Institute of Oncology, Shandong Provincial Hospital Affiliated to Shandong University, 324 Jingwu Road, Jinan, 250021, People's Republic of China.
Department of Clinical Oncology, The University of Hong Kong, Laboratory block, 21 Sassoon, Pokfulam, Hong Kong, People's Republic of China.
BMC Cancer. 2018 Jun 26;18(1):692. doi: 10.1186/s12885-018-4513-4.
Emerging inflammatory response biomarkers are developed to predict the survival of patients with cancer, the aim of our study is to establish an inflammation-related nomogram based on the classical predictive biomarkers to predict the survivals of patients with non-small cell lung cancer (NSCLC).
Nine hundred and fifty-two NSCLC patients with lung cancer surgery performed were enrolled into this study. The cutoffs of inflammatory response biomarkers were determined by Receiver operating curve (ROC). Univariate and multivariate analysis were conducted to select independent prognostic factors to develop the nomogram.
The median follow-up time was 40.0 months (range, 1 to 92 months). The neutrophil to lymphocyte ratio (cut-off: 3.10, HR:1.648, P = 0.045) was selected to establish the nomogram which could predict the 5-year OS probability. The C-index of nomogram was 0.72 and the 5-year OS calibration curve displayed an optimal agreement between the actual observed outcomes and the predictive results.
Neutrophil to lymphocyte ratio was shown to be a valuable biomarker for predicting survival of patients with NSCLC. The addition of neutrophil to lymphocyte ratio could improve the accuracy and predictability of the nomogram in order to provide reference for clinicians to assess patient outcomes.
新兴的炎症反应生物标志物被开发出来,以预测癌症患者的生存情况。我们的研究旨在建立一个基于经典预测生物标志物的炎症相关列线图,以预测非小细胞肺癌(NSCLC)患者的生存情况。
本研究纳入了 952 名接受肺癌手术的 NSCLC 患者。通过接收者操作曲线(ROC)确定炎症反应生物标志物的截断值。进行单因素和多因素分析,以选择独立的预后因素来开发列线图。
中位随访时间为 40.0 个月(范围 1 至 92 个月)。中性粒细胞与淋巴细胞比值(截断值:3.10,HR:1.648,P=0.045)被选用来建立列线图,以预测 5 年 OS 概率。列线图的 C 指数为 0.72,5 年 OS 校准曲线显示实际观察结果与预测结果之间具有最佳一致性。
中性粒细胞与淋巴细胞比值是预测 NSCLC 患者生存情况的有价值的生物标志物。加入中性粒细胞与淋巴细胞比值可以提高列线图的准确性和预测能力,为临床医生评估患者预后提供参考。