Exploring dyserythropoiesis in patients with myelodysplastic syndrome by imaging flow cytometry and machine‐learning assisted morphometrics-Reference-Cited by-同舟云学术

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Exploring dyserythropoiesis in patients with myelodysplastic syndrome by imaging flow cytometry and machine‐learning assisted morphometrics

Published:2020-12-07 Issue:5 Volume:100 Page:554-567
ISSN:1552-4949
Container-title:Cytometry Part B: Clinical Cytometry
language:en
Short-container-title:Cytometry

Author:

Rosenberg Carina A.¹^ORCID,Bill Marie¹,Rodrigues Matthew A.²,Hauerslev Mathias¹,Kerndrup Gitte B.³,Hokland Peter⁴,Ludvigsen Maja¹⁴

Affiliation:

1. Department of Hematology Aarhus University Hospital Aarhus Denmark

2. Amnis Flow Cytometry, Luminex Corporation Seattle Washington USA

3. Department of Pathology Aarhus University Hospital Aarhus Denmark

4. Department of Clinical Medicine Aarhus University Aarhus Denmark

Funder

Kræftens Bekæmpelse

Publisher

Wiley

Subject

Cell Biology,Histology,Pathology and Forensic Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cyto.b.21975

Reference56 articles.

1. Prediction of acute myeloid leukaemia risk in healthy individuals

2. Severely impaired terminal erythroid differentiation as an independent prognostic marker in myelodysplastic syndromes

3. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia

4. The bone marrow aspirate of healthy subjects

5. Diagnostic criteria to distinguish hypocellular acute myeloid leukemia from hypocellular myelodysplastic syndromes and aplastic anemia: recommendations for a standardized approach

Cited by 14 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Recent advancements in machine learning for bone marrow cell morphology analysis;Frontiers in Medicine;2024-06-14

2. 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;2024-06-12

3. Comparative analysis of feature-based ML and CNN for binucleated erythroblast quantification in myelodysplastic syndrome patients using imaging flow cytometry data;Scientific Reports;2024-04-23

4. Side scatter ratio of the CD105-positive and CD105-negative red blood cell fractions is useful for the detection of low-grade myelodysplastic neoplasms by flow cytometry;American Journal of Clinical Pathology;2024-04-06

5. Diagnosis of erythroid dysplasia by flow cytometry: a review;Expert Review of Hematology;2023-12-02

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