American Association for Cancer Research
can-21-2843_supplementary_information_suppsm1.docx (11.16 MB)

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

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journal contribution
posted on 2023-04-12, 17:20 authored by Weisi Xie, Nicholas P. Reder, Can Koyuncu, Patrick Leo, Sarah Hawley, Hongyi Huang, Chenyi Mao, Nadia Postupna, Soyoung Kang, Robert Serafin, Gan Gao, Qinghua Han, Kevin W. Bishop, Lindsey A. Barner, Pingfu Fu, Jonathan L. Wright, C. Dirk Keene, Joshua C. Vaughan, Andrew Janowczyk, Adam K. Glaser, Anant Madabhushi, Lawrence D. True, Jonathan T.C. Liu

Supplementary methods, notes, figures, tables, video captions, and references


U.S. Department of Defense (DOD)

National Cancer Institute (NCI)

United States Department of Health and Human Services

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National Heart, Lung, and Blood Institute (NHLBI)

National Institute of Biomedical Imaging and Bioengineering (NIBIB)

United States Department of Health and Human Services

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National Institute of Mental Health (NIMH)

United States Department of Health and Human Services

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U.S. Department of Veterans Affairs (VA)

National Science Foundation (NSF)

Nancy and Buster Alvord Endowment

Prostate Cancer Foundation (PCF)



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.

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