Wuxi School of Medicine, Jiangnan University, Wuxi, China.
Department of Hematology, Affiliated Hospital of Jiangnan University, Wuxi, China.
Technol Cancer Res Treat. 2023 Jan-Dec;22:15330338231180785. doi: 10.1177/15330338231180785.
Systemic inflammatory indicators are clinically significant in guiding diffuse large B-cell lymphoma (DLBCL) prognosis. However, which inflammatory markers are the best predictors of DLBCL prognosis is still unclear. In this study, we aimed to create a nomogram based on the best inflammatory markers and clinical indicators to predict the overall survival of patients with DLBCL. We analyzed data from 423 DLBCL patients from two institutions and divided them into a training set, an internal validation set, and an external validation set (n = 228, 97, and 98, respectively). The least absolute shrinkage and selection operator and Cox regression analysis were used to develop nomograms. We assessed model fit using the Akaike information criterion and Bayesian information criterion. The concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to assess the nomogram's predictive performance and clinical net benefit and compared with the International Prognostic Index (IPI) and National Comprehensive Cancer Network (NCCN)-IPI. The inclusion variables for the nomogram model were age, Eastern Cooperative Oncology Group performance status, lactate dehydrogenase level, the systemic immune-inflammation index (SII), the prognostic nutritional index (PNI), and β-2 microglobulin (β-2 MG) level. In the training cohort, the nomogram showed better goodness of fit than the IPI and NCCN-IPI. The C-index of the nomogram (0.804, 95% CI: 0.751-0.857) outperformed the IPI (0.690, 95% CI: 0.629-0.751) and NCCN-IPI (0.691, 95% CI: 0.632-0.750). The calibration curve, ROC curve, and DCA curve analysis showed that the nomogram has satisfactory predictive power and clinical utility. Similar results were found in the validation cohort. The nomogram integrated with the clinical characteristics and inflammatory markers is beneficial to predict the prognosis of patients with DLBCL.
系统性炎症指标在指导弥漫性大 B 细胞淋巴瘤（DLBCL）预后方面具有重要的临床意义。然而，哪种炎症标志物是预测 DLBCL 预后的最佳指标仍不清楚。本研究旨在建立一个基于最佳炎症标志物和临床指标的列线图，以预测 DLBCL 患者的总生存率。
我们分析了来自两个机构的 423 例 DLBCL 患者的数据，并将其分为训练集、内部验证集和外部验证集（n=228、97 和 98）。使用最小绝对收缩和选择算子和 Cox 回归分析来开发列线图。我们使用赤池信息量准则和贝叶斯信息准则来评估模型拟合度。使用一致性指数（C-index）、校准曲线、接受者操作特征（ROC）曲线和决策曲线分析（DCA）来评估列线图的预测性能和临床净获益，并与国际预后指数（IPI）和国家综合癌症网络（NCCN）-IPI 进行比较。列线图模型的纳入变量包括年龄、东部合作肿瘤组表现状态、乳酸脱氢酶水平、全身免疫炎症指数（SII）、预后营养指数（PNI）和β-2 微球蛋白（β-2MG）水平。在训练队列中，列线图的拟合优度优于 IPI 和 NCCN-IPI。列线图的 C-index（0.804，95%CI：0.751-0.857）优于 IPI（0.690，95%CI：0.629-0.751）和 NCCN-IPI（0.691，95%CI：0.632-0.750）。校准曲线、ROC 曲线和 DCA 曲线分析表明，该列线图具有良好的预测能力和临床实用性。验证队列中也得到了类似的结果。
该列线图综合了临床特征和炎症标志物，有助于预测 DLBCL 患者的预后。
