Chen Xiao-Yong, Pan Ding-Long, Xu Jia-Heng, Chen Yue, Xu Wei-Feng, Chen Jin-Yuan, Wu Zan-Yi, Lin Yuan-Xiang, You Hong-Hai, Ding Chen-Yu, Kang De-Zhi
Department of Neurosurgery, Neurosurgical Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Department of Radiation Oncology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Front Oncol. 2022 Jan 27;11:754920. doi: 10.3389/fonc.2021.754920. eCollection 2021.
To evaluate the prognostic value of serum inflammatory biomarkers and develop a risk stratification model for high-grade glioma (HGG) patients based on clinical, laboratory, radiological, and pathological factors.
A retrospective study of 199 patients with HGG was conducted. Patients were divided into a training cohort (n = 120) and a validation cohort (n = 79). The effects of potential associated factors on the overall survival (OS) time were investigated and the benefits of serum inflammatory biomarkers in improving predictive performance was assessed. Univariable and multivariable Cox regression analyses, the least absolute shrinkage and selection operator (LASSO) regression analysis, and support vector machines (SVM) were used to select variables for the final nomogram model.
After multivariable Cox, LASSO, and SVM analysis, in addition to 3 other clinico-pathologic factors, platelet-to-lymphocyte ratio (PLR) >144.4 (hazard ratio [HR], 2.05; 95% confidence interval [CI], 1.25-3.38; = 0.005) were left for constructing the predictive model. The model with PLR exhibited a better predictive performance than that without them in both cohorts. The nomogram based on the model showed an excellent ability of discrimination in the entire cohort (C-index, 0.747; 95%CI, 0.706-0.788). The calibration curves showed good consistency between the predicted and observed survival probability.
Our study confirmed the prognostic value of serum inflammatory biomarkers including PLR and established a comprehensive scoring system for the OS prediction in HGG patients.
评估血清炎症生物标志物的预后价值，并基于临床、实验室、影像学和病理因素为高级别胶质瘤（HGG）患者建立风险分层模型。
对199例HGG患者进行回顾性研究。患者被分为训练队列（n = 120）和验证队列（n = 79）。研究潜在相关因素对总生存（OS）时间的影响，并评估血清炎症生物标志物在改善预测性能方面的作用。采用单变量和多变量Cox回归分析、最小绝对收缩和选择算子（LASSO）回归分析以及支持向量机（SVM）来选择最终列线图模型的变量。
经过多变量Cox、LASSO和SVM分析，除其他3个临床病理因素外，血小板与淋巴细胞比值（PLR）>144.4（风险比[HR]，2.05；95%置信区间[CI]，1.25 - 3.38；P = 0.005）被保留用于构建预测模型。在两个队列中，包含PLR的模型均比不包含PLR的模型具有更好的预测性能。基于该模型的列线图在整个队列中显示出优异的区分能力（C指数，0.747；95%CI，0.706 - 0.788）。校准曲线显示预测生存概率与观察到的生存概率之间具有良好的一致性。
我们的研究证实了包括PLR在内的血清炎症生物标志物的预后价值，并为HGG患者OS预测建立了一个综合评分系统。
