Literature DB >> 33002332

Automated leukocyte parameters are useful in the assessment of myelodysplastic syndromes.

Anna Shestakova1,2, Ali Nael1,3, Virgilita Nora1, Sherif Rezk1, Xiaohui Zhao1.   

Abstract

BACKGROUND: Study utility of seven automated VCS parameters (V-volume, C-conductivity and S-scatter) in leukocytes as an objective read-out of dysplasia in Myelodysplastic Syndromes (MDS).
METHODS: Peripheral blood was analyzed by Beckman-Coulter DxH800 hematology analyzer in 43 patients with low-grade, high-grade MDS and 21 control individuals. The differences in mean (MN) and standard deviation (SD) of each parameter were examined. The optimal sensitivity and specificity to predict MDS were determined by statistical analysis.
RESULTS: In neutrophils, all means of the light scatters were significantly lower in high-grade MDS than in the control group. Mean median angle light scatter (MN-MALS-NE) and mean upper median angle light scatter (MN-UMALS-NE) were significantly different between low-grade MDS and control patients. MN-MALS-NE as a MDS predictor revealed 63% sensitivity and 67% specificity with a cutoff value of ≤133. SDs of each parameter in neutrophils differed significantly among three groups. SD of neutrophil upper median angle light scatter (SD-UMALS-NE) had 77% sensitivity and 82% specificity (cutoff value of ≥11.16) to predict MDS.
CONCLUSIONS: MDS patients have a significant decrease with a linear trend in VCS parameters in neutrophils, indicating cell dysplasia. The degree of the heterogeneity measured by SD is the most predictive of MDS.
© 2020 International Clinical Cytometry Society.

Entities:  

Keywords:  automatic hematology analyzer 2; light scatter parameters 3; myelodysplastic syndromes 1

Mesh:

Year:  2020        PMID: 33002332     DOI: 10.1002/cyto.b.21947

Source DB:  PubMed          Journal:  Cytometry B Clin Cytom        ISSN: 1552-4949            Impact factor:   3.058


  2 in total

Review 1.  Automated Detection of Dysplasia: Data Mining from Our Hematology Analyzers.

Authors:  Jaja Zhu; Sylvain Clauser; Nicolas Freynet; Valérie Bardet
Journal:  Diagnostics (Basel)       Date:  2022-06-26

2.  Machine learning-based improvement of MDS-CBC score brings platelets into the limelight to optimize smear review in the hematology laboratory.

Authors:  Jaja Zhu; Pierre Lemaire; Stéphanie Mathis; Emily Ronez; Sylvain Clauser; Katayoun Jondeau; Pierre Fenaux; Lionel Adès; Valérie Bardet
Journal:  BMC Cancer       Date:  2022-09-10       Impact factor: 4.638

  2 in total

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