Li Xiao-Na, Zong Ying-Rui, Zhang Yan-Xi, Hou Zhen-Zhen, Lu Li-Wen
Department of Aesthetic Stomatology, First Affiliated Hospital of Zhengzhou University. Zhengzhou 450000, Henan Province, China. E-mail:
Shanghai Kou Qiang Yi Xue. 2024 Apr;33(2):205-210.
To investigate the relationship between preoperative systemic immune-inflammation index (SII) and relapse-free survival (RFS) after surgical resection of mucoepidermoid carcinoma(MEC).
The data of 135 patients with MEC who underwent surgical resection in the First Affiliated Hospital of Zhengzhou University from January 2016 to July 2019 were collected, and the receiver operating characteristic(ROC) curve was performed on the SII of patients. The optimal cut-off value was obtained by ROC analysis. Therefore, the patients' SII index was divided into high and low group, and survival analysis was performed by Kaplan-Meier method. Cox proportional regression model and least absolute shrinkage and selection operator (LASSO) were used to analyze the factors influencing prognosis, and a nomogram model was built to predict patients' relapse-free survival(RFS). Area under curve (AUC) and correction curve were used to evaluate the model and verify the consistency.
Survival analysis showed that the RFS rate in low SII group was significantly higher than that in high SII group. Cox proportional hazard regression model showed high SII(HR=2.179, 95%CI: 1.072-4.426, P=0.031) and low tumor differentiation(HR=6.894, 95%CI: 2.770-17.158, P=0.000) and cervical lymph node metastasis (HR=2.091, 95%CI: 1.034-4.230, P=0.040) were significant predictors of poor RFS.
The lower the preoperative SII, the better the prognosis of patients. The nomogram prognosis of MEC based on SII is effective.
探讨术前全身免疫炎症指数(SII)与黏液表皮样癌(MEC)手术切除后无复发生存期(RFS)之间的关系。
收集2016年1月至2019年7月在郑州大学第一附属医院接受手术切除的135例MEC患者的数据,并对患者的SII进行受试者操作特征(ROC)曲线分析。通过ROC分析获得最佳截断值。因此,将患者的SII指数分为高、低两组,采用Kaplan-Meier法进行生存分析。采用Cox比例回归模型和最小绝对收缩和选择算子(LASSO)分析影响预后的因素,并建立列线图模型预测患者的无复发生存期(RFS)。采用曲线下面积(AUC)和校正曲线评估模型并验证一致性。
生存分析显示,低SII组的RFS率显著高于高SII组。Cox比例风险回归模型显示,高SII(HR=2.179,95%CI:1.072-4.426,P=0.031)、低肿瘤分化(HR=6.894,95%CI:2.770-17.158,P=0.000)和颈部淋巴结转移(HR=2.091,95%CI:1.034-4.230,P=0.040)是RFS不良的显著预测因素。
术前SII越低,患者预后越好。基于SII的MEC列线图预后评估有效。