Abstract 2435: Automatic detection of prostate cancer in 3D pathology datasets based on synthetic immunolabeling of cytokeratins

Abstract For AI-analysis of 3D pathology datasets, delineating benign and cancerous tissue regions is often a critical first step. For prostate cancer (PCa) this can be challenging due to the complex intermixing of benign and cancerous glands in 3D. Automated detection of cancerous glands could improve the efficiency and accuracy of downstream computational tasks, such as risk stratification using 3D histomorphometric features. For example, we previously developed machine classifiers based on 3D glandular and nuclear morphologies in PCa biopsies but relied on pathologists to manually identify cancer-enriched volumes for analysis, which was tedious and could introduce bias. Here, we present an approach to automatically identify cancer-enriched regions in 3D pathology datasets of prostate tissue. The tissues are labeled with inexpensive small-molecule fluorescence analogs of H&E staining and are imaged with open-top light-sheet (OTLS) microscopy. Generative adversarial networks were used to train separate models to convert our H&E-analog datasets into synthetic 3D immunofluorescence datasets of two distinct biomarkers: a low-molecular weight cytokeratin (CK8) expressed by luminal epithelial cells found in all prostate glands, and a high-molecular weight cytokeratin (CK5) expressed by basal epithelial cells that are only found in benign prostate glands. Each 3D image-translation model was trained on OTLS microscopy images of prostate tissues that were tri-labeled with fluorescent analogs of H&E plus the antibody of interest (i.e. CK5 or CK8). The models achieved high accuracy for synthetic immunolabeling in held-out validation datasets (Dice scores of 0.72 and 0.83 respectively). By training these models to predict the expression of each CK target directly from H&E-analog datasets, we can avoid the time and cost of immunolabeling large intact tissues. The 3D image-translation models were applied to 3D pathology datasets of 3-mm diameter punch biopsies extracted from archived prostatectomies from 100 PCa patients with known biochemical recurrence outcomes. Using the predicted expression of CK5 and CK8, we developed a simple algorithm to generate a spatial heatmap of cancer-enriched regions throughout each biopsy. For validation, we compared these 3D heatmaps against annotations from 3 GU pathologists of 40 regions of interest, yielding an average Dice score of 0.82. We plan to show improved prognostication with classifiers trained on 3D histomorphometric features derived from cancer-enriched regions (identified by our method) vs. all regions of a prostate specimen. Citation Format: Robert B. Serafin, Jennifer Salguero-Lopez, Rui Wang, Sarah Chow, Elena Baraznenok, Lydia Lan, Kevin W. Bishop, Michelle Downes, Xavier Farre, Lawrence D. True, Anant Madabhushi, Jonathan T. Liu. Automatic detection of prostate cancer in 3D pathology datasets based on synthetic immunolabeling of cytokeratins [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2435.

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