Histopathologic and Machine Deep Learning Criteria to Predict Lymphoma Transformation in Bone Marrow Biopsies.

Journal: Archives of pathology & laboratory medicine
PMID: 34086849

Abstract

CONTEXT.—: Large cell transformation (LCT) of indolent B-cell lymphomas, such as follicular lymphoma (FL) and chronic lymphocytic leukemia (CLL), signals a worse prognosis, at which point aggressive chemotherapy is initiated. Although LCT is relatively straightforward to diagnose in lymph nodes, a marrow biopsy is often obtained first given its ease of procedure, low cost, and low morbidity. However, consensus criteria for LCT in bone marrow have not been established.

Authors

  • Lina Irshaid
    From the Departments of Pathology (Irshaid, Garritano, Patsenker, Kluger, Katz, Xu).
  • Jonathan Bleiberg
    the Program of Applied Mathematics, Yale University, New Haven, Connecticut (Bleiberg, Weinberger, Lindenbaum, Kluger).
  • Ethan Weinberger
    Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
  • James Garritano
    From the Departments of Pathology (Irshaid, Garritano, Patsenker, Kluger, Katz, Xu).
  • Rory M Shallis
    and Internal Medicine (Shallis), Yale New Haven Hospital, Yale School of Medicine, New Haven, Connecticut.
  • Jonathan Patsenker
    From the Departments of Pathology (Irshaid, Garritano, Patsenker, Kluger, Katz, Xu).
  • Ofir Lindenbaum
    the Program of Applied Mathematics, Yale University, New Haven, Connecticut (Bleiberg, Weinberger, Lindenbaum, Kluger).
  • Yuval Kluger
    Department of Pathology, Yale School of Medicine, New Haven, CT 06510, USA.
  • Samuel G Katz
    From the Departments of Pathology (Irshaid, Garritano, Patsenker, Kluger, Katz, Xu).
  • Mina L Xu
    From the Departments of Pathology (Irshaid, Garritano, Patsenker, Kluger, Katz, Xu).

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