非肌肉浸润性膀胱癌患者全身免疫炎症指数和中性粒细胞淋巴细胞比值与疾病复发和进展风险的关系

Mehmet Hamza GÜLTEKİN, Ufuk ÇAĞLAR, Abdullah ESMERAY, Akif ERBİN, Fatih YANARAL, Murat BAYKAL, Faruk ÖZGÖR, Ömer SARİLAR, Mehmet Fatih AKBULUT
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引用次数: 0

摘要

目的:一些基于全身炎症反应的生物标志物有望预测非肌肉浸润性膀胱癌(NMIBC)患者的预后,并且可以在不需要任何重大成本的情况下进行风险分类。我们的目的是评估中性粒细胞-淋巴细胞比率(NLR)和全身免疫炎症指数(SII)对NMIBC患者复发和进展风险的预测。材料和方法:该研究包括对2015年至2019年在三级转诊中心接受经尿道膀胱切除术(TURB)的211例患者的回顾性分析。采用受试者工作特征(ROC)曲线确定截止值。构建Kaplan-Meier曲线和log-rank检验,根据不同水平的炎症标志物评估无复发生存率和无进展生存率。采用多元回归分析估计独立预后因素。结果:经ROC分析,SII的最佳临界值为568。多因素分析显示,SII值、初发TURB时肿瘤数、欧洲癌症研究与治疗组织(EORTC)复发分型是预测复发的有统计学意义的参数。肿瘤大小、NLR和SII在单因素分析中达到统计学显著水平,但在多因素分析中无统计学意义。结论:SII、肿瘤数目、EORTC复发分型是评估复发的预后参数。然而,炎症参数在预测进展率方面不具有相同的预测能力。
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Relationship of Systemic Immune-Inflammation Index and Neutrophil-Lymphocyte ratio with Disease Recurrence and Progression risk in Non-Muscle-Invasive Bladder Cancer
Objective: Some systemic inflammatory response-based biomarkers are promising for predicting prognosis of non-muscle-invasive bladder cancer (NMIBC) patients and can contribute to the risk classification without any significant cost. We aimed to evaluate the neutrophil-lymphocyte ratio (NLR) and systemic immune-inflammation index (SII) for the prediction of recurrence and progression risk in patients with NMIBC. Material and Methods: The study included a retrospective analysis of 211 patients who underwent transurethral resection of bladder (TURB) in a tertiary referral center between 2015 and 2019. The receiver operating characteristic (ROC) curve was used to determine the cut-off value. The Kaplan-Meier curves and the log-rank test were constructed to evaluate the recurrence-free and progression-free survival rates according to different levels of inflammatory markers. The multivariate regression analysis was undertaken to estimate the independent prognostic factors. Results: The optimal cut-off value of SII was found to be 568 in the ROC analysis. According to the multivariate analysis, the SII value, number of tumors at the time of initial TURB, and European Organization for Research and Treatment of Cancer (EORTC) recurrence classification were statistically significant parameters in predicting recurrence. While tumor size, NLR, and SII achieved statistically significant levels in the univariate analysis, they didn’t have significance in the multivariate analysis. Conclusion: The SII, number of tumors, and EORTC recurrence classification are prognostic parameters that can be used in the assessment of recurrence. However, inflammatory parameters do not have the same predictive ability in the prediction of the progression rate.
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