De Novo Identification and Visualization of Important Cell Populations for Classic Hodgkin Lymphoma Using Flow Cytometry and Machine Learning.

Journal: American journal of clinical pathology
PMID: 34175918

Abstract

OBJECTIVES: Automated classification of flow cytometry data has the potential to reduce errors and accelerate flow cytometry interpretation. We desired a machine learning approach that is accurate, is intuitively easy to understand, and highlights the cells that are most important in the algorithm's prediction for a given case.

Authors

  • Paul D Simonson
    Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA.
  • Yue Wu
    Key Laboratory of Luminescence and Real-Time Analytical Chemistry (Ministry of Education), College of Pharmaceutical Sciences, Southwest University, Chongqing 400716, China.
  • David Wu
    School of Nursing & Health Professions, Georgia State University, Atlanta, GA.
  • Jonathan R Fromm
    Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA.
  • Aaron Y Lee
    Department of Ophthalmology, University of Washington, Seattle, Washington.

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