Literature DB >> 17984041

Automated neutrophil morphology and its utility in the assessment of neutrophil dysplasia.

Amparo Miguel1, Maite Orero, Ramon Simon, Rosa Collado, Pedro L Perez, Alejandro Pacios, Rosario Iglesias, Antonio Martinez, Felix Carbonell.   

Abstract

The automated hematology cell analyzer Coulter LH 750 (Beckman Coulter, Brea, CA, USA) uses a combination of 3 measurements-volume, conductivity, and scatter-to identify cells, but it could take advantage of these parameters to evaluate their morphologic changes. The neutrophil mean cell volume (MNEV), mean cell conductivity (MNEC), and mean cell scatter (MNES) were evaluated in 54 patients with myelodysplastic syndrome (MDS), 18 with chronic myelomonocytic leukemia (CMML), and 59 healthy controls. MNES and MNEC in the MDS group including all subtypes (World Health Organization classification) and in the CMML patients were significantly lower than the control group. MNES in MDS and CMML correlated well with the cytoplasmic hypogranularity evaluated by microscopic observation (P = .01). The value of MNES and MNEC as screening parameters in the neutrophil dysplasia evaluation showed, for this study, a sensitivity of 71.8% with a specificity of 86.4% (area under the curve [AUC], 0.817) and a cutoff of <139.3 for MNES and a sensitivity of 70.4% with a specificity of 76.3% (AUC, 0.752) and a cutoff of <150.9 for MNEC.

Entities:  

Mesh:

Year:  2007        PMID: 17984041     DOI: 10.1532/LH96.07011

Source DB:  PubMed          Journal:  Lab Hematol        ISSN: 1080-2924


  5 in total

1.  Clinical utility of the neutrophil distribution pattern obtained using the CELL-DYN SAPPHIRE hematology analyzer for the diagnosis of myelodysplastic syndrome.

Authors:  Tohru Inaba; Yoichi Yuki; Soichi Yuasa; Naohisa Fujita; Kazue Yoshitomi; Toshinori Kamisako; Kunio Torii; Toshiharu Okada; Yoshimasa Urasaki; Takanori Ueda; Kaoru Tohyama
Journal:  Int J Hematol       Date:  2011-07-06       Impact factor: 2.490

2.  Utility of cell population data (VCS parameters) as a rapid screening tool for Acute Myeloid Leukemia (AML) in resource-constrained laboratories.

Authors:  Harpreet Virk; Neelam Varma; Shano Naseem; Ishwar Bihana; Dmitry Sukhachev
Journal:  J Clin Lab Anal       Date:  2018-09-29       Impact factor: 2.352

3.  Cell Population Data-Driven Acute Promyelocytic Leukemia Flagging Through Artificial Neural Network Predictive Modeling.

Authors:  Rana Zeeshan Haider; Ikram Uddin Ujjan; Tahir S Shamsi
Journal:  Transl Oncol       Date:  2019-11-13       Impact factor: 4.243

Review 4.  Personalized Risk Schemes and Machine Learning to Empower Genomic Prognostication Models in Myelodysplastic Syndromes.

Authors:  Hussein Awada; Carmelo Gurnari; Arda Durmaz; Hassan Awada; Simona Pagliuca; Valeria Visconte
Journal:  Int J Mol Sci       Date:  2022-03-03       Impact factor: 5.923

5.  Beyond the In-Practice CBC: The Research CBC Parameters-Driven Machine Learning Predictive Modeling for Early Differentiation among Leukemias.

Authors:  Rana Zeeshan Haider; Ikram Uddin Ujjan; Najeed Ahmed Khan; Eloisa Urrechaga; Tahir Sultan Shamsi
Journal:  Diagnostics (Basel)       Date:  2022-01-07
  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.