In the context of prostate cancer, combining machine learning with PSMA PET/CT-derived semiquantitative metrics, clinical features, and pathological data could enable the development of high-performance predictive models, allowing for early identification of high-risk patients and supporting more precise stratified management and clinical decision-making. This evidence concerns the gene FOLH1 and prostate carcinoma.