부산대학교 도서관

Introduction: With F-FDG PET/CT, tumor uptake intensity and heterogeneity have been associated with outcome in several cancers. This study aimed at investigating whether F-FDG uptake intensity, volume or heterogeneity could predict the outcome in patients with non-small cell lung cancers (NSCLC) treated by stereotactic body radiation therapy (SBRT). Methods: Sixty-three patients with NSCLC treated by SBRT underwent a F-FDG PET/CT before treatment. Maximum and mean standard uptake value (SUVmax and SUVmean), metabolic tumoral volume (MTV), total lesion glycolysis (TLG), as well as 13 global, local and regional textural features were analysed. The predictive value of these parameters, along with clinical features, was assessed using univariate and multivariate analysis for overall survival (OS), disease-specific survival (DSS) and disease-free survival (DFS). Cutoff values were obtained using logistic regression analysis, and survivals were compared using Kaplan-Meier analysis. Results: The median follow-up period was 27.1 months for the entire cohort and 32.1 months for the surviving patients. At the end of the study, 25 patients had local and/or distant recurrence including 12 who died because of the cancer progression. None of the clinical variables was predictive of the outcome, except age, which was associated with DFS (HR 1.1, P = 0.002). None of the F-FDG PET/CT or clinical parameters, except gender, were associated with OS. The univariate analysis showed that only dissimilarity (D) was associated with DSS (HR = 0.822, P = 0.037), and that several metabolic measurements were associated with DFS. In multivariate analysis, only dissimilarity was significantly associated with DSS (HR = 0.822, P = 0.037) and with DFS (HR = 0.834, P < 0.01). Conclusion: The textural feature dissimilarity measured on the baseline F-FDG PET/CT appears to be a strong independent predictor of the outcome in patients with NSCLC treated by SBRT. This may help selecting patients who may benefit from closer monitoring and therapeutic optimization. [ABSTRACT FROM AUTHOR]