Machine learning analysis by including the association of radiomics by [68Ga]Ga-PSMA-11 PET/CT, pathomics and genomics in terms of tumor mutational burden (TMB) can predict the Gleason grade in a more accurate way, as compared to the biopsy, thus significantly affecting the risk stratification of prostate cancer patients [28]. This evidence concerns the gene FOLH1 and prostate carcinoma.