Inflammatory composite indicators and clinical features for prognostic factor analysis and malignancy prediction model construction in patients with glioma

Abstract

Introduction/Objective. The objective was to evaluate inflammatory composite indicators and clinical features as prognostic factors and develop a predictive model for glioma malignancy grade. Methods. A retrospective analysis of 107 patients with glioma who underwent surgical resection was conducted. Clinical data, including age, gender, body mass index, Karnofsky Performance Status (KPS), tumour characteristics and complete blood count parameters, were collected preoperatively and postoperatively. Cox proportional hazards regression was performed to identify independent prognostic factors for progression-free survival (PFS). Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of inflammatory markers in predicting high-grade glioma. Results. The cohort included 107 patients (mean age: 54.3 ± 13.5 years, 53.3% men). High-grade gliomas (World Health Organization grade IV) accounted for 55.1% of cases. Perioperative neutrophil counts increased significantly, whereas lymphocyte counts remained stable. In multivariate Cox analysis, younger age (hazard ratio 0.93, 95% confidence interval: 0.89-0.98, p = 0.009) was independently associated with improved PFS. The ROC analysis demonstrated that age (area under the curve [AUC] = 0.632, p = 0.019) and preoperative neutrophil-to-lymphocyte ratio (NLR) (AUC = 0.606, p = 0.058) showed potential in predicting high-grade glioma. Spearman correlation analysis revealed a significant negative correlation between KPS and NLR. Conclusions. Age represents an independent prognostic factor for PFS in patients with glioma. Preoperative inflammatory markers, particularly NLR, combined with clinical parameters such as age, show promise in predicting glioma malignancy grade.