Mean Corpuscular Volume as a Prognostic Marker in Patients with Non-Small Cell Lung Cancer Undergoing Surgical...

Background and Objectives: Anatomical staging alone insufficiently explains survival heterogeneity in patients with resected non-small cell lung cancer (NSCLC). Although inflammation-based biomarkers have demonstrated prognostic value, the clinical relevance of erythrocyte-derived indices-particularly mean corpuscular volume (MCV)-remains poorly characterized in this setting. This study evaluated the prognostic significance of preoperative MCV and examined whether its integration with the Noble and Underwood (NUn) score improves survival prediction. Methods: We retrospectively analyzed patients with stage I-IIIA NSCLC who underwent complete surgical resection. Associations between clinicopathological variables and overall survival (OS) were assessed using Cox proportional hazards models. Prognostic performance was evaluated using the concordance index and the integrated time-dependent area under the curve. Continuous variables were modeled on their original scale without dichotomization. Results: Model comparison using the Akaike Information Criterion indicated that incorporation of the composite NUn-MCV index into the intermediate model-comprising age, basal metabolic rate, American Society of Anesthesiologists physical status, pleural invasion, and pathological stage-provided a superior model fit compared with inclusion of the NUn score and MCV as separate covariates. On this basis, the composite NUn-MCV model was selected as the full model. Across all evaluations, the full model demonstrated consistently greater discriminative ability for survival prediction than both the intermediate model and the baseline model based solely on pathological stage. Conclusions: Preoperative MCV independently predicts OS in patients with resected stage I-IIIA NSCLC. Integration of MCV with the NUn score into a composite index provides incremental prognostic value beyond anatomical staging and established clinical factors, supporting its use as a complementary tool for postoperative risk stratification.