The Prognostic Value of Multiple Systemic Inflammatory Biomarkers in Preoperative Patients With Non-small Cell Lung Cancer

IntroductionThe preoperative inflammatory and nutrient status of the patient are closely correlated to the outcome of surgery-based treatment for non-small cell lung cancer (NSCLC). We aimed to investigate the prognostic value of inflammation and nutrient biomarkers in preoperative patients with non-small cell lung cancer (NSCLC) by constructing a prognostic predictive model. MethodsWe retrospectively studied 995 patients with NSCLC who underwent surgery in the Shandong Provincial Hospital and randomly allocated them into the training and validation group with a ratio of 7:3. We then compared their prognostic performance and conducted univariate Cox analyses with several clinicopathological variables. Based on the performance of the receiver operating characteristic (ROC) curves and decision curves analysis (DCA), the prognostic model was optimized and validated. ResultThe median overall overall survival (OS) of patients was 74 months. Univariate Cox analysis indicated that fifteen inflammatory biomarkers were significantly correlated with OS (p < 0.100). Multivariate Cox analysis revealed that the model incorporating grade, age, stage, basophil-to-lymphocyte ratio (BLR, >= 0.00675 vs. < 0.00675) and albumin-to-globulin ratio (AGR, >= 1.40 vs. <1.40) showed the maximum area under the curve (AUC, 0.744). The C-index in the training and validation group was 0.690 and 0.683, respectively. The 3-year integrated discrimination improvement (IDI) compared to TNM (Tumor Node Metastasis) stage was 0.035 vs. 0.011 in the training and validation group, respectively. ConclusionsLower AGR, ANRI, and higher BLR were associated with a worse outcome for patients with NSCLC. We constructed a prognostic nomogram with risk stratification based on inflammatory and nutrient biomarkers. The discrimination and calibration abilities of the model were evaluated to confirm its validity, indicating the potential utility of this prognostic model for clinical guidance.