Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images.

Journal: Diagnostic pathology
Published Date: Jul 28, 2020

Abstract

BACKGROUND: Multiplex immunohistochemistry (mIHC) permits the labeling of six or more distinct cell types within a single histologic tissue section. The classification of each cell type requires detection of the unique colored chromogens localized to cells expressing biomarkers of interest. The most comprehensive and reproducible method to evaluate such slides is to employ digital pathology and image analysis pipelines to whole-slide images (WSIs). Our suite of deep learning tools quantitatively evaluates the expression of six biomarkers in mIHC WSIs. These methods address the current lack of readily available methods to evaluate more than four biomarkers and circumvent the need for specialized instrumentation to spectrally separate different colors. The use case application for our methods is a study that investigates tumor immune interactions in pancreatic ductal adenocarcinoma (PDAC) with a customized mIHC panel.

Authors

  • Danielle J Fassler
    Department of Pathology, Stony Brook University Renaissance School of Medicine, 101 Nicolls Rd, Stony Brook, 11794, USA.
  • Shahira Abousamra
    Department of Computer Science, Stony Brook University, Stony Brook, New York.
  • Rajarsi Gupta
    Department of Biomedical Informatics, Stony Brook Medicine, Stony Brook, NY 11794, USA; Department of Pathology, Stony Brook Medicine, Stony Brook, NY 11794, USA.
  • Chao Chen
    Department of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
  • Maozheng Zhao
    Department of Computer Science, Stony Brook University, 100 Nicolls Rd, Stony Brook, 11794, USA.
  • David Paredes
    Department of Computer Science, Stony Brook University, 100 Nicolls Rd, Stony Brook, 11794, USA.
  • Syeda Areeha Batool
    Department of Biomedical Informatics, Stony Brook University Renaissance School of Medicine, 101 Nicolls Rd, Stony Brook, 11794, USA.
  • Beatrice S Knudsen
    Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Luisa Escobar-Hoyos
    Department of Pathology, Stony Brook University Renaissance School of Medicine, 101 Nicolls Rd, Stony Brook, 11794, USA.
  • Kenneth R Shroyer
    Department of Pathology, Stony Brook Medicine, Stony Brook, NY 11794, USA.
  • Dimitris Samaras
    Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA.
  • Tahsin Kurc
    Department of Biomedical Informatics, Stony Brook Medicine, Stony Brook, NY 11794, USA.
  • Joel Saltz
    Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York.

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