Exploring the implications of modified advanced lung cancer inflammation index on outcomes in patients with advanced non-small cell lung cancer

AIM: Sarcopenia and skeletal muscle density (SMD) have been shown to be both predictive and prognostic marker in oncology. Advanced lung cancer inflammation index (ALI) has been shown to predict overall survival (OS) in small cell lung cancer (SCLC). Computed tomography (CT) enables skeletal muscle to be quantified, whereas body mass index (BMI) cannot accurately reflect body composition. The purpose was to evaluate the prognostic value of modified ALI (mALI) using CT-determined third lumbar vertebra (L3) muscle index beyond original ALI and see the interaction between sarcopenia, SMD, neutrophil-lymphocyte ratio (NLR), ALI and mALI at baseline and post 4 cycles of chemotherapy and their effects on OS and progress free survival (PFS) in patients with advanced non-SCLC (NSCLC). METHODS: This retrospective study consisted of a total of 285 advanced NSCLC patients. The morphometric parameters such as SMD, skeletal muscle index (SMI) and fat-free mass (FFM) were measured by CT at the L3 vertebra. ALI was defined as BMI × serum albumin/NLR and mALI was defined as SMI × serum albumin/NLR. RESULTS: Sarcopenia was observed in over 70% of patients across all BMI categories. Patients having sarcopenia suffered from a higher incidence of chemotherapeutic drug toxicities but this was not found to be statistically significant. Concordance was seen between ALI and mALI in the pre-treatment setting and this was statistically significant. A significant proportion of patients with poor ALI (90.9%), poor pre-chemotherapy mALI (91.3%) and poor post-chemotherapy mALI (89%) had poor NLR and each of them was statistically significant. CONCLUSIONS: In both univariate and multivariate analyses, this study demonstrated the statistical significance of sarcopenia, SMD, and mALI as predictive factors for OS. Additionally, sarcopenia and SMD were also found to be statistically significant factors in predicting PFS. These biomarkers could potentially help triage patients for active nutritional intervention for better outcomes.