Aim: We studied whether the characterization of tumor texture in FDopa-PET/CT could assist in the identification of tumor grades in both primitive and recurrent gliomas. Materials and Methods: Eighty one patients with gliomas were studied, including 52 newly diagnosed tumors and 29 recurrent tumors. For each tumor, the SUVpeak and metabolic volume (MV) were measured, as well as 32 textural indices (TI). The ability of SUVpeak, MV and TI was investigated by using each index alone first (with ROC analyses), and then by using couples consisting of one TI with SUVpeak in a binomial model (with ROC analyses and a reclassification method). The pathological examination was assumed to provide the gold standard grade. Results: Neither SUVpeak nor MV could discriminate low-grade tumors (LG) from high-grade tumors (HG) in newly-diagnosed tumors, while SUVpeak alone could discriminate LG from HG in recurrent tumors (p=0.02). Combining a TI with SUVpeak led to a significant LG / HG discrimination for newly-diagnosed tumors (p = 0.01). Among all TI, entropy led to the best reclassification performance. Conclusion: The co-analysis of FDopa-PET/CT SUVpeak and well-selected TI (such as entropy) made it possible to improve the classification of newly-diagnosed gliomas.