Prostate Cancer Risk Stratification via Nondestructive 3D Pathology with Deep Learning–Assisted Gland Analysis

TLDR

Prostate cancer treatment planning depends on core‑needle biopsies, yet grading relies on microscopic gland architecture that is difficult to interpret accurately from limited 2D sections, leading to under‑ or overtreatment. The authors aim to improve risk assessment and treatment decisions by developing a nondestructive 3D pathology workflow and computational analysis of whole prostate biopsies using a rapid fluorescent analogue of H&E staining. This workflow employs interpretable glandular features and an image‑translation‑assisted segmentation in 3D (ITAS3D), a deep‑learning strategy that volumetrically segments tissue microstructures without annotation or immunolabeling, and was applied to 300 ex vivo biopsies from 50 radical prostatectomy specimens, 118 of which contained cancer. The study shows that 3D glandular features outperform 2D features for risk stratification of low‑to‑intermediate‑risk prostate cancer, supporting computational 3D pathology for guiding clinical management and indicating its potential for superior prognostic stratification.

Abstract

Prostate cancer treatment planning is largely dependent upon examination of core-needle biopsies. The microscopic architecture of the prostate glands forms the basis for prognostic grading by pathologists. Interpretation of these convoluted three-dimensional (3D) glandular structures via visual inspection of a limited number of two-dimensional (2D) histology sections is often unreliable, which contributes to the under- and overtreatment of patients. To improve risk assessment and treatment decisions, we have developed a workflow for nondestructive 3D pathology and computational analysis of whole prostate biopsies labeled with a rapid and inexpensive fluorescent analogue of standard hematoxylin and eosin (H&E) staining. This analysis is based on interpretable glandular features and is facilitated by the development of image translation-assisted segmentation in 3D (ITAS3D). ITAS3D is a generalizable deep learning-based strategy that enables tissue microstructures to be volumetrically segmented in an annotation-free and objective (biomarker-based) manner without requiring immunolabeling. As a preliminary demonstration of the translational value of a computational 3D versus a computational 2D pathology approach, we imaged 300 ex vivo biopsies extracted from 50 archived radical prostatectomy specimens, of which, 118 biopsies contained cancer. The 3D glandular features in cancer biopsies were superior to corresponding 2D features for risk stratification of patients with low- to intermediate-risk prostate cancer based on their clinical biochemical recurrence outcomes. The results of this study support the use of computational 3D pathology for guiding the clinical management of prostate cancer. SIGNIFICANCE: An end-to-end pipeline for deep learning-assisted computational 3D histology analysis of whole prostate biopsies shows that nondestructive 3D pathology has the potential to enable superior prognostic stratification of patients with prostate cancer.