Predictive value of baseline metabolic tumor burden on (18)F-FDG PET/CT for brain metastases in patients with locally advanced non-small-cell lung cancer

OBJECTIVES: Brain metastases (BMs) are a major cause leading to the failure of treatment management for non-small-cell lung cancer (NSCLC) patients. The purpose of this study was to evaluate the predictive value of baseline metabolic tumor burden on (18)F-FDG PET/CT measured with metabolic tumor volume (MTV) and total lesion glycolysis (TLG) for brain metastases (BMs) development in patients with locally advanced non-small-cell lung cancer (NSCLC) after treatment. METHODS: Forty-seven patients with stage IIB-IIIC NSCLC who underwent baseline (18)F-FDG PET/CT examinations were retrospectively reviewed. The maximum standardized uptake value (SUV(max)), MTV, and TLG of the primary tumor (SUV(maxT), MTV(T), and TLG(T)), metastatic lymph nodes (SUV(maxN), MTV(N), and TLG(N)), and whole-body tumors (SUV(maxWB), MTV(WB), and TLG(WB)) were measured. The optimal cut-off values of PET parameters to predict brain metastasis-free survival were obtained using Receiver operating characteristic (ROC) analysis, and the predictive value of clinical variables and PET parameters were evaluated using Cox proportional hazards regression analysis. RESULTS: The median follow-up duration was 25.0 months for surviving patients, and 13 patients (27.7%) developed BM. The optimal cut-off values were 21.1 mL and 150.0 g for MTV(T) and TLG(T), 20.0, 10.9 mL and 55.6 g for SUV(maxN), MTV(N) and TLG(N), and 27.9, 27.4 mL and 161.0 g for SUV(maxWB), MTV(WB) and TLG(WB), respectively. In the Cox proportional hazards models, the risk of BM was significantly associated with MTV(N) and MTV(WB) or TLG(N) and TLG(WB) after adjusting for histological cell type, N stage, SUV(maxN), and SUV(maxWB). CONCLUSIONS: Baseline metabolic tumor burden (MTV and TLG) evaluated from the level of metastatic lymph nodes and whole-body tumors are significant predictive factors for BM development in patients with locally advanced NSCLC.