patient outcomes through integrated analysis of molecular and clinical data.
背景/目的：肝细胞癌是全球范围内癌症相关死亡的主要原因，其发病率持续上升。这导致对肝移植的需求日益增加，而肝移植是早期肝细胞癌最有效的治疗方法。然而，当前的患者选择标准在预测复发方面存在局限，并引发了关于公平获得治疗机会的伦理关切。本研究旨在通过改进HepatoPredict工具来优化患者选择，该工具是一种基于机器学习的模型，结合分子与临床数据预测肝移植结局。方法：更新后的HP算法基于双中心数据集进行训练，并与标准临床标准进行比较评估。通过准确性指标评估其预后性能，并额外分析了肿瘤异质性和潜在抽样偏倚的影响。结果：HP在所有临床标准中表现更优，特别是在阴性预测值方面，有效解决了现有选择策略的关键局限。该工具还显示出对无复发生存期和总生存期结局的区分能力有所提升。重要的是，HP的预后准确性基本不受结节内和患者内异质性的影响，表明即使在从较小或非优势结节取样时仍保持稳健性。结论：这些发现支持将HP作为优化肝移植候选者选择的重要工具，通过整合分子与临床数据分析，促进器官公平分配并改善患者预后。
肿瘤(癌症)患者之家