Label-Free Leukemia Monitoring by Computer Vision

Minh Doan, Marian Case, Dino Masic, Holger Hennig, Claire McQuin, Juan Caicedo, Shantanu Singh, Allen Goodman, Olaf Wolkenhauer, Huw D. Summers, David Jamieson, Frederik V. Delft, Andrew Filby, Anne E. Carpenter, Paul Rees, Julie Irving

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. While there are a number of well-recognized prognostic biomarkers at diagnosis, the most powerful independent prognostic factor is the response of the leukemia to induction chemotherapy (Campana and Pui: Blood 129 (2017) 1913–1918). Given the potential for machine learning to improve precision medicine, we tested its capacity to monitor disease in children undergoing ALL treatment. Diagnostic and on-treatment bone marrow samples were labeled with an ALL-discriminating antibody combination and analyzed by imaging flow cytometry. Ignoring the fluorescent markers and using only features extracted from bright-field and dark-field cell images, a deep learning model was able to identify ALL cells at an accuracy of >88%. This antibody-free, single cell method is cheap, quick, and could be adapted to a simple, laser-free cytometer to allow automated, point-of-care testing to detect slow early responders. Adaptation to other types of leukemia is feasible, which would revolutionize residual disease monitoring.

Original languageEnglish (US)
Pages (from-to)407-414
Number of pages8
JournalCytometry Part A
Volume97
Issue number4
DOIs
StatePublished - Apr 1 2020

Keywords

  • computer vision
  • deep learning
  • imaging flow cytometry
  • label-free
  • leukemia
  • machine learning
  • neural networks

ASJC Scopus subject areas

  • Pathology and Forensic Medicine
  • Histology
  • Cell Biology

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