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

Journal: Cancer research
Published Date: Dec 1, 2021

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 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.

Authors

  • Weisi Xie
    Department of Mechanical Engineering, University of Washington, Seattle, Washington.
  • Nicholas P Reder
    Department of Mechanical Engineering, University of Washington, Seattle, Washington.
  • Can Koyuncu
    Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio.
  • Patrick Leo
    Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio.
  • Sarah Hawley
    Canary Foundation, Palo Alto, California.
  • Hongyi Huang
    Department of Mechanical Engineering, University of Washington, Seattle, Washington.
  • Chenyi Mao
    Department of Chemistry, University of Washington, Seattle, Washington.
  • Nadia Postupna
    Department of Laboratory Medicine & Pathology, University of Washington, Seattle, Washington.
  • Soyoung Kang
    Department of Mechanical Engineering, University of Washington, Seattle, Washington.
  • Robert Serafin
    Department of Mechanical Engineering, University of Washington, Seattle, Washington.
  • Gan Gao
    Department of Mechanical Engineering, University of Washington, Seattle, Washington.
  • Qinghua Han
    Department of Bioengineering, University of Washington, Seattle, Washington.
  • Kevin W Bishop
    Department of Mechanical Engineering, University of Washington, Seattle, Washington.
  • Lindsey A Barner
    Department of Mechanical Engineering, University of Washington, Seattle, Washington.
  • Pingfu Fu
    Department of Biomedical Engineering, Case Western Reserve University School of Engineering, 2071 Martin Luther King Dr, Cleveland, OH 44106-7207 (M. Khorrami, K.B., A.M.); Departments of Internal Medicine (M. Khunger) and Solid Tumor Oncology (A.Z., P.P.), Cleveland Clinic, Cleveland, Ohio; Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.); Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio (P.F.); Department of Hematology and Oncology, New York University, New York, NY (V.V.); Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A.M.).
  • Jonathan L Wright
    Department of Urology, University of Washington, Seattle, Washington.
  • C Dirk Keene
    Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA.
  • Joshua C Vaughan
    Department of Chemistry, University of Washington, Seattle, Washington.
  • Andrew Janowczyk
    Biomedical Engineering Department, Case Western Reserve University, Cleveland, Ohio.
  • Adam K Glaser
    Department of Mechanical Engineering, University of Washington, Seattle, Washington.
  • Anant Madabhushi
    Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio.
  • Lawrence D True
    Department of Laboratory Medicine & Pathology, University of Washington, Seattle, Washington.
  • Jonathan T C Liu
    Department of Mechanical Engineering, University of Washington, Seattle, Washington. jonliu@uw.edu.

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