Wang Hui-Ke, Wei Qian, Yang Ya-Lan, Lu Tai-Ying, Yan Yan, Wang Feng
Department of Oncology, The First Affiliated Hospital of Zhengzhou University, No.50 Eastern Jianshe Road, Zhengzhou, 450052, Henan, China.
Cancer Cell Int. 2023 Jan 27;23(1):13. doi: 10.1186/s12935-023-02856-3.
Multiple perioperative inflammatory markers are considered important factors affecting the long-term survival of esophageal cancer (EC) patients. Hematological parameters, whether single or combined, have high predictive value.
To investigate the inflammatory status of patients with preoperative EC using blood inflammatory markers, and to establish and validate competing risk nomogram prediction models for overall survival (OS) and progression-free survival (PFS) in EC patients.
A total of 508 EC patients who received radical surgery (RS) treatment in The First Affiliated Hospital of Zhengzhou University from August 5, 2013, to May 1, 2019, were enrolled and randomly divided into a training cohort (356 cases) and a validation cohort (152 cases). We performed least absolute shrinkage and selection operator (LASSO)-univariate Cox- multivariate Cox regression analyses to establish nomogram models. The index of concordance (C-index), time-dependent receiver operating characteristic (ROC) curves, time-dependent area under curve (AUC) and calibration curves were used to evaluate the discrimination and calibration of the nomograms, and decision curve analysis (DCA) was used to evaluate the net benefit of the nomograms. The relative integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were calculated to evaluate the improvement in predictive accuracy of our new model compared with the AJCC staging system and another traditional model. Finally, the relationship between systemic inflammatory response markers and prognostic survival was explored according to risk plot, time-dependent AUC, Kaplan-Meier and restricted cubic spline (RCS).
Based on the multivariate analysis for overall survival (OS) in the training cohort, nomograms with 10 variables, including the aggregate index of systemic inflammation (AISI) and lymphocyte-to-monocyte ratio (LMR), were established. Time-dependent ROC, time-dependent AUC, calibration curves, and DCA showed that the 1-, 3-, and 5 year OS and PFS probabilities predicted by the nomograms were consistent with the actual observations. The C-index, NRI, and IDI of the nomograms showed better performance than the AJCC staging system and another prediction model. Moreover, risk plot, time-dependent AUC, and Kaplan-Meier showed that higher AISI scores and lower LMR were associated with poorer prognosis, and there was a nonlinear relationship between them and survival risk.
AISI and LMR are easy to obtain, reproducible and minimally invasive prognostic tools that can be used as markers to guide the clinical treatment and prognosis of patients with EC.
多种围手术期炎症标志物被认为是影响食管癌（EC）患者长期生存的重要因素。血液学参数，无论是单一参数还是联合参数，都具有较高的预测价值。
利用血液炎症标志物调查术前EC患者的炎症状态，并建立和验证EC患者总生存（OS）和无进展生存（PFS）的竞争风险列线图预测模型。
选取2013年8月5日至2019年5月1日在郑州大学第一附属医院接受根治性手术（RS）治疗的508例EC患者，随机分为训练队列（356例）和验证队列（152例）。我们进行了最小绝对收缩和选择算子（LASSO）-单因素Cox-多因素Cox回归分析以建立列线图模型。一致性指数（C指数）、时间依赖性受试者工作特征（ROC）曲线、时间依赖性曲线下面积（AUC）和校准曲线用于评估列线图的辨别力和校准度，决策曲线分析（DCA）用于评估列线图的净效益。计算相对综合辨别改善（IDI）和净重新分类改善（NRI），以评估我们的新模型与美国癌症联合委员会（AJCC）分期系统及另一个传统模型相比在预测准确性方面的改善。最后，根据风险图、时间依赖性AUC、Kaplan-Meier曲线和限制性立方样条（RCS）探索全身炎症反应标志物与预后生存之间的关系。
基于训练队列中总生存（OS）的多因素分析，建立了包含全身炎症综合指数（AISI）和淋巴细胞与单核细胞比值（LMR）等10个变量的列线图。时间依赖性ROC、时间依赖性AUC、校准曲线和DCA显示，列线图预测的1年、3年和5年OS及PFS概率与实际观察结果一致。列线图的C指数、NRI和IDI表现优于AJCC分期系统和另一个预测模型。此外，风险图、时间依赖性AUC和Kaplan-Meier曲线显示，较高的AISI评分和较低的LMR与较差的预后相关，且它们与生存风险之间存在非线性关系。
AISI和LMR是易于获得、可重复且微创的预后工具，可作为指导EC患者临床治疗和预后的标志物。
