In this study, we propose a multimodal data fusion strategy that integrates clinicopathological features (e.g., age, Gleason score) and PSMA PET/CT semi-quantitative metrics (e.g., SUVmax,PSMA-TVp) to construct a machine learning-based predictive model for assessing the risk of metastasis in prostate cancer patients. This evidence concerns the gene FOLH1 and prostate cancer.