Ann Lab Med.  2021 Jan;41(1):44-50. 10.3343/alm.2021.41.1.44.

Evaluation of the CellaVision Advanced RBC Application for Detecting Red Blood Cell Morphological Abnormalities

Affiliations
  • 1Department of Laboratory Medicine, Korea University Guro Hospital, Seoul, Korea

Abstract

Background
The Advanced RBC Application of the CellaVision DM9600 system (CellaVision AB, Lund, Sweden) automatically characterizes and classifies red blood cells (RBCs) into 21 morphological categories based on their size, color, shape, and inclusions. We evaluated the diagnostic performance of the CellaVision Advanced RBC Application with respect to the classification and grading of RBC morphological abnormalities in accordance with the 2015 International Council for Standardization in Haematology (ICSH) guidelines.
Methods
A total of 223 samples, including 123 with RBC morphological abnormalities and 100 from healthy controls, were included. Seven RBC morphological abnormalities and their grading obtained with CellaVision DM9600 pre- and post-classification were compared with the results obtained using manual microscopic examination. The grading cut-off percentages were determined in accordance with the 2015 ICSH guidelines. The sensitivity and specificity of the CellaVision DM9600 system were evaluated using the manual microscopic examination results as a true positive.
Results
In pre-classification, > 90% sensitivity was observed for target cells, tear drop cells, and schistocytes, while > 90% specificity was observed for acanthocytes, spherocytes, target cells, and tear drop cells. In post-classification, the detection sensitivity and specificity of most RBC morphological abnormalities increased, except for schistocytes (sensitivity) and acanthocytes (specificity). The grade agreement rates ranged from 35.9% (echinocytes) to 89.7% (spherocytes) in pre-classification and from 46.2% (echinocytes) to 90.1% (spherocytes) in post-classification. The agreement rate of samples with withinone grade difference exceeded 90% in most categories, except for schistocytes and echinocytes.
Conclusions
The Advanced RBC Application of CellaVision DM9600 is a valuable screening tool for detecting RBC morphological abnormalities.

Keyword

Red blood cell; Morphology; Advanced RBC Application; CellaVision DM9600; International Council for Standardization in Haematology guidelines

Figure

  • Fig. 1 Overview of the CellaVision DM9600 Advanced RBC Application. (A) The software provides an overview image using the RBC morphological category results, which are graded semi-quantitatively in four flag levels. (B) Image depicting individual cells categorized into each RBC morphological abnormality. Abbreviation: RBC, red blood cell.

  • Fig. 2 Screenshots of the CellaVision Advanced RBC Application. (A) The overview image of a sample with acanthocytes (grade 2). (B) Pre-classification results showing some acanthocytes classified as schistocytes. (C) Image of individual cells showing that the acanthocyte grade was underestimated. Abbreviation: RBC, red blood cell.


Cited by  1 articles

Obtaining Reliable CBC Results in Clinical Laboratories
Seon Young Kim, Hyun Kyung Kim
Ann Lab Med. 2022;42(5):505-506.    doi: 10.3343/alm.2022.42.5.505.


Reference

1. Stouten K, Riedl JA, Levin MD, van Gelder W. Examination of peripheral blood smears: performance evaluation of a digital microscope system using a large-scale leukocyte database. Int J Lab Hematol. 2015; 37:e137–40.
Article
2. Lee LH, Mansoor A, Wood B, Nelson H, Higa D, Naugler C. Performance of CellaVision DM96 in leukocyte classification. J Pathol Inform. 2013; 4:14.
Article
3. Cornet E, Perol JP, Troussard X. Performance evaluation and relevance of the CellaVision DM96 system in routine analysis and in patients with malignant hematological diseases. Int J Lab Hematol. 2008; 30:536–42.
4. Kratz A, Bengtsson HI, Casey JE, Keefe JM, Beatrice GH, Grzybek DY, et al. Performance evaluation of the CellaVision DM96 system: WBC differentials by automated digital image analysis supported by an artificial neural network. Am J Clin Pathol. 2005; 124:770–81.
5. Criel M, Godefroid M, Deckers B, Devos H, Cauwelier B, Emmerechts J. Evaluation of the red blood cell advanced software application on the CellaVision DM96. Int J Lab Hematol. 2016; 38:366–74.
Article
6. Horn CL, Mansoor A, Wood B, Nelson H, Higa D, Lee LH, et al. Performance of the CellaVision DM96 system for detecting red blood cell morphologic abnormalities. J Pathol Inform. 2015; 6:11.
7. Palmer L, Briggs C, McFadden S, Zini G, Burthem J, Rozenberg G, et al. ICSH recommendations for the standardization of nomenclature and grading of peripheral blood cell morphological features. Int J Lab Hematol. 2015; 37:287–303.
Article
8. Kim HN, Hur M, Kim H, Kim SW, Moon HW, Yun YM. Performance of automated digital cell imaging analyzer Sysmex DI-60. Clin Chem Lab Med. 2017; 56:94–102.
Article
9. Rümke CL, Bezemer PD, Kuik DJ. Normal values and least significant differences for differential leukocyte counts. J Chron Dis. 1975; 28:661–8.
Article
10. Barnes PW, McFadden SL, Machin SJ, Simson E. international consensus group for hematology. The international consensus group for hematology review: suggested criteria for action following automated CBC and WBC differential analysis. Lab Hematol. 2005; 11:83–90.
Article
11. Hervent AS, Godefroid M, Cauwelier B, Billiet J, Emmerechts J. Evaluation of schistocyte analysis by a novel automated digital cell morphology application. Int J Lab Hematol. 2015; 37:588–96.
Article
12. Kratz A, Lee SH, Zini G, Riedl JA, Hur M, Machin S. Digital morphology analyzers in hematology: ICSH review and recommendations. Int J Lab Hematol. 2019; 41:437–47.
Article
13. Zini G, d’Onofrio G, Briggs C, Erber W, Jou JM, Lee SH, et al. ICSH recommendations for identification, diagnostic value, and quantitation of schistocytes. Int J Lab Hematol. 2012; 34:107–16.
Article
14. Yoon J, Kwon JA, Yoon SY, Jang WS, Yang DJ, Nam J, et al. Diagnostic performance of CellaVision DM96 for Plasmodium vivax and Plasmodium falciparum screening in peripheral blood smears. Acta Trop. 2019; 193:7–11.
15. Briggs C, Longair I, Slavik M, Thwaite K, Mills R, Thavaraja V, et al. Can automated blood film analysis replace the manual differential? An evaluation of the CellaVision DM96 automated image analysis system. Int J Lab Hematol. 2009; 31:48–60.
Article
Full Text Links
  • ALM
Actions
Cited
CITED
export Copy
Close
Share
  • Twitter
  • Facebook
Similar articles
Copyright © 2024 by Korean Association of Medical Journal Editors. All rights reserved.     E-mail: koreamed@kamje.or.kr