Background. As artificial intelligence is expanding its applications in medicine, metabolic imaging is gaining the ability to retrieve data otherwise missed by even an experienced naked eye. Also, new radiopharmaceuticals and peptides aim to increase the specificity of positron emission tomography (PET) scans. Herein, a preliminary experience is reported regarding searching for a texture signature in routinely performed [F18]Fluciclovine imaging in prostate cancer. Materials and methods. Twenty-nine patients who underwent a PET/computed tomography (CT) scan with [¹⁸F]Fluciclovine because of biochemical prostate cancer relapse were retrospectively enrolled. First- and second-order radiomic features were manually extracted in lesions visually considered pathologic from the Local Image Features Extraction (LIFEx) platform. Statistical analysis was performed on a database of 29 lesions, one1 per patient. The dataset was split to have 20 lesions for the model training set and 9 lesions for the validation set. The Wilcoxon–Mann–Whitney test was used on the training set to select the most significant features (p-value < 0.05) predicting the dichotomous outcome in a univariate analysis. Results. The best model for predicting the outcome was found to be a multiple logistic linear regression model with two features as variables: an intensity histogram type and a gray-level size zone-based type. Conclusions. Texture analysis of [F18]Fluciclovine PET scans helps in defining prostate cancer relapse in a daily clinical setting.