Fu Hongyuan, Wang Yubo, Xiang Bangde
Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi Province, People's Republic of China.
The Second Clinical Medical College of Guangxi Medical University, Nanning, Guangxi Province, People's Republic of China.
J Inflamm Res. 2025 May 20;18:6411-6425. doi: 10.2147/JIR.S521603. eCollection 2025.
Hepatocellular carcinoma (HCC) poses a substantial threat to global health, characterized by its high incidence and mortality rates. This research aims to assess the prognostic value of a systematic serum inflammation index, the pan-immune-inflammation value (PIV), in patients with HCC who have undergone hepatectomy.
A total of 1764 HCC patients who underwent surgery were included in the study. These patients were divided into two groups based on the median PIV value. The Cox regression model was utilized to ascertain the independent risk factors that influence the prognosis of patients. A PIV-based nomogram was constructed and its performance was evaluated by the C-index, calibration curve, ROC curve, and DCA curve. Finally, a comparison was made between the nomogram and existing staging models.
Patients with elevated PIV exhibited diminished OS and RFS compared to those with lower PIV. Univariate and multivariate Cox analyses revealed that PIV is an independent predictor of prognosis. The PIV-based nomogram demonstrated excellent discrimination, calibration, and clinical net benefit. The proposed nomogram outperformed the other existing staging systems, as evidenced by a higher AUC value.
PIV exhibits potential as a prognostic factor for both OS and RFS in patients with HCC who have undergone hepatectomy. The PIV-based nomogram can serve as an additional tool in conjunction with the existing liver cancer staging system, thereby facilitating more personalized treatment decisions for clinicians.
肝细胞癌(HCC)对全球健康构成重大威胁,其特点是发病率和死亡率高。本研究旨在评估一种系统性血清炎症指标——全免疫炎症值(PIV)在接受肝切除术的HCC患者中的预后价值。
本研究共纳入1764例接受手术的HCC患者。根据PIV值中位数将这些患者分为两组。采用Cox回归模型确定影响患者预后的独立危险因素。构建基于PIV的列线图,并通过C指数、校准曲线、ROC曲线和DCA曲线评估其性能。最后,将列线图与现有的分期模型进行比较。
与PIV值较低的患者相比,PIV值升高的患者总生存期(OS)和无复发生存期(RFS)缩短。单因素和多因素Cox分析显示,PIV是预后的独立预测因素。基于PIV的列线图显示出良好的区分度、校准度和临床净效益。较高的AUC值表明,所提出的列线图优于其他现有的分期系统。
PIV在接受肝切除术的HCC患者中对OS和RFS均具有作为预后因素的潜力。基于PIV的列线图可作为现有肝癌分期系统的辅助工具,从而为临床医生提供更个性化的治疗决策。