摘要
目的探讨TACE联合微波消融(MWA)术前中性粒细胞与淋巴细胞比率(NLR)的变化在评估肝癌患者预后方面的临床意义。方法回顾性分析2005年3月到2012年5月行TACE联合MWA的174例肝细胞性肝癌患者的临床资料。通过倾向性匹配分析按1∶1进行匹配后分为术前高NLR组和低NLR组,截止数值是2.37,观察指标是患者的生存期和术前NLR。结果实际匹配的患者41对,配对后组间资料具有可比性,结果表明术前NLR为2.37是中位生存期(mOS)的独立预测因子。术前NLR<2.37的患者比术前NLR≥2.37的患者存活期更长。结论术前NLR为2.37可作为行TACE联合MWA治疗的肝癌患者预测mOS的补充因子。
Objective To discuss the clinical significance of preoperative neutrophil-to-lymphocyte ratio (NLR) in predicting the prognosis of patients with hepatoeellular carcinoma (HCC) before transeatheter arterial chemoembolization (TACE) combined with microwave ablation (MWA). Methods The clinical and pathological data of 174 HCC patients, who had received TACE combined with MWA at authors" hospital from March 2005 to May 2012, were retrospectively analyzed. Using the propensity score-based analysis, one-to-one nearest-neighbor matching was performed, and the patients were divided into preoperative high- NLR group and low-NLR group, with the cutoff value being 2.37. The patient survival time and preoperative NLR were used as the observation indexes. Results A total of 41 pairings of actually-matched patients were obtained. The materials of the tow paired groups were comparable. The results of analysis indicated that preoperative NLR of 2.37 was an independent prediction factor for median overall survival (mOS). Patients with a preoperative NLR〈2.37 survived longer than patients with a preoperative NLR 〉12.37. Conclusion The preoperative NLR value of 2.37 may be used as a supplementary index for predicting mOS in HCC patients who have received TACE combined with MWA therapy.
作者
严守美
崔新江
于志军
邢辉
赵邦利
杜苗苗
曹贵文
YAN Shoumei;CUI Xinjiang;YU Zhijun;XING Hui;ZHAO Bangli;DU Miaomiao;CAO Guiwen(Department of Medical Imaging, Weifang Medical College, Weifang, Shandong Province 261053, China)
出处
《介入放射学杂志》
CSCD
北大核心
2018年第7期632-635,共4页
Journal of Interventional Radiology
关键词
肝细胞性肝癌
经肝动脉化疗栓塞术
微波消融
中性粒细胞与淋巴细胞比率
倾向性
匹配分析
hepatocellular carcinoma
transcatheter arterial chemoembolization
microwave ablation
neutrophil-to-lymphocyte ratio
propensity score-based analysis