LAUSR

Simple Summary Patients with advanced epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma have been known to respond to first-line tyrosine kinase inhibitor (TKI) treatment. However, a subgroup of patients are non-responsive to the treatment, with poor survival outcomes, and those who are initially responsive may still experience resistance. A reliable prognostic tool may provide a valuable direction for tailoring individual treatment strategies in this clinical setting. With this aim, the present study explores the prognostic power of the combination of the systemic inflammation index (portrayed by hematological markers) and tumor glycolytic heterogeneity (characterized by 18F-fluorodeoxyglucose positron emission tomography images). The model integrating these two biomarkers could be used to improve risk stratification, and the subsequent personalized management strategy in patients with advanced EGFR-mutated lung adenocarcinoma. Abstract Tyrosine kinase inhibitors (TKIs) are the first-line treatment for patients with advanced epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma. Over half of patients failed to achieve prolonged survival benefits from TKI therapy. Awareness of a reliable prognostic tool may provide a valuable direction for tailoring individual treatments. We explored the prognostic power of the combination of systemic inflammation markers and tumor glycolytic heterogeneity to stratify patients in this clinical setting. One hundred and five patients with advanced EGFR-mutated lung adenocarcinoma treated with TKIs were retrospectively analyzed. Hematological variables as inflammation-induced biomarkers were collected, including the neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), and systemic inflammation index (SII). First-order entropy, as a marker of heterogeneity within the primary lung tumor, was obtained by analyzing 18F-fluorodeoxyglucose positron emission tomography images. In a univariate Cox regression analysis, sex, smoking status, NLR, LMR, PLR, SII, and entropy were associated with progression-free survival (PFS) and overall survival (OS). After adjusting for confounders in the multivariate analysis, smoking status, SII, and entropy, remained independent prognostic factors for PFS and OS. Integrating SII and entropy with smoking status represented a valuable prognostic scoring tool for improving the risk stratification of patients. The integrative model achieved a Harrell’s C-index of 0.687 and 0.721 in predicting PFS and OS, respectively, outperforming the traditional TNM staging system (0.527 for PFS and 0.539 for OS, both p < 0.001). This risk-scoring model may be clinically helpful in tailoring treatment strategies for patients with advanced EGFR-mutated lung adenocarcinoma.