FOLH1 and neoplasm: All these high-risk pathological tumor features were predicted by machine-learning-based analysis with significance (p < 0.01), thus suggesting that PSMA expression is linked to primary cancer histopathology and metastatic tendency and that radiomic analysis could be integrated into clinical practice to select low-risk patients for whom ePLND would be unnecessary.