Abstract 2436: Prostate cancer risk stratification based on 3D histomorphometric features related to perineural invasion (PNI)
Authors: Sarah S. Chow, Rui Wang, Yujie Zhao, Robert Serafin, Elena Baraznenok, Lydia Lan, Xavier Farre, Kevin Bishop, Gan Gao, Lawrence D. True, Anant Madabhushi, Jonathan T. Liu
Journal: Cancer Research (2025)

Prostate cancer (PCa) treatment decisions rely heavily on the examination of 2D histology sections (i.e. Gleason grading). However, the limited sampling of specimens afforded by 2D histopathology, and the ambiguities of viewing cross-sections of complex structures such as prostate glands, can cause high interpathologist variability and nonoptimal treatment decisions, especially for low- to intermediate-grade PCa. Our lab has previously shown that computational analysis of 3D histomorphometric features, such as those derived from gland and nuclear segmentation, can improve PCa risk assessment compared to analogous 2D features. Here, we expand on these findings by exploring the prognostic value of 3D features from nerves. These structures are critical as prostate cancer cells follow them to migrate and metastasize, i.e. perineural invasion (PNI), which is correlated with worse patient outcomes. We therefore aim to show the analysis of 3D features related to PNI can provide additional prognostic value. To analyze 3D nerve structures, we utilized a 3D deep learning-based segmentation model, nnU-Net, trained on 3D data from punch biopsies extracted from archived formalin-fixed paraffin-embedded (FFPE) prostatectomy specimens and imaged with a 4th-generation open-top light-sheet (OTLS) microscope. To train a segmentation model without requiring tedious manual annotations, prostate specimens were triple labeled with a fluorescent analog of H&amp;E (nuclear and cytoplasmic stains) plus an antibody targeting PGP9.5, which labels nerves. The H&amp;E-analog channels serve as inputs for the nnU-Net model. To train the model, ground-truth 3D segmentation masks were generated from the PGP9.5 immunofluorescence channel. The trained model allows us to generate 3D segmentation masks of nerves directly from specimens labeled with small-molecule (quickly diffusing) fluorescent analog of H&amp;E, which is fast and inexpensive compared with thick-tissue immunolabeling. The trained 3D nerve segmentation model achieved an average Dice score of 0.86 on held-out validation datasets of tri-labeled specimens (n=10). The segmentation performance was also evaluated on 2D regions (n=53) extracted from 3D pathology datasets of 8 prostatectomy specimens, yielding an average Dice score of 0.64 compared against annotations of nerves generated by a board-certified pathologist. The trained model has been applied to 3D pathology datasets of 120 archived prostatectomy specimens from patients with known biochemical recurrence (BCR) outcomes. We are extracting 3D histomorphometric features (i.e. spatial features) from the resulting nerve segmentations and are integrating them with 3D features from previously developed segmentations of glands, nuclei, and/or cancer-enriched tissue regions. We aim to show that 3D features related to PNI are superior to analogous 2D features for PCs prognostication. Citation Format: Sarah S. Chow, Rui Wang, Yujie Zhao, Robert Serafin, Elena Baraznenok, Lydia Lan, Xavier Farre, Kevin Bishop, Gan Gao, Lawrence D. True, Anant Madabhushi, Jonathan T. Liu. Prostate cancer risk stratification based on 3D histomorphometric features related to perineural invasion (PNI) [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 2436.