Detection of Prostate Cancer in 3-Dimensional Pathology Datasets via Generative Immunolabeling

Robert B Serafin; Jennifer Salguero Lopez; Suet Chow; Rui Wang; Yujie Zhao; Elena Baraznenok; Lydia Lan; Kevin Bishop; Michelle Downes; Xavier Farre; Lawrence D True; Priti Lal; Anant Madabhushi; Jonathan T C Liu

doi:10.1016/j.modpat.2026.100975

Detection of Prostate Cancer in 3-Dimensional Pathology Datasets via Generative Immunolabeling

Mod Pathol. 2026 Apr;39(4):100975. doi: 10.1016/j.modpat.2026.100975. Epub 2026 Feb 13.

Authors

Robert B Serafin¹, Jennifer Salguero Lopez², Suet Chow³, Rui Wang³, Yujie Zhao⁴, Elena Baraznenok⁵, Lydia Lan⁵, Kevin Bishop⁶, Michelle Downes⁷, Xavier Farre⁸, Lawrence D True⁹, Priti Lal¹⁰, Anant Madabhushi¹¹, Jonathan T C Liu¹²

Affiliations

¹ Department of Mechanical Engineering, University of Washington, Seattle, Washington; Section of Hematology and Oncology, Department of Medicine, University of Chicago, Chicago, Illinois. Electronic address: robserafin@uchicago.edu.
² Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia; Universidad Nacional de Colombia, Bogota, Colombia.
³ Department of Mechanical Engineering, University of Washington, Seattle, Washington; Department of Pathology, Stanford University School of Medicine, Stanford, California.
⁴ Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington.
⁵ Department of Bioengineering, University of Washington, Seattle, Washington.
⁶ Department of Bioengineering, University of Washington, Seattle, Washington; Broad Institute of MIT and Harvard, Cambridge, Massachusetts.
⁷ Department of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, Ontario, Canada.
⁸ Public Health Agency of Catalonia, Lleida, Spain.
⁹ Department of Laboratory Medicine & Pathology, University of Washington School of Medicine, Seattle, Washington.
¹⁰ Department of Laboratory Medicine & Pathology, University of Pennsylvania, Philadelphia, Pennsylvania.
¹¹ Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia; Atlanta Veterans Affairs Medical Center, Decatur, Georgia.
¹² Department of Mechanical Engineering, University of Washington, Seattle, Washington; Department of Pathology, Stanford University School of Medicine, Stanford, California; Department of Bioengineering, University of Washington, Seattle, Washington; Department of Laboratory Medicine & Pathology, University of Washington School of Medicine, Seattle, Washington.

PMID: 41692323
PMCID: PMC13185537 (available on 2027-02-13)
DOI: 10.1016/j.modpat.2026.100975

Abstract

Recent advancements in nondestructive 3-dimensional (3D) pathology offer a complement to standard histology by enabling comprehensive volumetric analyses of intact clinical specimens (eg, biopsies). Previous studies have demonstrated the added prognostic value of 3D pathology for prostate cancer (PCa) risk stratification by correlating 3D microarchitectural features with long-term patient outcomes. However, these analyses relied on coarse manual annotations of cancer-enriched regions for downstream analysis without fine-grained delineation between often-intermixed cancerous and benign glands. To automate and improve the process of delineating between benign and PCa-enriched regions in 3D pathology data sets, we have developed a 3D computational pipeline: Synthetic Immunolabeling for Generative Heatmaps of Tumor (SIGHT). SIGHT relies on deep learning-based 3D image translation models, trained in a fully supervised manner, to convert hematoxylin and eosin analog 3D pathology data sets into multiplexed 3D immunofluorescence data sets that facilitate tumor detection. Our implementation of SIGHT synthetically labels 2 cytokeratin markers that are differentially expressed in cancerous and benign prostate glands, which are used to generate explainable 3D heatmaps of cancer-enriched regions in prostate tissues. Validation of SIGHT against ground truth annotations from a panel of genitourinary pathologists (X.F., M.D., L.D.T.) yields an average F1 score of 0.88, which is comparable with the average interpathologist agreement F1 score of 0.90. To demonstrate how SIGHT can automate and improve a pipeline for risk stratification based on 3D pathology data sets of PCas, we developed preliminary machine classifiers of recurrence risk based on 3D glandular histomorphometric features from 75 patients. Volumetric glandular analysis in SIGHT-identified cancer-enriched regions versus all tissue regions yields a Kaplan-Meier hazard ratio of 3.57 (CI, 1.6-7.9) versus 0.92 (CI, 0.45-1.89).

Keywords: 3-dimensional pathology; computational pathology; generative immunolabeling; light-sheet microscopy; prostate cancer.

MeSH terms

Biomarkers, Tumor / analysis
Deep Learning*
Generative Artificial Intelligence
Humans
Image Interpretation, Computer-Assisted* / methods
Imaging, Three-Dimensional* / methods
Immunohistochemistry
Male
Prostatic Neoplasms* / pathology

Substances

Biomarkers, Tumor

Abstract

MeSH terms

Substances

Grants and funding