Evaluation of the detection of blasts by Sysmex hematology instruments, CellaVision DM96, and manual microscopy using flow cytometry as the confirmatory method

Eilertsen, Heidi; Saether, Per C.; Henriksson, Carola E.; Petersen, Anne‐Sofie; Hagve, Tor‐Arne

doi:10.1111/ijlh.12980

Cited by 10 publications

(8 citation statements)

References 23 publications

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“…Improvements in hematologic analysis technology have enabled rapid and highly accurate results for clinicians. The emergence of automatic blood smear preparations and microscopy examination units has also simplified the manual slide review 3,4 . Artificial intelligence is projected to be widely used in medical laboratories 5,6 .…”

Section: Introductionmentioning

confidence: 99%

Establishment of improved review criteria for hematology analyzers in cancer hospitals

Wang

et al. 2020

Clinical Laboratory Analysis

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Background Although hematologic review criteria for general hospitals have been established, they may be insufficient for cancer hospitals. This study aimed to establish the appropriate review criteria for hematology analyzers in cancer hospitals. Methods A total of 1003 samples from our hospital were randomly selected for blood smear preparation and microscopic review. The review criteria of the International Consensus Group for Hematology Review (ICGH) and Chinese consensus group were used to obtain the review, true‐negative (TN), true‐positive (TP), false‐negative (FN), and false‐positive (FP) rates, as well as the triggered rules. Our review criteria were established by comparing flag or numeric value information of TP and FP samples, adjusting rules to obtain better efficiency, a low slide review rate, and an acceptable FN rate. Results Overall, 197 (19.64%) samples showed positive smear findings. Compared to the ICGH criteria, the slide review rate of the newly established criteria declined from 51.25% to 39.28%, and the TP and TN rates increased from 17.85% and 46.06% to 23.13% and 55.83%, respectively. The FN rate of the newly established criteria was 3.69%. Another set of samples used to validate the newly established criteria yielded the review, FN, and FP rates as 33.49%, 1.86%, and 25.58%, respectively. Conclusion The newly established review criteria for hematology analyzers enabled the prompt identification, smear, and further verification of doubtful specimens, without a significant increase in the workload, thus improving the efficiency of the review process. This study provided data support for other cancer hospitals to establish review criteria.

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Section: Introductionmentioning

confidence: 99%

Establishment of improved review criteria for hematology analyzers in cancer hospitals

Wang

et al. 2020

Clinical Laboratory Analysis

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“…However, the warning flags work sufficiently well with samples containing these critical cell populations. 38 According to the specific characteristics analyzed by each technology, the distributions that clusters of pathological cells assume provide interesting diagnostic indications in the face of particular blood disorders. The observation of scattergrams and scatterplots allows recognizing different types of leukemia (see Supplemental Material).…”

Section: Counting Anomalous Populationsmentioning

confidence: 99%

“…No instrument, however, currently provides a percentage and absolute count of neither blasts nor atypical lymphocytes, except for research purposes. However, the warning flags work sufficiently well with samples containing these critical cell populations 38 …”

Section: Abnormal Cell Flagging and Countingmentioning

confidence: 99%

Full‐field hemocytometry. Forty years of progress seen through Clinical and Laboratory Hematology and the International Journal of Laboratory Hematology

D’Onofrio

2021

Int J Lab Hematology

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The extraordinary advances in clinical hematology, biology, and oncology in the last decades would not have been possible without discovering how to identify and count the cells circulating in the blood. For centuries, scientists have used slides, counting chambers (hemocytometers), and diluting and staining solutions for this task. Then, automated hemocytometry began. This science, now linked to the daily routine of laboratory hematology, has completed an overwhelming path over a few decades. Our laboratories today operate with versatile multiparameter systems, ranging from complex single‐channel instruments to bulky continuous flow machines. In terms of clinical information obtained from a simple routine blood test, the full exploitation of their potential depends on the operators' imagination and courage. A comprehensive review of the scientific publications that have accompanied the development of hemocytometry from the 1950s to today would require entire volumes. More than seven hundred contributions that authors worldwide have published in Clinical and Laboratory Haematology until 2007 and then the International Journal of Laboratory Hematology are summarized. Such journals have represented and hopefully will continue to represent the privileged place of welcome for future scientific research in hemocytometry. Improved technologies, attention to quality, new reagents and electronics, information technology, and scientist talent ensure a more profound and deeper knowledge of cell properties: current laboratory devices measure and count even minor immature or pathological cell subpopulations. Full‐field hemocytometry includes the analysis of nonhematic fluids, digital adds to the microscope, and the development of effective point‐of‐care devices.

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“…Such limitations could be mitigated by reliable automatic support of the initial diagnosis and categorization of cytology images, which are less technology dependent and more accessible. However, PB cytology image analysis is still inaccurate and a moderate agreement between flow cytometry and blast microscopic examination has been reported (Kappa = 0.499) [24,25]. Likewise, the need for more objective, robust and reproducible quantification of blast patterns becomes more evident.…”

Section: Introductionmentioning

confidence: 99%

Intra‐nucleus mosaic pattern (InMop) and whole‐cell Haralick combined‐descriptor for identifying and characterizing acute leukemia blasts on single cell peripheral blood images

et al. 2023

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Acute leukemia is usually diagnosed when a test of peripheral blood shows at least 20% of abnormal immature cells (blasts), a figure even lower in case of recurrent cytogenetic abnormalities. Blast identification is crucial for White Blood Cell (WBC) Counting, which depends on both identifying the cell type and characterizing the cellular morphology, processes susceptible of inter‐ and intraobserver variability. The present work introduces an image combined‐descriptor to detect blasts and determine their probable lineage. This strategy uses an Intra‐nucleus Mosaic pattern (InMop) descriptor that captures subtle nuclei differences within WBCs, and Haralick's statistics which quantify the local structure of both nucleus and cytoplasm. The InMop captures WBC inner‐nucleus structure by applying a multiscale Shearlet decomposition over a repetitive pattern (mosaic) of automatically‐segmented nuclei. As a complement, Haralick's statistics characterize the local structure of the whole cell from an intensity co‐occurrence matrix representation. Both InMoP and Haralick‐based descriptors are calculated using the b‐channel from Lab color‐space. The combined‐descriptor is assessed by differentiating blasts from non‐leukemic cells with support vector machine (SVM) classifiers and different transformation kernels, in two public and independent databases. The first database‐D1 (n=260) is composed of healthy and Acute Lymphoid Leukemia (ALL) single cell images, and second database‐D2 contains Acute Myeloid Leukemia (AML) blasts (n=3,294) and non‐blast (n=15,071) cell images. In a first experiment, blasts vs non‐blast differentiation is performed by training with a subset of D2 (n=6,588) and testing in D1 (n=260), obtaining a training AUC of 0.991±0.002 and AUC=0.782 for the independent validation. A second experiment automatically differentiates AML blasts (260 images from D2) from ALL blasts (260 images from D1), with an AUC of 0.93. In a third experiment, state‐of‐the‐art strategies, VGG16 and RESNEXT convolutional neural networks (CNN), separate blast from non‐blast cells in both databases. The VGG16 showed an AUC of 0.673 and the RESNEXT of 0.75. Reported metrics for all the experiments are area under the ROC curve (AUC), accuracy and F1‐score.This article is protected by copyright. All rights reserved.

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Evaluation of the detection of blasts by Sysmex hematology instruments, CellaVision DM96, and manual microscopy using flow cytometry as the confirmatory method

Cited by 10 publications

References 23 publications

Establishment of improved review criteria for hematology analyzers in cancer hospitals

Establishment of improved review criteria for hematology analyzers in cancer hospitals

Full‐field hemocytometry. Forty years of progress seen through Clinical and Laboratory Hematology and the International Journal of Laboratory Hematology

Intra‐nucleus mosaic pattern (InMop) and whole‐cell Haralick combined‐descriptor for identifying and characterizing acute leukemia blasts on single cell peripheral blood images

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