Prostate cancer treatment decisions rely heavily on subjective visual interpretation [assigning Gleason patterns or International Society of Urological Pathology (ISUP) grade groups] of limited numbers of two‐dimensional (2D) histology sections. Under this paradigm, interobserver variance is high, with ISUP grades not correlating well with outcome for individual patients, and this contributes to the over‐ and undertreatment of patients. Recent studies have demonstrated improved prognostication of prostate cancer outcomes based on computational analyses of glands and nuclei within 2D whole slide images. Our group has also shown that the computational analysis of three‐dimensional (3D) glandular features, extracted from 3D pathology datasets of whole intact biopsies, can allow for improved recurrence prediction compared to corresponding 2D features. Here we seek to expand on these prior studies by exploring the prognostic value of 3D shape‐based nuclear features in prostate cancer (e.g. nuclear size, sphericity). 3D pathology datasets were generated using open‐top light‐sheet (OTLS) microscopy of 102 cancer‐containing biopsies extracted
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.
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.
- Award ID(s):
- 1934292
- NSF-PAR ID:
- 10370177
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- DOI PREFIX: 10.1158
- Date Published:
- Journal Name:
- Cancer Research
- Volume:
- 82
- Issue:
- 2
- ISSN:
- 0008-5472
- Page Range / eLocation ID:
- p. 334-345
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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