Literature DB >> 33494332

Automated Early Detection of Myelodysplastic Syndrome within the General Population Using the Research Parameters of Beckman-Coulter DxH 800 Hematology Analyzer.

Noémie Ravalet1,2, Amélie Foucault1,2, Frédéric Picou1,2, Martin Gombert1, Emmanuel Renoult1, Julien Lejeune3, Nicolas Vallet3, Sébastien Lachot1, Emmanuelle Rault1, Emmanuel Gyan2,3, Marie C Bene4, Olivier Herault1,2.   

Abstract

The incidence of myelodysplastic syndrome increases with aging and the early diagnosis enables optimal care of these diseases. The DxH 800 hematology analyzer measures and calculates 126 cytological parameters, but only 23 are used for routine CBC assessment. The goal of this study was to use the 103 unexploited "research parameters" to develop an algorithm allowing for an early detection of subclinical MDS patients by triggering morphological analysis. Blood sample parameters from 101 MDS patients and 88 healthy volunteers were analyzed to identify the critical "research parameters" with: (i) the most significant differences between MDS patients and healthy volunteers, (ii) the best contributions to principal component analysis (PCA), first axis, and (iii) the best correlations with PCA, first two axes (cos2 > 0.6). Ten critical "research parameters" of white blood cells were identified, allowing for the calculation of an MDS-likelihood score (MDS-LS), based on logistic regression. Automatic calculation of the MDS-LS is easily implementable on the middleware system of the DxH 800 to generate a flag for blood smear review, and possibly early detection of MDS patients in the general population.

Entities:  

Keywords:  blood smear; cell blood count (CBC); myelodysplastic syndrome

Year:  2021        PMID: 33494332     DOI: 10.3390/cancers13030389

Source DB:  PubMed          Journal:  Cancers (Basel)        ISSN: 2072-6694            Impact factor:   6.639


  3 in total

1.  In-vivo pharmacokinetic studies of Dolutegravir loaded spray dried Chitosan nanoparticles as milk admixture for paediatrics infected with HIV.

Authors:  Priya Dharshini K; Ramya Devi D; Banudevi S; Vedha Hari B Narayanan
Journal:  Sci Rep       Date:  2022-08-16       Impact factor: 4.996

Review 2.  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

3.  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

  3 in total

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