肝细胞癌（HCC）对全球生命构成威胁；然而，预测这些患者临床预后的数值工具仍然很少。本研究的主要目的是建立一个临床评分系统，用于评估 HCC 患者的总生存率 (OS) 和癌症特异性生存率 (CSS)。从监测、流行病学和最终结果 (SEER) 计划中，我们确定了 45,827 名原发性 HCC 患者。这些病例被随机分配到训练队列（22,914 名患者）和验证队列（22,913 名患者）。采用单变量和多变量 Cox 回归分析，结合 Kaplan-Meier 方法来评估与预后相关的临床和人口特征。使用证明预后意义的因素来构建模型。通过C指数、受试者工作特征（ROC）曲线、校准曲线和临床决策曲线分析（DCA）评估模型的稳定性和准确性，并与美国癌症联合委员会（AJCC）分期进行比较。最终，机器学习 (ML) 对模型中的变量进行量化，以建立临床评分系统。单变量和多变量 Cox 回归分析确定了 11 项人口统计学和临床​​病理特征作为 CSS 和 OS 使用的独立预后指标。开发了两个模型，每个模型都包含 11 个特征，这两个模型都显示出显着的预后相关性。用于预测 CSS 和 OS 的 C 指数超过了 AJCC 分期系统。在训练集和验证集中，时间依赖性 ROC 的曲线下面积 (AUC) 始终超过 0.74。此外，内部和外部校准图表明模型预测与观察到的结果密切相关。此外，DCA 证明了该模型相对于 AJCC 分期系统的优越性，产生了更大的临床净效益。最终，量化的临床评分系统可以有效区分高风险和低风险患者。在大规模数据集上训练的 ML 临床评分系统在队列中表现出良好的预测和风险分层性能。这样的临床评分系统很容易融入临床实践，对于提高 HCC 管理的准确性和效率具有重要价值。2024 年胃肠肿瘤学杂志。版权所有。

Hepatocellular carcinoma (HCC) poses a global threat to life; however, numerical tools to predict the clinical prognosis of these patients remain scarce. The primary objective of this study is to establish a clinical scoring system for evaluating the overall survival (OS) rate and cancer-specific survival (CSS) rate in HCC patients.From the Surveillance, Epidemiology, and End Results (SEER) Program, we identified 45,827 primary HCC patients. These cases were randomly allocated to a training cohort (22,914 patients) and a validation cohort (22,913 patients). Univariate and multivariate Cox regression analyses, coupled with Kaplan-Meier methods, were employed to evaluate prognosis-related clinical and demographic features. Factors demonstrating prognostic significance were used to construct the model. The model's stability and accuracy were assessed through C-index, receiver operating characteristic (ROC) curves, calibration curves, and clinical decision curve analysis (DCA), while comparisons were made with the American Joint Committee on Cancer (AJCC) staging. Ultimately, machine learning (ML) quantified the variables in the model to establish a clinical scoring system.Univariate and multivariate Cox regression analyses identified 11 demographic and clinical-pathological features as independent prognostic indicators for both CSS and OS using. Two models, each incorporating the 11 features, were developed, both of which demonstrated significant prognostic relevance. The C-index for predicting CSS and OS surpassed that of the AJCC staging system. The area under the curve (AUC) in time-dependent ROC consistently exceeded 0.74 in both the training and validation sets. Furthermore, internal and external calibration plots indicated that the model predictions aligned closely with observed outcomes. Additionally, DCA demonstrated the superiority of the model over the AJCC staging system, yielding greater clinical net benefit. Ultimately, the quantified clinical scoring system could efficiently discriminate between high and low-risk patients.A ML clinical scoring system trained on a large-scale dataset exhibits good predictive and risk stratification performance in the cohorts. Such a clinical scoring system is readily integrable into clinical practice and will be valuable in enhancing the accuracy and efficiency of HCC management.2024 Journal of Gastrointestinal Oncology. All rights reserved.