Evaluation of bone marrow aspirates using the automated hematology analyzer Sysmex XN-3000.

INTRODUCTION: This study evaluated the feasibility of the Sysmex XN-3000 automated hematology analyzer for the assessment of total nucleated cells (TNC) and bone marrow (BM) cell density in routine bone marrow aspiration (BMA) samples. METHODS: A total of 54 BMA samples from 39 hematological patients were evaluated. The number of megakaryocytes was calculated by a specific gating algorithm using the body fluid mode of the WBC differential (WDF) channel. Lipid contents were calculated through a newly developed algorithm utilizing the WDF channel. The ratio of lipid particles over TNCs by the WNR channel was compared with the BM cellularity assessed by the BM biopsy. The myeloid/erythroid (M/E) ratio was calculated by measuring the number of myeloid cells in the WDF channel and the number of nucleated red blood cells (NRBCs) in the WNR channel. RESULTS: XN-3000 counts and microscopic results showed a linear correlation in TNC (R2  = .98, p < .001), megakaryocytes (R2  = .59, p = .002), NRBC (R2  = .84, p < .001), and M/E ratio (R2  = .59, p < .001). There were significant differences in the lipid/TNC ratios of hypercellular, normocellular, and hypocellular BMs measured by XN-3000 (p < .001). Receiver-operating characteristic analysis detected cut-off values of the lipid/TNC ratio of >0.4054 for hypoplasia and <0.157 for hyperplasia. The sensitivity and specificity for hypoplasia were 100% and 88%, and for hyperplasia were 89% and 86%, respectively. CONCLUSION: XN-3000 provides a quantitative assessment of BM cellularity, supporting the qualitative assessment by myelogram and BM biopsy.

Automated diagnostic support system with deep learning algorithms for distinction of Philadelphia chromosome-negative myeloproliferative neoplasms using peripheral blood specimen.

Philadelphia chromosome-negative myeloproliferative neoplasms (Ph-negative MPNs) such as polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis are characterized by abnormal proliferation of mature bone marrow cell lineages. Since various non-hematologic disorders can also cause leukocytosis, thrombocytosis and polycythemia, the detection of abnormal peripheral blood cells is essential for the diagnostic screening of Ph-negative MPNs. We sought to develop an automated diagnostic support system of Ph-negative MPNs. Our strategy was to combine the complete blood cell count and research parameters obtained by an automated hematology analyzer (Sysmex XN-9000) with morphological parameters that were extracted using a convolutional neural network deep learning system equipped with an Extreme Gradient Boosting (XGBoost)-based decision-making algorithm. The developed system showed promising performance in the differentiation of PV, ET, and MF with high accuracy when compared with those of the human diagnoses, namely: > 90% sensitivity and > 90% specificity. The calculated area under the curve of the ROC curves were 0.990, 0.967, and 0.974 for PV, ET, MF, respectively. This study is a step toward establishing a universal automated diagnostic system for all types of hematology disorders.

Modification of the Algorithm Used by Automated Hematology Analyzer XN-3000 Improves Specificity in the Detection of Schistocytes.

BACKGROUND: This study investigated the feasibility and accuracy of an automated hematology analyzer in the detection of schistocytes. METHODS: In total, 1,026 peripheral blood samples were collected. Schistocytes were morphologically diagnosed by manual examination of digital microscopic red blood cell images captured by a Sysmex DI-60. Automated diagnoses were performed using a Sysmex XN-3000. RESULT: The accuracy of automated diagnosis using the XN-3000 with the default algorithm "fragments?" was determined through comparison with the findings of morphological examination. The comparison showed a sensitivity of 100% and a specificity of 41.6% for automated diagnosis. To improve the low specificity, a two-step analysis was performed. Use of the algorithm "fragments?" in XN-3000 followed by an off-line analysis using the cell parameter %FRC (percent fragmented red blood cells) yielded a sensitivity of 86.5% and a specificity of 70.3%. CONCLUSIONS: Our study indicated that combined use of the %FRC parameter with the default algorithm of the Sysmex XN-3000 automated hematology analyzer can improve the low specificity of the default algorithm in rapid screening for schistocytes.

A novel automated image analysis system using deep convolutional neural networks can assist to differentiate MDS and AA.

Detection of dysmorphic cells in peripheral blood (PB) smears is essential in diagnostic screening of hematological diseases. Myelodysplastic syndromes (MDS) are hematopoietic neoplasms characterized by dysplastic and ineffective hematopoiesis, which diagnosis is mainly based on morphological findings of PB and bone marrow. We developed an automated diagnostic support system of MDS by combining an automated blood cell image-recognition system using a deep learning system (DLS) powered by convolutional neural networks (CNNs) with a decision-making system using extreme gradient boosting (XGBoost). The DLS of blood cell image-recognition has been trained using datasets consisting of 695,030 blood cell images taken from 3,261 PB smears including hematopoietic malignancies. The DLS simultaneously classified 17 blood cell types and 97 morphological features of such cells with >93.5% sensitivity and >96.0% specificity. The automated MDS diagnostic system successfully differentiated MDS from aplastic anemia (AA) with high accuracy; 96.2% of sensitivity and 100% of specificity (AUC 0.990). This is the first CNN-based automated initial diagnostic system for MDS using PB smears, which is applicable to develop new automated diagnostic systems for various hematological disorders.

Evaluation of cell count and classification capabilities in body fluids using a fully automated Sysmex XN equipped with high-sensitive Analysis (hsA) mode and DI-60 hematology analyzer system.

The XN series automated hematology analyzer has been equipped with a body fluid (BF) mode to count and differentiate leukocytes in BF samples including cerebrospinal fluid (CSF). However, its diagnostic accuracy is not reliable for CSF samples with low cell concentration at the border between normal and pathologic level. To overcome this limitation, a new flow cytometry-based technology, termed "high sensitive analysis (hsA) mode," has been developed. In addition, the XN series analyzer has been equipped with the automated digital cell imaging analyzer DI-60 to classify cell morphology including normal leukocytes differential and abnormal malignant cells detection. Using various BF samples, we evaluated the performance of the XN-hsA mode and DI-60 compared to manual microscopic examination. The reproducibility of the XN-hsA mode showed good results in samples with low cell densities (coefficient of variation; % CV: 7.8% for 6 cells/µL). The linearity of the XN-hsA mode was established up to 938 cells/µL. The cell number obtained using the XN-hsA mode correlated highly with the corresponding microscopic examination. Good correlation was also observed between the DI-60 analyses and manual microscopic classification for all leukocyte types, except monocytes. In conclusion, the combined use of cell counting with the XN-hsA mode and automated morphological analyses using the DI-60 mode is potentially useful for the automated analysis of BF cells.

Novel flowcytometry-based approach of malignant cell detection in body fluids using an automated hematology analyzer.

Morphological microscopic examinations of nucleated cells in body fluid (BF) samples are performed to screen malignancy. However, the morphological differentiation is time-consuming and labor-intensive. This study aimed to develop a new flowcytometry-based gating analysis mode "XN-BF gating algorithm" to detect malignant cells using an automated hematology analyzer, Sysmex XN-1000. XN-BF mode was equipped with WDF white blood cell (WBC) differential channel. We added two algorithms to the WDF channel: Rule 1 detects larger and clumped cell signals compared to the leukocytes, targeting the clustered malignant cells; Rule 2 detects middle sized mononuclear cells containing less granules than neutrophils with similar fluorescence signal to monocytes, targeting hematological malignant cells and solid tumor cells. BF samples that meet, at least, one rule were detected as malignant. To evaluate this novel gating algorithm, 92 various BF samples were collected. Manual microscopic differentiation with the May-Grunwald Giemsa stain and WBC count with hemocytometer were also performed. The performance of these three methods were evaluated by comparing with the cytological diagnosis. The XN-BF gating algorithm achieved sensitivity of 63.0% and specificity of 87.8% with 68.0% for positive predictive value and 85.1% for negative predictive value in detecting malignant-cell positive samples. Manual microscopic WBC differentiation and WBC count demonstrated 70.4% and 66.7% of sensitivities, and 96.9% and 92.3% of specificities, respectively. The XN-BF gating algorithm can be a feasible tool in hematology laboratories for prompt screening of malignant cells in various BF samples.

"Aged sample" software on automated routine hematology analyzer enables differentiation between pathological and non-pathological WBC flagging in aging samples.

BACKGROUND: Storing K(x)EDTA-conjugated blood samples at room temperature or under insufficient cooling conditions results in various morphological changes such as swelling of the blood cells. These changes are reproducible and have already been described well. However, they can lead to incorrect flagging when using automated hematology analyzers for complete blood counts and white blood cell differentials. The aim of this study was to determine if those changes can be detected automatically and used to prevent false positive flagging. METHODS: 150 blood samples were aged under controlled conditions and the impact on the "Aged sample" software was checked retrospectively. The results were verified in a second retrospective study including 6288 routine samples. RESULTS: When tested in a routine laboratory, the "Aged sample" software was able to reduce overall flagging by 23% without increasing false negative flagging. CONCLUSIONS: The "Aged sample" software of XN-Series analyzers does not only detect and flag samples that are aging or were stored under suboptimal conditions but also prevents false positive flagging.