Translating prognostic quantification of c-MYC and BCL2 from tissue microarrays to whole slide images in diffuse large B-cell lymphoma using deep learning.

Journal: Diagnostic pathology
PMID: 38243330

Abstract

BACKGROUND: c-MYC and BCL2 positivity are important prognostic factors for diffuse large B-cell lymphoma. However, manual quantification is subject to significant intra- and inter-observer variability. We developed an automated method for quantification in whole-slide images of tissue sections where manual quantification requires evaluating large areas of tissue with possibly heterogeneous staining. We train this method using annotations of tumor positivity in smaller tissue microarray cores where expression and staining are more homogeneous and then translate this model to whole-slide images.

Authors

  • Thomas E Tavolara
    Center for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem, USA.
  • M Khalid Khan Niazi
    Center for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem, NC, United States of America.
  • Andrew L Feldman
    Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
  • David L Jaye
    Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA. dljaye@emory.edu.
  • Christopher Flowers
    Department of Lymphoma/Myeloma, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Lee A D Cooper
    Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, 30322, USA. lee.cooper@emory.edu.
  • Metin N Gurcan
    Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA. Electronic address: metin.gurcan@osumc.edu.

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