CT-based body composition and inflammatory nutritional biomarker nomogram for predicting early postoperative...

The inflammatory-nutritional status of the human body holds considerable clinical significance for the prognosis of patients with malignant neoplasms. Meanwhile, the assessment of body composition (including adipose tissue and skeletal muscle) via computed tomography (CT) imaging also exhibits a significant correlation with the prognostic outcomes of patients with non-small cell lung cancer (NSCLC). However, the association between these two factors and early recurrence (ER) remains unclear. This study aims to evaluate the prognostic value of body composition and inflammatory nutritional biomarker (BCINB) in patients with NSCLC. A CT-based BCINB nomogram was developed to predict postoperative ER.

A training cohort (251 patients, Jiangxi Cancer Hospital) and an external test cohort (104 patients, The Second Affiliated Hospital of Nanchang University) were analyzed. Body composition metrics and clinical-pathological parameters were evaluated. Least absolute shrinkage and selection operator (LASSO)-Cox regression identified BCINB components, and multivariate Cox regression determined ER predictors. Nomogram performance was validated via concordance index (C-index), calibration curves, time-dependent receiver operator characteristic curve (ROC) analysis, and decision curve analysis (DCA).

The BCINB score integrated systemic inflammation index (SII), systemic inflammatory response index (SIRI), albumin-globulin ratio (AGR), intramuscular adipose content (IMAC), intermuscular adipose tissue (IMAT) area, subcutaneous adipose tissue index (SATI), skeletal muscle density (SMD), and skeletal muscle index (SMI). It correlated with male sex, age >65 years, tumor size >3 cm, and stage III disease. BCINB independently predicted recurrence-free survival (RFS) [hazard ratio (HR): 13.853, 95% confidence interval (CI): 6.393-30.018]. The nomogram combining BCINB with clinicopathological variables yielded C-indices of 0.822 (95% CI: 0.78-0.864) and 0.806 (95% CI: 0.736-0.869) in training and test cohorts, respectively. Calibration curves confirmed accuracy in recurrence risk prediction. Compared to pathological Tumor-Node-Metastasis (pTNM) staging, the nomogram provided superior discrimination and clinical benefit for 1- and 2-year RFS across broader threshold probabilities.

The BCINB score, integrating body composition, inflammation, and nutritional markers, is a robust prognostic tool for NSCLC. The nomogram enables precise postoperative ER risk stratification, outperforming conventional staging systems.