Subcutaneous adipose tissue radiodensity as a prognostic marker in metastatic non-small cell lung cancer treated with immune checkpoint inhibitors

Introduction
Radiodensity of subcutaneous adipose tissue (SAT), measurable on routine computed tomography (CT), may reflect metabolic status and cachexia, both of which influence cancer outcomes. However, its prognostic role in metastatic non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICI) remains unclear. This study aimed to evaluate the prognostic value of SAT radiodensity in this patient population.

Material and methods
The retrospective analysis included 92 patients with stage IV NSCLC receiving ICI. Subcutaneous adipose tissue radiodensity (Hounsfield units) was measured from pre-treatment CT at the L3 level and categorized into quartiles. Kaplan-Meier analysis, log-rank test, and Cox proportional hazards models were used. Nonlinear associations were assessed using restricted cubic splines. Cox models were? adjusted for demographic, clinical, and treatment factors. A p-value < 0.05 was considered statistically significant.

Results
Median overall survival for Q1, Q2, Q3, and Q4 was 13.4, 26.3, 18.4, and 14.2 months, respectively (log-rank p = 0.0226). Compared with Q1, Q2 showed a significantly reduced mortality risk across all models (fully adjusted hazard ratios = 0.32, 95% CI: 0.15–0.64, p = 0.002). Q3 and Q4 were not significantly different from Q1. Restricted cubic spline analysis revealed a mild U-shaped relationship (p for nonlinearity = 0.0094), with intermediate SAT density linked to best outcomes. Programmed death ligand 1 expression significantly modified the SAT-survival association (p for interaction < 0.0001).

Conclusions
Moderate SAT radiodensity was associated with improved survival in metastatic NSCLC patients on ICI, potentially reflecting an optimal metabolic-immune balance. Subcutaneous adipose tissue density, easily obtained from routine imaging, warrants further prospective validation as a scalable prognostic biomarker.