Zhang Jinyu, Wu Qionglan, Zeng Jinhua, Zeng Yongyi, Liu Jingfeng, Zeng Jianxing
Department of Hepatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian, China.
Hepatobiliary Medical Center of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian, China.
Biosci Trends. 2025 Jan 14;18(6):567-583. doi: 10.5582/bst.2024.01228. Epub 2024 Dec 5.
The prognosis for patients with hepatocellular carcinoma (HCC) depends on tumor stage and remnant liver function. However, it often includes tumor morphology, which is usually assessed with imaging studies or pathologic analysis, leading to limited predictive performance. Therefore, the aim of this study was to develop a simple and low-cost prognostic score for HCC based on serum biomarkers in routine clinical practice. A total of 3,100 patients were recruited. The least absolute shrinkage and selector operation (LASSO) algorithm was used to select the significant factors for overall survival. The prognostic score was devised based on multivariate Cox regression of the training cohort. Model performance was assessed by discrimination and calibration. Albumin (ALB), alkaline phosphatase (ALP), and alpha-fetoprotein (AFP) were selected by the LASSO algorithm. The three variables were incorporated into multivariate Cox regression to create the risk score (APP score = 0.390* ln (ALP) + 0.063* ln(AFP) - 0.033*ALB). The C-index, K-index, and time-dependent AUC of the score displayed significantly better predictive performance than 5 other models and 5 other staging systems. The model was able to stratify patients into three different risk groups. In conclusion, the APP score was developed to estimate survival probability and was used to stratify three strata with significantly different outcomes, outperforming other models in training and validation cohorts as well as different subgroups. This simple and low-cost model could help guide individualized follow-up.
肝细胞癌（HCC）患者的预后取决于肿瘤分期和残余肝功能。然而，其通常还包括肿瘤形态，这一般通过影像学检查或病理分析来评估，导致预测性能有限。因此，本研究的目的是基于常规临床实践中的血清生物标志物，开发一种简单且低成本的HCC预后评分系统。共招募了3100例患者。采用最小绝对收缩和选择算子（LASSO）算法来选择影响总生存期的显著因素。基于训练队列的多变量Cox回归设计预后评分。通过区分度和校准来评估模型性能。LASSO算法选择了白蛋白（ALB）、碱性磷酸酶（ALP）和甲胎蛋白（AFP）。将这三个变量纳入多变量Cox回归以创建风险评分（APP评分 = 0.390 * ln(ALP) + 0.063 * ln(AFP) - 0.033 * ALB）。该评分的C指数、K指数和时间依赖性AUC显示出比其他5种模型和5种分期系统显著更好的预测性能。该模型能够将患者分为三个不同的风险组。总之，开发APP评分是为了估计生存概率，并用于将患者分为三个预后显著不同的层次，在训练和验证队列以及不同亚组中均优于其他模型。这种简单且低成本的模型有助于指导个体化随访。
