Analysis of 3D pathology samples using weakly supervised AI.

Journal: Cell
Published Date: May 9, 2024

Abstract

Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by complex manual evaluation and lack of computational platforms to distill clinical insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing tissue volumes and efficiently predicting clinical outcomes based on 3D morphological features. Recurrence risk-stratification models were trained on prostate cancer specimens imaged with open-top light-sheet microscopy or microcomputed tomography. By comprehensively capturing 3D morphologies, 3D volume-based prognostication achieves superior performance to traditional 2D slice-based approaches, including clinical/histopathological baselines from six certified genitourinary pathologists. Incorporating greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, further emphasizing the value of capturing larger extents of heterogeneous morphology.

Authors

  • Andrew H Song
    Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
  • Mane Williams
    Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Drew F K Williamson
    Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Sarah S L Chow
    Department of Mechanical Engineering, Bioengineering, and Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA.
  • Guillaume Jaume
    IBM Zurich Research Lab, Zurich, Switzerland; Signal Processing Laboratory 5, EPFL, Lausanne, Switzerland.
  • Gan Gao
    Department of Mechanical Engineering, University of Washington, Seattle, Washington.
  • Andrew Zhang
    Amazon Web Service, 450 West 33rd Street, New York, NY 10001, USA.
  • Bowen Chen
    Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Alexander S Baras
    Bloomberg Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Robert Serafin
    Department of Mechanical Engineering, University of Washington, Seattle, Washington.
  • Richard Colling
    Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Michelle R Downes
    Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada.
  • Xavier Farré
    Department of Health, Public Health Agency of Catalonia, Lleida, Catalonia, Spain.
  • Peter Humphrey
    Department of Urology, Yale School of Medicine, New Haven, CT, USA.
  • Clare Verrill
    Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK; Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK. Electronic address: Clare.Verrill@ouh.nhs.uk.
  • Lawrence D True
    Department of Laboratory Medicine & Pathology, University of Washington, Seattle, Washington.
  • Anil V Parwani
    Department of Pathology, The Ohio State University Wexner Medical Centre, Columbus, OH, USA.
  • Jonathan T C Liu
    Department of Mechanical Engineering, University of Washington, Seattle, Washington. jonliu@uw.edu.
  • Faisal Mahmood
    Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. faisalmahmood@bwh.harvard.edu.

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