Prostate cancer (PCa) is the most diagnosed cancer in men, accounting for 27% of male new cancer diagnoses in 2022. If organ-confined, removal of the prostate through radical prostatectomy is considered curative; however, distant metastases may form resulting in poor patient prognosis. This study sought to determine whether quantitative pathomic features of prostate cancer differ in patients who biochemically recur following surgery. Whole mount prostate histology from 78 patients was analyzed for this study. In total, 614 slides were hematoxylin and eosin (H&E) stained and digitized to produce whole slide images (WSI). Regions of differing Gleason patterns were digitally annotated by a GU-fellowship trained pathologist (KAI), and high-resolution tiles were extracted from each annotated region of interest (ROI) for further analysis. Individual glands within the prostate were identified using automated image processing algorithms, and histomorphometric features were calculated on a per-tile basis as well as across WSI and averaged by patient. Tiles were organized into cancer and benign tissue. Logistic regression models were fit to assess the predictive value of the calculated pathomic features across tile groups and WSI, as well as models using clinical information for comparison. Logistic regression classified each pathomic feature model at accuracies >80% with areas under the curve (AUC) of 0.82, 0.76, 0.75, and 0.72 for all tiles, cancer only, noncancer only, and across WSI. This was comparable to standard clinical information, Gleason Grade Groups, and CAPRA score, which achieved similar accuracies but AUCs of 0.80, 0.77, and 0.70, respectively. This study demonstrates that the use of quantitative pathomic features calculated from digital histology of prostate cancer may provide clinicians with additional information beyond the traditional qualitative pathologist assessment. Further research is warranted to determine possible inclusion in treatment guidance.