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Prostate cancer risk stratification via non-destructive 3D pathology with annotation-free gland segmentation and analysis

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

Posted 02 Sep 2021
medRxiv DOI: 10.1101/2021.08.30.21262847

Prostate cancer treatment planning is largely dependent upon examination of core-needle biopsies. In current clinical practice, the microscopic architecture of the prostate glands is what forms the basis for prognostic grading by pathologists. Interpretation of these convoluted 3D glandular structures via visual inspection of a limited number of 2D histology sections is often unreliable, which contributes to the under- and over-treatment of patients. To improve risk assessment and treatment decisions, we have developed a workflow for non-destructive 3D pathology and computational analysis of whole prostate biopsies labeled with a rapid and inexpensive fluorescent analog of standard H&E staining. Our 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 real immunolabeling. To provide evidence of the translational value of a computational 3D pathology approach, we analyzed ex vivo biopsies (n = 300) extracted from archived radical-prostatectomy specimens (N = 50), and found that 3D glandular features are superior to corresponding 2D features for risk stratification of low- to intermediate-risk PCa patients based on their clinical biochemical recurrence (BCR) outcomes.

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