D'Andrea David, Moschini Marco, Gust Kilian M, Abufaraj Mohammad, Özsoy Mehmet, Mathieu Romain, Soria Francesco, Briganti Alberto, Rouprêt Morgan, Karakiewicz Pierre I, Shariat Shahrokh F
Department of Urology, Medical University of Vienna, Vienna, Austria.
Urological Research Institute, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy.
J Surg Oncol. 2017 Mar;115(4):455-461. doi: 10.1002/jso.24521. Epub 2017 Jan 20.
To evaluate the role of lymphocyte-to-monocyte ratio (LMR) and neutrophil-to-lymphocyte ratio (NLR) as pre-operative markers for predicting extravesical disease and survival outcomes in patients undergoing radical cystectomy (RC) for urothelial carcinoma of the bladder (UCB).
Data from 4335 patients undergoing RC for clinically non-metastatic UCB were analyzed. Multivariable logistic regression models were used to predict lymph node involvement and extravesical disease (defined as ≥pT3 and N0). Recurrence-free (RFS), cancer-specific (CSS), and overall survival (OS) were evaluated using multivariable Cox models. The accuracy of the models was assessed with receiver operating characteristics (ROC) curves and concordance-index.
Median LMR was 3.5 and median NLR was 2.7. Addition of LMR and NLR to a standard preoperative model improved its discrimination for prediction of lymph node metastasis by 4.5%. On multivariable analysis LMR and NLR independently predicted RFS, CSS, and OS. The discrimination of this model increased by adding LMR and NLR but was not significant.
LMR and NLR independently improved the preoperative prediction of lymph node metastasis and survival outcomes. As they are readily available, they could be integrated in a panel of preoperative markers helping selecting patients who have extravesical lymph node involvement and more aggressive disease.
评估淋巴细胞与单核细胞比值(LMR)和中性粒细胞与淋巴细胞比值(NLR)作为术前标志物,在预测接受根治性膀胱切除术(RC)治疗膀胱尿路上皮癌(UCB)患者的膀胱外疾病及生存结局中的作用。
分析了4335例接受RC治疗临床无转移UCB患者的数据。采用多变量逻辑回归模型预测淋巴结受累情况及膀胱外疾病(定义为≥pT3且N0)。使用多变量Cox模型评估无复发生存期(RFS)、癌症特异性生存期(CSS)和总生存期(OS)。通过受试者工作特征(ROC)曲线和一致性指数评估模型的准确性。
LMR中位数为3.5,NLR中位数为2.7。将LMR和NLR添加到标准术前模型中,可将其预测淋巴结转移的辨别力提高4.5%。多变量分析显示,LMR和NLR可独立预测RFS、CSS和OS。添加LMR和NLR后该模型的辨别力有所提高,但差异不显著。
LMR和NLR可独立改善术前对淋巴结转移及生存结局的预测。由于它们易于获得,可纳入术前标志物组合,有助于筛选出有膀胱外淋巴结受累及更具侵袭性疾病的患者。