Yang Yifan, Zong Shaoqi, Hua Yongqiang
Department of Integrative Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
BMC Cancer. 2025 Jan 21;25(1):107. doi: 10.1186/s12885-025-13523-3.
Pancreatic cancer is one of the most malignant tumors with an inferior prognosis. This study aims to determine the prognostic significance of immune-inflammatory scores and coagulation indices in patients with metastatic pancreatic cancer(MPC) and develop a predictive nomogram.
This study retrospectively analyzed the clinical data of 384 patients with MPC who underwent intra-arterial infusion chemotherapy (IAIC). Patients were randomly divided into training and validation cohorts. Firstly, the optimal cutoff values for continuous variables were obtained in the training cohort. Then, survival analysis was performed to evaluate the impact of immune-inflammatory scores neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and coagulation indicators prothrombin time (PT), fibrinogen (FIB), and D-dimer on the overall survival (OS) of patients. Next, univariate analysis was utilized to identify prognostic factors, and a stepwise regression method was employed for variable selection to construct a nomogram based on the Cox proportional hazards model. Additionally, the predictive performance of the nomogram was assessed by the concordance index (C-index), the area under the ROC curve (AUC), and calibration curves. Finally, patients were stratified into risk groups based on the total score of the nomogram.
The Kaplan-Meier survival curves indicated that immune-inflammatory scores NLR, PLR, SII, and coagulation indicators PT, FIB, and D-dimer were associated with OS. Through Cox regression analysis, a nomogram was ultimately constructed incorporating NLR, PLR, PT, alkaline phosphatase (ALP), carbohydrate antigen 125 (CA125), age, and ablation. The model demonstrated good discriminative ability, with a C-index of 0.722, and the AUC values at 6- and 12-month OS predictions were 0.828 and 0.851 in the training cohort, while in the validation cohort, the corresponding AUC values were 0.754 and 0.791, respectively. The calibration curves showed a good fit, confirming the stability of the model. A cutoff value of 353.3 was identified as optimal for risk stratification, with a statistically significant difference in OS between the high- and low-risk groups.
The nomogram based on immune-inflammatory scores, coagulation indicators, and other clinicopathological factors can effectively predict the OS of patients with MPC.
胰腺癌是预后较差的最恶性肿瘤之一。本研究旨在确定免疫炎症评分和凝血指标在转移性胰腺癌（MPC）患者中的预后意义，并开发一种预测列线图。
本研究回顾性分析了384例行动脉内灌注化疗（IAIC）的MPC患者的临床资料。患者被随机分为训练队列和验证队列。首先，在训练队列中获得连续变量的最佳截断值。然后，进行生存分析，以评估免疫炎症评分中性粒细胞与淋巴细胞比值（NLR）、血小板与淋巴细胞比值（PLR）、全身免疫炎症指数（SII）以及凝血指标凝血酶原时间（PT）、纤维蛋白原（FIB）和D-二聚体对患者总生存期（OS）的影响。接下来，采用单因素分析确定预后因素，并采用逐步回归方法进行变量选择，以构建基于Cox比例风险模型的列线图。此外，通过一致性指数（C-index）、ROC曲线下面积（AUC）和校准曲线评估列线图的预测性能。最后，根据列线图的总分将患者分为风险组。
Kaplan-Meier生存曲线表明，免疫炎症评分NLR、PLR、SII以及凝血指标PT、FIB和D-二聚体与OS相关。通过Cox回归分析，最终构建了一个包含NLR、PLR、PT、碱性磷酸酶（ALP）、糖类抗原125（CA125）、年龄和消融的列线图。该模型具有良好的判别能力，C-index为0.722，训练队列中6个月和12个月OS预测的AUC值分别为0.828和0.851，而在验证队列中，相应的AUC值分别为0.754和0.791。校准曲线显示拟合良好，证实了模型的稳定性。确定风险分层的最佳截断值为353.3，高危组和低危组的OS差异具有统计学意义。
基于免疫炎症评分、凝血指标和其他临床病理因素的列线图能够有效预测MPC患者的OS。
