ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2020
Label-Free Leukemia Monitoring by Computer Vision.
Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 3 syntheses or guidelines pooled it, 57 citations in OpenAlex.
- Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary.Haematologica · 2023Pooled it
- Artificial intelligence applied to musculoskeletal oncology: a systematic review.Skeletal radiology · 2022Pooled it
- Applications of Artificial Intelligence in Pediatric Oncology: A Systematic Review.JCO clinical cancer informatics · 2021Pooled it
- Evaluating Isochoric Freezing as a Strategy for Storage of Red Blood Cells.Biotechnology and bioengineering · 2026Article
- Multi-contrast machine learning improves schistosomiasis diagnostic performance.PLoS neglected tropical diseases · 2025Article
- Cold storage surpasses the impact of biological age and donor characteristics on red blood cell morphology classified by deep machine learning.Scientific reports · 2025Article
- Imaging flow cytometry as a novel approach for the diagnosis of heparin-induced thrombocytopenia.British journal of haematology · 2025Article
- Blood cancer prediction model based on deep learning technique.Scientific reports · 2025Article
- Review
- Capturing cell heterogeneity in representations of cell populations for image-based profiling using contrastive learning.PLoS computational biology · 2024Article
- Capturing cell heterogeneity in representations of cell populations for image-based profiling using contrastive learning.bioRxiv : the preprint server for biology · 2024Article
- Imaging Flow Cytometry and Convolutional Neural Network-Based Classification Enable Discrimination of Hematopoietic and Leukemic Stem Cells in Acute Myeloid Leukemia.International journal of molecular sciences · 2024Article
- Comparative analysis of feature-based ML and CNN for binucleated erythroblast quantification in myelodysplastic syndrome patients using imaging flow cytometry data.Scientific reports · 2024Article
- PXPermute reveals staining importance in multichannel imaging flow cytometry.Cell reports methods · 2024Article
- Assessment of stored red blood cells through lab-on-a-chip technologies for precision transfusion medicine.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Leveraging machine learning to identify acute myeloid leukemia patients and their chemotherapy regimens in an administrative database.Pediatric blood & cancer · 2023Article
- Detection of acute promyelocytic leukemia in peripheral blood and bone marrow with annotation-free deep learning.Scientific reports · 2023Article
- Artificial intelligence in imaging flow cytometry.Frontiers in bioinformatics · 2023Article
- A New Image for Cell Sorting.The New England journal of medicine · 2022Article
- A weakly supervised deep learning approach for label-free imaging flow-cytometry-based blood diagnostics.Cell reports methods · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 4 institutions in 3 countries.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.