Dataset Information

Label-Free Leukemia Monitoring by Computer Vision.

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. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

SUBMITTER: Doan M

PROVIDER: S-EPMC7213640 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Label-Free Leukemia Monitoring by Computer Vision.

Doan Minh M Case Marian M Masic Dino D Hennig Holger H McQuin Claire C Caicedo Juan J Singh Shantanu S Goodman Allen A Wolkenhauer Olaf O Summers Huw D HD Jamieson David D Delft Frederik V FV Filby Andrew A Carpenter Anne E AE Rees Paul P Irving Julie J

Cytometry. Part A : the journal of the International Society for Analytical Cytology 20200224 4

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 sam ...[more]

PMID: 32091180

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Dataset Information

Label-Free Leukemia Monitoring by Computer Vision.

Publications

Label-Free Leukemia Monitoring by Computer Vision.

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