Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Clinical Chemistry and Laboratory Medicine (CCLM)

Published in Association with the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM)

Editor-in-Chief: Plebani, Mario

Ed. by Gillery, Philippe / Lackner, Karl J. / Lippi, Giuseppe / Melichar, Bohuslav / Payne, Deborah A. / Schlattmann, Peter / Tate, Jillian R.

12 Issues per year


IMPACT FACTOR 2016: 3.432

CiteScore 2016: 2.21

SCImago Journal Rank (SJR) 2016: 1.000
Source Normalized Impact per Paper (SNIP) 2016: 1.112

Online
ISSN
1437-4331
See all formats and pricing
More options …
Volume 56, Issue 1

Issues

Performance of automated digital cell imaging analyzer Sysmex DI-60

Hyeong Nyeon Kim
  • Department of Laboratory Medicine, Konkuk University School of Medicine, Seoul, Republic of Korea
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Mina Hur
  • Corresponding author
  • Department of Laboratory Medicine, Konkuk University School of Medicine, Konkuk University Medical Center, 120-1, Neungdong-ro, Hwayang-dong, Gwangjin-gu, Seoul 05030, Republic of Korea, Phone: +82-2-2030-5581, Fax: +82-2-2636-6764
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Hanah Kim
  • Department of Laboratory Medicine, Konkuk University School of Medicine, Seoul, Republic of Korea
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Seung Wan Kim
  • Department of Laboratory Medicine, Konkuk University School of Medicine, Seoul, Republic of Korea
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Hee-Won Moon
  • Department of Laboratory Medicine, Konkuk University School of Medicine, Seoul, Republic of Korea
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Yeo-Min Yun
  • Department of Laboratory Medicine, Konkuk University School of Medicine, Seoul, Republic of Korea
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2017-06-16 | DOI: https://doi.org/10.1515/cclm-2017-0132

Abstract

Background:

The Sysmex DI-60 system (DI-60, Sysmex, Kobe, Japan) is a new automated digital cell imaging analyzer. We explored the performance of DI-60 in comparison with Sysmex XN analyzer (XN, Sysmex) and manual count.

Methods:

In a total of 276 samples (176 abnormal and 100 normal samples), white blood cell (WBC) differentials, red blood cell (RBC) classification and platelet (PLT) estimation by DI-60 were compared with the results by XN and/or manual count. RBC morphology between pre-classification and verification was compared according to the ICSH grading criteria. The manual count was performed according to the Clinical and Laboratory Standards Institute guidelines (H20-A2).

Results:

The overall concordance between DI-60 and manual count for WBCs was 86.0%. The agreement between DI-60 pre-classification and verification was excellent (weighted κ=0.963) for WBC five-part differentials. The correlation with manual count was very strong for neutrophils (r=0.955), lymphocytes (r=0.871), immature granulocytes (r=0.820), and blasts (r=0.879). RBC grading showed notable differences between DI-60 and manual counting on the basis of the ICSH grading criteria. Platelet count by DI-60 highly correlated with that by XN (r=0.945). However, DI-60 underestimated platelet counts in samples with marked thrombocytosis.

Conclusions:

The performance of DI-60 for WBC differential, RBC classification, and platelet estimation seems to be acceptable even in abnormal samples with improvement after verification. DI-60 would help optimize the workflow in hematology laboratory with reduced manual workload.

Keywords: comparison; manual count; performance; Sysmex DI-60; Sysmex XN

Introduction

Validation of automated hematology analyzer results by manual slide review (MSR) is an inevitable process in clinical hematology laboratories [1]. Manual differential count of 200 cells performed by two experienced laboratory staff members is still the gold standard for white blood cells (WBC) differential [2]. MSR, however, is time consuming, labor intensive and requires highly experienced, well-trained laboratory staffs. Furthermore, its subjectivity makes it difficult to apply proper quality control [3], [4].

Compared with MSR, automated cell image analysis would provide a more standardized differential result and would improve differential turnaround time significantly. It would help reduce errors due to subjectivity and allow for increased repeatability between samples and operators. Time-saving would make additional time that can be applied to other activities in the lab, resulting in a significant advantage when switching from MSR to automated image analysis. Previous studies on clinical applications of automated image analyzers showed potential benefits and some limitations [5], [6], [7], [8].

The Sysmex DI-60 system (DI-60, Sysmex, Kobe, Japan) is the first fully-integrated cell image analyzer on the market, which is co-operable with XN hematology analyzer (Sysmex) and slide making/staining device SP-10 (Sysmex), thus allowing a single sample placed on automation line to produce complete blood count (CBC) results, smear preparation and digital cell location. DI-60 can pre-classify WBC, pre-characterize red blood cells (RBC) with advanced feature and estimate numbers of platelets (PLT) on peripheral blood smears. The performance of DI-60 was evaluated in only one study, which evaluated the accuracy of pre-classification of WBC and the efficiency of the XN-series as an integrated blood cell analysis system [9]. In this study, we wanted to explore the performance of DI-60 comprehensively with regard to WBC differentials, RBC classifications and PLT estimation, in comparison with manual count and Sysmex XN.

Materials and methods

Blood samples

This study was conducted from March to October in 2016 in Konkuk University Medical Center (KUMC), Seoul, Korea, using a total of 276 samples (100 normal samples and 176 abnormal samples). Normal samples were obtained from healthy individuals who showed unremarkable findings in routine physical check-up. Abnormal samples consisted of 81 samples from umbilical cord blood (CB) and 95 samples from the patients with abnormal CBC findings and/or hematologic disorders, including plasma cell myeloma (n=15), myelodysplastic syndrome (n=8), myeloproliferative neoplasm (n=8), malignant lymphoma (n=5), autoimmune cytopenia (n=5), acute myeloid leukemia (n=4), aplastic anemia (n=3), acute lymphoblastic leukemia (n=2), mixed phenotype acute leukemia (B/myeloid) (n=1) and lymphoplasmacytic lymphoma (n=1). The samples were collected from the antecubital veins of participants in K3-EDTA-containing vacutainer (Greiner Bio-One GmbH, Frickenhausen, Germany). CB samples (3 mL) were collected directly from umbilical veins of neonates using syringes and transferred immediately into K3-EDTA containing vacutainer. All samples were run in Sysmex XN within 4 h after collection. Blood smears were prepared and stained using SP-10 (Sysmex) and Wright Giemsa (Muto Pure chemicals, Tokyo, Japan), respectively, and were reviewed according to the Clinical and Laboratory Standards Institute (CLSI) H20-A2 guidelines [2]. The same, two pathologists, who were trained and experienced in diagnostic hematology, examined the slides; after scanning the blood smear under low power for unusual or abnormal cells and an acceptable cell distribution, 200 cells were counted using the battlement track pattern. The data from DI-60 (pre-classification and verification), Sysmex XN, and manual count were compared with each other.

This study was an in vitro comparison study using remnant blood samples or CB samples that would have been discarded. There was no study-specific intervention or additional blood collection, and the study protocol was designed following the criteria of the Declaration of Helsinki; therefore, this study was exempted from the approval of the Institutional Review Board of KUMC. Written informed consent was obtained from the mother of each neonate for the use of CB samples and was exempt for the use of remnant blood samples because the data were obtained during routine CBC without additional blood sampling.

DI-60

DI-60 has an optic unit consisting of a microscope, camera, and a computer system containing the acquisition and classification software Cella Vision DM software. The motorized microscope has two objectives (10× and 100×) and intermediate optics switching between 1.0× and 0.5× magnifications, which combined yields images with 5×, 10×, 50× or 100× magnification. For the quality control of cell location performance in DI-60, cell location test is run at a regular interval (once or twice per day in high-load laboratories) and after changes in staining procedure or staining solutions, using freshly stained blood sample with normal WBC counts as cell location slides; if the system cannot locate at least 100 nucleated cells, the result would be discarded, and the percentage of non-nucleated cells should not exceed 30% of the total number of objects.

WBC analysis

In each sample, 200 cells were pre-classified and verified independently by the same two hematology experts. DI-60 pre-classifies 18 WBC classes, including leukocytes (segmented and band neutrophils, eosinophils, basophils, lymphocytes, monocytes, blast cells, promyelocytes, myelocytes, metamyelocytes, atypical lymphocytes and plasma cells) and non-leukocytes (smudged cells, artifacts, giant platelets, platelet clumps, erythroblasts and unidentified). Counting band neutrophils has been eliminated in the laboratories in Europe and the United States; however, it still remains in the Korean and Japanese laboratories, band neutrophils are separately counted in the CLSI guidelines, and DI-60 differentiates band neutrophils from segmented neutrophils [2], [9], [10]. Accordingly, we classified band and segmented neutrophils separately. For the comparison, pre-classified plasma cells were counted as variant lymphocytes; giant platelets and platelet clumps were excluded from the comparison.

Concordance and agreement were evaluated between DI-60 pre-classification and verification in normal and abnormal samples. Abnormal samples included 47 leukopenic samples (WBC<4.0×109/L) and 15 dysplastic samples (from patients diagnosed as having myelodysplastic syndrome or in post-chemotherapy status with prominent [>10%] granulocytic dysplasia). The difference and correlation of cell classification was evaluated among DI-60 (pre-classification and verification), manual counts, and XN analyzer. In Sysmex XN, the manufacturer-claimed maximum within-run coefficient of variance was 3% in normal samples (WBC≥4.0×109/L); we adopted this value to evaluate the difference [11].

The number of WBC counting can be set up as an option in DI-60 (100 or 200 cells) and this was set by counting 200 cells for the further evaluation of detection performance in the 47 leukopenic samples. These leukopenic samples were further divided into three groups: mild leukopenia (2.0–4.0×109/L, n=28), moderate leukopenia (1.0–2.0×109/L, n=15), and severe leukopenia (<1.0×109/L, n=4).

RBC analysis

RBC analysis was performed according to the 2014 ICSH recommendation guideline for the RBC morphology grading [12]. The ICSH recommendations suggested counting at least 1000 RBCs for grading RBC morphology. Except for the 62 samples with low RBC counts (<1000, n=7) or poor stain quality/low resolution (n=55), 214 samples were evaluated between pre-classification by DI-60 and verification by user. RBCs were counted in ideal zone, which characterizes 200–250 RBCs in a high power field (HPF) [13].

DI-60 pre-characterizes 21 RBC morphology characteristics for an overview and individual cell image through the advanced RBC application and the ICSH guideline suggests 22 RBC classifications [12]. Consequently, there were 18 overlapping RBC classifications between DI-60 and the ICSH guideline (Table 1). Among these 18 overlapping RBC classifications, we excluded three classifications (basophilic stippling, Howell-Jolly bodies, and Pappenheimer bodies) due to limited positive samples and/or low resolution capture image quality. For the included 15 RBC classifications, three RBC classifications (macrocytes, microcytes, and hypochromic cells) were compared between DI-60 and XN, according to the ICSH guideline [12]. The other 12 RBC classifications were compared between DI-60 and manual method; 100 poikilocytosis-positive samples were selected for this comparison.

Table 1:

Comparison between ICSH grading criteria and DI-60 default value.

The ICSH morphology grading contains a two-tiered grading system for 2+ (moderate) and 3+ (many). The designation for 1+ (few/rare) is reserved only for schistocytes, as the observation of schistocytes, even in small numbers, is clinically significant [12]. Therefore, we also explored the optimal cutoff of schistocytes in DI-60, in comparison with manual count.

PLT estimation analysis

In a total of 246 samples, 53 samples were thrombocytopenic (<150×109/L) and nine samples were thrombocythaemic (>450×109/L). PLT estimation feature in DI-60 is manipulated by counting PLTs manually in the nine square field captured images (0.89 HPF/square field) and multiplied by preset estimation factor that is calculated in demonstration setting; the estimation factor was set as 11.7 in this study. PLT counts estimated by DI-60 were compared with PLT numbers counted by XN in all 276 samples.

Statistical analysis

Data were expressed as median (interquartile range) or number (percentage). Manual counts or XN results was considered gold standard for each comparison, appropriately. The performances of DI-60 were evaluated using sensitivity and specificity with their 95% confidence interval (CI). The concordance rates between pre-classification and verification were obtained, and agreement between them was assessed using Cohen’s kappa with 95% CI: <0.20, none; 0.21–0.39, minimal; 0.40–0.59, weak; 0.60–0.79, moderate; 0.80–0.90, strong; and >0.90, almost perfect [14]. Proportional difference between groups was compared using χ2 test. Receiver operating characteristics (ROC) curves were analyzed and areas under the curves (AUC) were compared to find optima cut-off for schistocytes. Passing-Bablok regression analysis and the Bland-Altman plot were used to compare WBC differentials and PLT counts between DI-60 and manual counts and/or XN. Pearson’s correlation coefficient (r) with 95% CI was interpreted as follows: <0.30, negligible; 0.30–0.50, low; 0.50–0.70, moderate; 0.70–0.90, high; and 0.90–1.00, very high) [15]. For statistical analysis, MedCalc Statistical Software (version 12.3.0, MedCalc Software, Mariakerke, Belgium) and Analyse-It (Analyse-it Software Ltd., Leeds, UK) were used. p-Values less than 0.05 were considered statistically significant.

Results

WBC analysis

The overall concordance between DI-60 pre-classification and verification was 86.0% for all 43,710 cells and 87.9% for normal WBCs (Table 2). Similar findings were observed in 47 leukopenic samples and 15 dysplastic samples: in leukopenic samples, the concordance was 83.5% for all cells and 85.9% for normal WBCs; and in dysplastic samples, the concordance was 84.3% for all cells and 85.7% for normal WBCs (data not shown). Although overall efficiency was very high for all cell classes, it was attributed to the combination of low false-positive rates and high false-negative rates. The agreement between DI-60 pre-classification and verification was almost perfect (weighted κ=0.963) for normal WBCs (Table 3). By including non-cells and abnormal cells, the agreement was moderate (weighted κ=0.722). The pre-classifications for blasts and monocytes showed lower performances than other WBC classifications. DI-60 misclassified 21.9% of blasts to lymphocytes and 23.9% of monocytes to atypical lymphocytes.

Table 2:

Performance of WBC pre-classification by Sysmex DI-60 on the basis of verification.

Table 3:

Agreement between pre-classification and verification in Sysmex DI-60.

The mean differences of WBC differentials among DI-60, XN, and manual count were mostly acceptable, except for the comparison between DI-60 pre-classification and XN (Table 4). The correlation between DI-60 pre-classification and manual count was very high for neutrophils (r=0.950) and high for lymphocytes (r=0.864), immature granulocytes (IG) (r=0.824), and blasts (r=0.880) in all 276 samples. After verification, the correlation between DI-60 and manual count improved substantially; it was high or very high for most cells (r=0.716 to 0.948), except basophils (r=0.354). DI-60 and XN also showed very high or high correlations for neutrophils (r=0.914), lymphocytes (r=0.856), eosinophils (r=0.830), IG (r=0.806) and nRBCs (r=0.960). For monocytes, pre-classification was unsatisfactory than the other WBC classifications, and this was maybe due to three samples from the patients with hematologic malignancies (one malignant lymphoma and two acute myeloid leukemia). In these samples, a huge number of monoblasts or lymphoma cells were counted as monocytes in XN. By excluding these three samples, the correlation became moderate (r=0.618).

Table 4:

Comparison of WBC differentials and nRBC counts by Sysmex DI-60, XN and manual count.

With the setting of 200 WBC counting, DI-60 counted 192.4 WBCs per slide in average in 28 samples with mild leukopenia; 158.8 WBCs per slide in average in 15 samples with moderate leukopenia; and 55.3 WBCs per slide in average in four samples with severe leukopenia, respectively.

RBC analysis

Table 5 summarizes the performance of DI-60 for RBC analysis. For hypochromic cells and microcytes, DI-60 was comparable to XN with high specificity (99.0% and 98.6%, respectively) and low sensitivity (11.1% and 0.0%, respectively). For macrocytes, in contrast, DI-60 showed higher sensitivity and lower specificity (87.1% and 41.0%, respectively), and the detection of macrocytes was significantly different between DI-60 and XN (68.2% vs. 18.7%, p<0.0001). For the other 12 RBC classifications, DI-60 mostly yielded high specificity (89.1–100.0%) and low sensitivity (0.0–62.5%). For anisocytosis, DI-60 showed 100% sensitivity with 4.2% specificity. DI-60 showed good sensitivity and specificity only for echinocytes (90.9% and 86.5%, respectively). Regarding the cutoff of schistocytes, the cutoff of 2+ showed the largest AUC (0.76); however, the AUCs for the cutoffs of 2+ and 3+ did not show any statistical difference (Table 6).

Table 5:

RBC analysis by DI-60 in comparison with Sysmex XN and manul method for 15 RBC morphology classes (n=214).

Table 6:

Comparison of cutoffs for schistocytes in DI-60.

PLT analysis

DI-60 and XN showed a very high correlation (r=0.948) for PLT estimation. However, in two samples that were obtained from patients diagnosed as having essential thrombocythaemia, PLT counts between DI-60 and XN showed a substantial difference; in both samples, DI-60 counted PLTs much less than XN (1387×109/L vs. 1754×109/L, difference=367×109/L; 1185×109/L vs. 1540×109/L, difference=355×109/L). Estimated PLT counts by DI-60 was 227×109/L in comparison with 241×109/L by XN (average PLT/HPF: 19.4) with mean difference of –4.72×109/L (95% CI, –14.86 to 5.42×109/L) (Figure 1). In six thrombocytopenic samples (<20×109/L), estimated PLT counts by DI-60 was 17.8×109/L in comparison with 15.2×109/L by XN with mean difference of 2.63×109/L (95% CI, –1.66 to 6.93×109/L).

Comparison of platelet counts between Sysmex DI-60 and Sysmex XN (n=276). (A) Correlation between Sysmex DI-60 and Sysmex XN. Solid line, Passing-Bablok regression; dashed line, identity line. (B) Bland-Altman plot for difference between Sysmex DI-60 and Sysmex XN. Bold line, mean difference between values; dashed lines, mean difference ±1.96 standard deviation (SD).
Figure 1:

Comparison of platelet counts between Sysmex DI-60 and Sysmex XN (n=276).

(A) Correlation between Sysmex DI-60 and Sysmex XN. Solid line, Passing-Bablok regression; dashed line, identity line. (B) Bland-Altman plot for difference between Sysmex DI-60 and Sysmex XN. Bold line, mean difference between values; dashed lines, mean difference ±1.96 standard deviation (SD).

Discussion

Advancements in imaging technology have enabled the automation of cell counting, providing improved accuracy and reliability, with much less time and effort [4]. Previous studies on digital cell imaging analyzers mainly focused on WBC differential [6], [9], [10], [16], [17], [18], [19]. We evaluated the performance of DI-60 comprehensively, encompassing WBC classification, RBC morphology grading and PLT estimation.

The overall performance of DI-60 for WBC pre-classification was satisfactory with high concordance with verification (Table 2), and this is in line with previous findings using DI-60 and DM96 [9], [10]. However, DI-60 WBC pre-classification showed low false-positive rates and high false-negative rates compared with verification. Of note, very low false-positive rate (0.8%) of blasts was combined with high (44.0%) false-negative rate. This finding implies that the absence and presence of blasts cannot be confirmed by pre-classification alone [20]. Moreover, if not located and pre-classified by DI-60, there is no chance to detect abnormal cells using verification. The agreement between pre-classification and verification was almost perfect for five-part differentials and moderate for all cells including non-cells, IG, blasts and nucleated RBCs (Table 3). Comparison of WBC differentials between DI-60, manual counts, and Sysmex XN showed acceptable difference and moderate to high or very high correlations both in normal and abnormal samples (Table 4). Such good performances were constantly observed in leukopenic and dysplastic samples, although the number of evaluated samples was limited. However, blasts or small-sized lymphoma cells were inappropriately pre-classified by DI-60, warranting a caution for its use in hematology laboratories with a large burden of pathologic samples [4], [16].

Several studies used advanced RBC application of Cella Vision DM96, but their performances varied according to the cutoffs used [21], [22], [23], [24], [25]. We adopted the ICSH grading criteria for the evaluation of RBC analysis [12]. For the three categories (hypochromic cells, microcytes, and macrocytes) compared with XN, DI-60 showed high specificity and much lower sensitivity for hypochromic cells and microcytes. For the other 12 categories using ICSH cutoff, DI-60 yielded overall high specificity and various sensitivity (Table 5). These results were similar with those by Egelé et al. [25].

Detection of schistocytes is clinically important [22]. As reported previously, detection ability of schistocytes in DI-60 is quite excellent, and our study also showed no false negative samples with 1+ cutoff [23]. However, the ICSH grading criterion of 1+ for schistocytes seems to be too sensitive to be applied for DI-60, and the ICSH grading criterion of 2+ for schistocytes seems to be suitable for DI-60 in terms of AUC (Table 6). Of note, a number of artifacts, which were made during slide preparation, were misidentified as schistocytes. Although it is important not missing schistocytes, considering its high work-load in daily routine, it would be useful to determine each laboratory’s internal cutoff for filtering samples that do not require manual reclassification [22].

ICSH guidelines provide information on how to reliably and consistently report abnormal RBC using manual microscopy [12]. It is unclear whether it can provide the exact RBC grading criteria for digital image-based hematology system, too. Our results imply that ICSH cutoff values, which were suggested for the manual counting, may be too insensitive to be applied for DI-60 [24]. Considering its sensitive detection of artifacts, different resolutions, and analyzing principles, new RBC grading criteria would be necessary for image analyzers instead of the current ICSH criteria.

Platelet count estimation by DI-60 was overall satisfactory with very high correlation with XN as reported by Gao et al. [26] (Figure 1). However, DI-60 may underestimate platelet counts in samples with marked thrombocytosis, and in such cases, estimated PLT counts by DI-60 should be confirmed by hematology analyzers and MSR.

The present study has its strength and limitation. We comprehensively explored a new automated digital cell morphology analyzer, Sysmex DI-60 system, in comparison with Sysmex XN and manual count. There were no positive samples for sickle cells, stomatocytes, and acanthocytes; this could be a limitation of this study. The performance of Sysmex DI-60 for WBC classification seems to be acceptable both in normal and abnormal samples with improvement after verification. RBC grading, which was evaluated on the basis of the ICSH grading criteria, showed significant differences between DI-60 and manual counting for most of the cells. Instead of the current ICSH criteria, new criteria for RBC grading would be necessary for digital cell morphology analyzers. Platelet count estimation by DI-60 was overall satisfactory except for the cases with marked thrombocytosis. Sysmex DI-60, with its high performance on WBC pre-classification, would help optimize the workflow in hematology laboratory with reduced workload of manual count. In addition to WBC differential, improved capabilities to view RBC morphology and PLT counts would facilitate a transition from the conventional method to digital image technology to review peripheral blood smears.

References

  • 1.

    Hur M, Cho JH, Kim H, Hong MH, Moon HW, Yun YM, et al. Optimization of laboratory workflow in clinical hematology laboratory with reduced manual slide review: comparison between Sysmex XE-2100 and ABX Pentra DX120. Int J Lab Hematol 2011;33:434–40. CrossrefPubMedWeb of ScienceGoogle Scholar

  • 2.

    Clinical and Laboratory Standards Institute (CLSI). Reference leukocytes (WBC) differential count (proportional) and evaluation of instrumental methods: approval standard, 2nd ed. CLSI Document H20-A2. Wayne, PA: CLSI, 2007. Google Scholar

  • 3.

    Rumke CL. Imprecision of ratio-derived differential leukocyte counts. Blood Cells 1985;11:311–5. PubMedGoogle Scholar

  • 4.

    Da Costa L. Digital image analysis of blood cells. Clin Lab Med 2015;35:105–22. PubMedWeb of ScienceCrossrefGoogle Scholar

  • 5.

    Perel ID, Herrmann NR, Watson LJ. Automated differential leucocyte counting by the Geometric Data Hematrak system: eighteen months experience in a private pathology laboratory. Pathology 1980;12:449–60. CrossrefGoogle Scholar

  • 6.

    Kratz A, Bengtsson HI, Case 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;127:770–81. Google Scholar

  • 7.

    Smits SM, Leyte A. Clinical performance evaluation of the CellaVision Image Capture System in the white blood cell differential on peripheral blood smears. J Clin Pathol 2014;67:168–72. PubMedCrossrefWeb of ScienceGoogle Scholar

  • 8.

    VanVranken SJ, Patterson ES, Rudmann SV, Waller KV. A survey study of benefits and limitations of using CellaVision DM96 for peripheral blood differentials. Clin Lab Sci 2014;27:32–9. PubMedGoogle Scholar

  • 9.

    Tabe Y, Yamamoto T, Maenou L, Nakai R, Idei M, Horii T, et al. Performance evaluation of the digital cell imaging analyzer DI-60 integrated into the fully automated Sysmex XN hamatology analyzer system. Clin Chem Lab Med 2015;53:281–9. Google Scholar

  • 10.

    Briggs C, Longair I, Slavik M, Thwaite K, Mills R, Thavaraja V, et al. Can automated blood film analysis replace the manaul differential? An evaluation of the CellaVision DM96 autoamted image analysis system. Int J Lab Hematol 2009;31:48–60. PubMedCrossrefGoogle Scholar

  • 11.

    Kim H, Hur M, Choi SG, Oh KM, Moon HW, Yun YM. Comparison of white blood cell counts by WNR, WDF, and WPC channels in Sysmex XN hematology analyzer. Int J Lab Hematol 2015;37:869–75. CrossrefPubMedWeb of ScienceGoogle Scholar

  • 12.

    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. Web of ScienceCrossrefPubMedGoogle Scholar

  • 13.

    Maedel LB, Doig K. Examination of the peripheral blood film and correlation with the complete blood count. In: Rodak’s hematology: Clinical principles and application, 5th ed. St. Louis, MO: Saunders, 2015:242. Google Scholar

  • 14.

    McHugh ML. Interrater reliability: the kappa statistic. Biochem Med 2012;22:276–82. Google Scholar

  • 15.

    Mukaka MM. A guide to appropriate use of correlation coefficient in medical research. Malawi Med J 2012;24:69–71. PubMedGoogle Scholar

  • 16.

    Billard M, Lainey E, Armoogum P, Alberti C, Fenneteau O, Da Costa L. Evaluation of the CellaVision DM automated microscope in pediatrics. Int J Lab Hematol 2010;32:530–8. CrossrefPubMedWeb of ScienceGoogle Scholar

  • 17.

    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. Web of SciencePubMedGoogle Scholar

  • 18.

    Park SH, Park CJ, Choi MO, Kim MJ, Cho YU, Jang S, et al. Automated digital cell morphology identification system (CellaVision DM96) is very useful for leukocyte differentials in specimens with qualitative or quantitative abnormalities. Int J Lab Hematol 2013;35:517–27. CrossrefPubMedWeb of ScienceGoogle Scholar

  • 19.

    International Council for Standardization in Haematology, Writing Group, Briggs C, Culp N, Davis B, D’onofrio G, Zini G, Machin SJ, et al. ICSH guidelines for the evaluation of blood cell analysers including those used for differential leucocyte and reticulocyte counting. Int J Lab Hematol 2014;36:613–27. CrossrefPubMedWeb of ScienceGoogle Scholar

  • 20.

    Eilertsen H, Henriksson CE, Hagve TA. The use of CellsVision DM96 in the verification of the presence of blasts in samples flagged by the sysmex XE-5000. Int J Lab Hematol 2017 Mar 23. doi: 10.1111/ijlh.12648. [Epub ahead of print] Google Scholar

  • 21.

    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. PubMedCrossrefGoogle Scholar

  • 22.

    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. PubMedCrossrefWeb of ScienceGoogle Scholar

  • 23.

    Egelé A, Gelder WV, Riedl J. Automated detection and classification of schistocytes by a novel red blood cell module using digital imaging/microscopy. J Hematol 2015;4:184–6. CrossrefGoogle Scholar

  • 24.

    Criel M, Godefroid M, Deckers B, Devos H, Cauwelier B, Emmerechts J. Evaluation of the red blood cell adavanced software application on the CellaVision DM96. Int J Lab Hematol 2016;38:366–74. CrossrefGoogle Scholar

  • 25.

    Egelé A, Stouten K, van der Heul-Nieuwenhuijsen L, de Bruin L, Teuns R, van Gelder W et al. Classification of several morphological red blood cell abnormalities by DM96 digital imaging. Int J Lab Hematol 2016;38:e98–101. PubMedCrossrefWeb of ScienceGoogle Scholar

  • 26.

    Gao Y, Mansoor A, Wood B, Nelson H, Higa D, Naugler C. Platelet count estimation using the CellaVision DM96 system. J Pathol Inform 2013;4:16. CrossrefPubMedGoogle Scholar

About the article

Corresponding author: Mina Hur, MD, PhD, Department of Laboratory Medicine, Konkuk University School of Medicine, Konkuk University Medical Center, 120-1, Neungdong-ro, Hwayang-dong, Gwangjin-gu, Seoul 05030, Republic of Korea, Phone: +82-2-2030-5581, Fax: +82-2-2636-6764


Received: 2017-02-16

Accepted: 2017-05-01

Published Online: 2017-06-16

Published in Print: 2017-11-27


Author contributions: Kim HN collected the samples, analyzed the data, and wrote the draft; Kim H analyzed the data; Hur M conceived the study, analyzed the data, and finalized the draft; Kim SW collected the samples; Moon HW and Yun YM discussed the data and reviewed the manuscript. All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

Research funding: This work was supported by Konkuk University Medical Center Research Grant 2017.

Employment or leadership: None declared.

Honorarium: None declared.

Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.


Citation Information: Clinical Chemistry and Laboratory Medicine (CCLM), Volume 56, Issue 1, Pages 94–102, ISSN (Online) 1437-4331, ISSN (Print) 1434-6621, DOI: https://doi.org/10.1515/cclm-2017-0132.

Export Citation

©2018 Walter de Gruyter GmbH, Berlin/Boston. Copyright Clearance Center

Comments (0)

Please log in or register to comment.
Log in