Scalable 3D cell-interaction analysis via supercell graphs for prostate cancer risk stratification
Year: 2026
Type: preprint
Abstract: Cellular interactions underlie fundamental biological processes but are not fully represented in conventional 2D histology images. While 3D pathology allows for more-accurate construction of cell-level graphs, machine-learning models are computationally unwieldy and prone to overfitting, especially when dealing with small cohorts. Here, we introduce SCALE3D , a S uper C ell graph A nalysis framework for L arg E 3D pathology datasets. In SCALE3D, spatially adjacent and morphologically similar cells are grouped into functional “supercells.” Supercell subtypes are defined via morphology-based clustering and 3D graphs connecting these supercells are used to model their interactions. Validation was performed with 76 radical prostatectomy specimens from patients with known 5-year biochemical recurrence (BCR) outcomes. SCALE3D-derived features achieve higher performance for BCR prediction than established 3D nuclear and glandular morphological features. Combining these complementary features further improves prediction performance. Compared to individual cell-level 3D graphs, SCALE3D maintains comparable prognostic performance with improved noise tolerance while reducing computational times by up to 1,000-fold.
Institutions: University of Washington, Stanford Medicine, Stanford University, University of Chicago, Broad Institute, Harvard University, Harvard–MIT Division of Health Sciences and Technology, Dana-Farber Cancer Institute, Mass General Brigham, Massachusetts Institute of Technology, The University of Texas MD Anderson Cancer Center, University of Pennsylvania, Sunnybrook Health Science Centre, University of Toronto, University of California, San Francisco, University of California, Berkeley, Georgia Institute of Technology, Emory University, The Wallace H. Coulter Department of Biomedical Engineering
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: (PQ6) Radiogenomics of colorectal polyps to assess benign proliferative vs. premalignant states., Genetic Determinants of Barrett's Esophagus and Esophageal Adenocarcinoma, Instrumentation platform for 3D pathology with open-top light-sheet microscopy, Prostate cancer risk stratification via computational 3D pathology, Biomarkers for Reducing Mortality of Cancers of the Colon and Esophagus, Esophageal Cancer from Cells to Population: A Multiscale Approach, Computational 3D pathology for Barrett's esophagus risk stratification, The Pacific Northwest Prostate Cancer SPORE, Biomarkers for optimizing risk prediction and early detection of cancers of the colon and esophagus
OpenAlex ID: W7168028495