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Pathologists diagnose and grade prostate cancer using thin two-dimensional (2D) histological sections, but these 3-5 micron sections are too thin to visualize complete glandular networks and three-dimensional (3D) spatial relationships of adenocarcinomas. We hypothesized that understanding volumetric glandular organization would reveal architectural features associated with prostate cancer progression and biochemical recurrence (BCR). We analyzed two archived prostatectomy cohorts using different sampling methods: simulated 1mm core needle biopsies from University of Washington and 3×1mm punch biopsies from University of Pennsylvania. We used Open-Top Light-Sheet microscopy to visualize intact tissue networks and developed GlaSkeN, a computational pathology framework to quantify 3D prostatic gland architecture. GlaSkeN used deep learning to segment glandular structures from 3D images, then constructed skeleton-based representations to extract volumetric features including branch length, branching angles, torsion, and curvature. We analyzed associations between architectural features and 5-year BCR-free survival using 6-fold cross-validated Cox regression. GlaSkeN identified 3D architectural features significantly associated with BCR in both cohorts: UW (HR=5.18, 95% CI: 1.18-22.68, C-index=0.68, p=0.019) and UPenn (HR=2.04, 95% CI: 1.14-3.65, C-index=0.62, p<0.05). In multivariable analysis, GlaSkeN remained prognostic after controlling for clinicopathological variables (HR=2.30, 95% CI: 1.13-4.7, p=0.021). Limitations include different sampling methods between cohorts and limited sample sizes. This 3D analysis captured glandular organization, spatial connectivity, and branching patterns unassessable in 2D cross-sections. GlaSkeN identified glandular architecture features associated with BCR independent of standard clinical variables, suggesting 3D architecture could provide additional prognostic information to complement current histopathological grading. Validation in larger independent cohorts is warranted.