Label-Free Leukemia Monitoring by Computer Vision.
Minh DoanMarian CaseDino MasicHolger HennigClaire McQuinJuan CaicedoShantanu SinghAllen GoodmanOlaf WolkenhauerHuw D SummersDavid JamiesonFrederik V DelftAndrew FilbyAnne E CarpenterPaul ReesJulie IrvingPublished in: Cytometry. Part A : the journal of the International Society for Analytical Cytology (2020)
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.
Keyphrases
- deep learning
- single cell
- bone marrow
- machine learning
- label free
- acute lymphoblastic leukemia
- acute myeloid leukemia
- flow cytometry
- prognostic factors
- rna seq
- artificial intelligence
- childhood cancer
- high throughput
- convolutional neural network
- young adults
- mesenchymal stem cells
- induced apoptosis
- stem cells
- randomized controlled trial
- high resolution
- squamous cell carcinoma
- computed tomography
- risk assessment
- locally advanced
- optical coherence tomography
- mass spectrometry
- big data
- human health
- quantum dots
- living cells
- pet imaging
- cell proliferation
- smoking cessation
- climate change