Related Experiment Video
Updated: May 28, 2026
Enhancing Prostate Tumor Biobanking Reliability with Improved Sampling Technique and Histological Characterization
Published on: November 17, 2023
Artificial Intelligence-informed Architectural Insights of 3-dimensional Glandular Networks Identify Patients With
Jennifer Salguero-Lopez1, Sebastian Medina2, Robert Serafin3
1Wallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia; Department of Electrical and Electronic Engineering, Universidad Nacional de Colombia, Bogotá, Colombia.
None:
Pathologists diagnose and grade prostate cancer using thin 2-dimensional (2D) histologic sections, but these 3 to 5 micron sections are too thin to visualize complete glandular networks and 3-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 2 archived prostatectomy cohorts using different sampling methods: simulated 1 mm core-needle biopsies from the University of Washington and 3 × 1 mm-punch biopsies from the 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 and 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: the University of Washington (hazard rati [HR], 5.18; 95% CI, 1.18-22.68; C-index = 0.68; P = .019) and the University of Pennsylvania (HR, 2.04; 95% CI, 1.14-3.65; C-index = 0.62; P < .05). In multivariable analysis, GlaSkeN remained prognostic after controlling for clinicopathological variables (HR, 2.30; 95% CI, 1.13-4.7; P = .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.
