18F-RGD PET/CT and Systemic Inflammatory Biomarkers Predict Outcomes of Patients With Advanced NSCLC Receiving Combined Antiangiogenic Treatment.

Background: The aim of this study was to evaluate 18F-AlF-NOTA-PRGD2 positron emission tomography/computed tomography (18F-RGD PET/CT) and serum inflammation biomarkers for predicting the outcomes of patients receiving combined antiangiogenic treatment for advanced non-small cell lung cancer (NSCLC). Methods: Patients with advanced NSCLC underwent 18F-RGD PET/CT examination and provided blood samples before treatments commenced. PET/CT parameters included maximum standard uptake value (SUVmax) and mean standard uptake value (SUVmean), peak standard uptake value (SUVpeak) and metabolic tumor volume (MTV) for all contoured lesions. Biomarkers for inflammation included the pretreatment neutrophil-to-lymphocyte ratio (PreNLR), pretreatment platelet-to-lymphocyte ratio (PrePLR), and pretreatment lymphocyte-to-monocyte ratio (PreLMR). Receiver operating characteristic (ROC) curve analysis was used to describe the response prediction accuracy. Logistic regression and Cox regression analyses were implemented to identify independent factors for short-term responses and progression-free survival (PFS). Results: This study included 23 patients. According to ROC curve analysis, there were significant correlations among the SUVmax, SUVmean, and 18F-RGD PET/CT MTV and short-term responses (p<0.05). SUVmax was identified from logistic regression analysis as a significant predictor of treatment sensitivity (p=0.008). Cox multivariate regression analysis suggested that high SUVpeak (p=0.021) and high PreLMR (p=0.03) were independent predictors of PFS. Combining SUVpeak and PreLMR may also increase the prognostic value for PFS, enabling us to identify a subgroup of patients with intermediate PFS. Conclusion: 18F-RGD uptake on PET/CT and serum inflammation biomarker pretreatment may predict outcomes of combined antiangiogenic treatments for advanced NSCLC patients. Higher 18F-RGD uptake and higher PreLMR also appear to predict improved short-term responses and PFS. Combining biomarkers may therefore provide a basis for risk stratification, although further research is required.