Purpose: Gastroenteropancreatic neuroendocrine neoplasms (GEP NEN) are widely heterogeneous in their biological behavior, and predicting prognosis and optimal treatment strategies can be challenging. 68Ga-DOTATATE PET/CT is a sensitive imaging modality for well-differentiated NEN and indicates a favorable prognosis, whereas 18F-FDG PET/CT avidity indicates disease that is potentially more aggressive. There has been emerging interest in the combined interpretation of 68Ga-DOTATATE and 18F-FDG PET and its prognostic significance. We aimed to assess the prognostic utility of a classification system that incorporates the complex findings of 68Ga-DOTATATE and 18F-FDG PET interpreted side-by-side in patients with metastatic GEP NEN.
Methods: We defined 3 68Ga-DOTATATE/18F-FDG "dual-tracer PET" groups: D1 (68Ga-DOTATATE positive/18F-FDG negative), D2 (68Ga-DOTATATE positive/18F-FDG positive), and D3 (68Ga-DOTATATE negative/18F-FDG positive). We retrospectively assessed the association between the dual-tracer PET classification and progression-free and overall survival (OS) using Kaplan-Meier analysis. Univariate and multivariate analyses were performed using the Cox proportional hazards model.
Results: Eighty-seven patients with metastatic GEP NEN and contemporaneous 68Ga-DOTATATE and 18F-FDG PET were included. The dual-tracer PET classification was an independent predictor of OS (multivariate P = 0.016) and also predicted progression-free survival (univariate P = 0.030). Other independent predictors of OS included chromogranin A and World Health Organization (WHO) grade. WHO grade was not associated with OS from the time of dual-tracer PET but was an independent predictor of OS from the date of histological diagnosis (multivariate P = 0.003).
Conclusion: Our study demonstrates that a classification system combining the complex findings of 68Ga-DOTATATE and 18F-FDG PET is correlated with prognosis. Further research is needed to prospectively validate these findings and to explore whether dual-tracer PET scores may also be able to predict response to treatment.
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