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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 23240

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Dr. Eloisa Urrechaga

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Haematology Laboratory, Hospital Galdakao—Usansolo, 48960 Galdakao, Vizcaya, Spain
Interests: medicine laboratory; clinical analysis technology; robotics in hematology; iron and anemia; erythropoiesis; glycohemoglobin; infection Inflammation; health
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Dear Colleagues,

Complete blood counts (CBC) and leukocyte differential are among the most frequently requested laboratory tests. These analyses are highly automated, and the evolution of automation in the hematology laboratory has enabled not only high throughput, greater reliability, and accuracy in the results, but as automated hematology systems become more sophisticated with the introduction of new physical principles for cellular analysis, also a generation of new parameters to characterize blood cells in more detail.Several parameters have been introduced to CBC, such as nucleated red blood cells, immature granulocytes, immature platelet fraction, as well as new parameters for the detection of ineffective erythropoiesis (reticulocyte hemoglobin, red cell subsets). Leucocyte morphometric parameters (cell population data) aid in the preliminary diagnosis of diseases (i.e., differentiating between abnormal lymphocytes in leukemia and viral conditions) and could be useful to ascertain infection and sepsis, and the discrimination of the etiology.

Different technologies can be used to enumerate platelets, and modern counters can provide not only platelet count, but also derived indices relating to the morphology, size or cytoplasm complexity, related to their functional activity.

The primary aim of this Special Issue is to update the information on the new parameters reported along with CBC and leukocyte differential to review the possible clinical applications of these parameters provided by modern hematologic instruments.

Dr. Eloisa Urrechaga
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

CBC count
leukocyte differential
reticulocyte indices
immature platelet fraction
cell population data
blood cell analyzers

Published Papers (7 papers)

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Research

11 pages, 1959 KiB

Open AccessArticle

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

by Rana Zeeshan Haider, Ikram Uddin Ujjan, Najeed Ahmed Khan, Eloisa Urrechaga and Tahir Sultan Shamsi

Diagnostics 2022, 12(1), 138; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12010138 - 07 Jan 2022

Cited by 8 | Viewed by 2950

Abstract

A targeted and timely treatment can be a beneficial tool for patients with hematological emergencies (particularly acute leukemias). The key challenges in the early diagnosis of leukemias and related hematological disorders are their symptom-sharing nature and prolonged turnaround time as well as the expertise needed in reporting confirmatory tests. The present study made use of the potential morphological and immature fraction-related parameters (research items or cell population data) generated during complete blood cell count (CBC), through artificial intelligence (AI)/machine learning (ML) predictive modeling for early (at the pre-microscopic level) differentiation of various types of leukemias: acute from chronic as well as myeloid from lymphoid. The routine CBC parameters along with research CBC items from a hematology analyzer in the diagnosis of 1577 study subjects with hematological neoplasms were collected. The statistical and data visualization tools, including heat-map and principal component analysis (PCA,) helped in the evaluation of the predictive capacity of research CBC items. Next, research CBC parameter-driven artificial neural network (ANN) predictive modeling was developed to use the hidden trend (disease’s signature) by increasing the auguring accuracy of these potential morphometric parameters in differentiation of leukemias. The classical statistics for routine and research CBC parameters showed that as a whole, all study items are significantly deviated among various types of leukemias (study groups). The CPD parameter-driven heat-map gave clustering (separation) of myeloid from lymphoid leukemias, followed by the segregation (nodding) of the acute from the chronic class of that particular lineage. Furthermore, acute promyelocytic leukemia (APML) was also well individuated from other types of acute myeloid leukemia (AML). The PCA plot guided by research CBC items at notable variance vindicated the aforementioned findings of the CPD-driven heat-map. Through training of ANN predictive modeling, the CPD parameters successfully differentiate the chronic myeloid leukemia (CML), AML, APML, acute lymphoid leukemia (ALL), chronic lymphoid leukemia (CLL), and other related hematological neoplasms with AUC values of 0.937, 0.905, 0.805, 0.829, 0.870, and 0.789, respectively, at an agreeably significant (10.6%) false prediction rate. Overall practical results of using our ANN model were found quite satisfactory with values of 83.1% and 89.4.7% for training and testing datasets, respectively. We proposed that research CBC parameters could potentially be used for early differentiation of leukemias in the hematology–oncology unit. The CPD-driven ANN modeling is a novel practice that substantially strengthens the predictive potential of CPD items, allowing the clinicians to be confident about the typical trend of the “disease fingerprint” shown by these automated potential morphometric items. Full article

(This article belongs to the Special Issue Advances in Hematology Laboratory)

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12 pages, 4554 KiB

Open AccessArticle

A Novel Algorithm Using Cell Population Data (VCS Parameters) as a Screening Discriminant between Alpha and Beta Thalassemia Traits

by Angeli Ambayya, Santina Sahibon, Thoo Wei Yang, Qian-Yun Zhang, Rosline Hassan and Jameela Sathar

Diagnostics 2021, 11(11), 2163; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11112163 - 22 Nov 2021

Cited by 3 | Viewed by 1916

Abstract

Thalassemia is one of the major inherited haematological disorders in the Southeast Asia region. This study explored the potential utility of red blood cell (RBC) parameters and reticulocyte cell population data (CPD) parameters in the differential diagnosis of α and β-thalassaemia traits as a rapid and cost-effective tool for screening of thalassemia traits. In this study, a total of 1597 subjects (1394 apparently healthy subjects, 155 subjects with α-thalassaemia trait, and 48 subjects with β-thalassaemia trait) were accrued. The parameters studied were the RBC parameters and reticulocyte CPD parameters derived from Unicel DxH800. A novel algorithm named αβ-algorithm was developed: (MN-LMALS-RET × RDW) − MCH) to discriminate α from β-thalassaemia trait with a cut-off value of 1742.5 [AUC = 0.966, sensitivity = 92%, specificity = 90%, 95% CI = 0.94–0.99]. Two prospective studies were carried: an in-house cohort to assess the specificity of this algorithm in 310 samples comprising various RBC disorders and in an interlaboratory cohort of 65 α-thalassemia trait, and 30 β-thalassaemia trait subjects to assess the reproducibility of the findings. We propose the αβ-algorithm to serve as a rapid, inexpensive surrogate evaluation tool of α and β-thalassaemia in the population screening of thalassemia traits in geographic regions with a high burden of these inherited blood disorders. Full article

(This article belongs to the Special Issue Advances in Hematology Laboratory)

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12 pages, 1135 KiB

Open AccessArticle

Evaluation of the New Beckmann Coulter Analyzer DxH 900 Compared to Sysmex XN20: Analytical Performance and Flagging Efficiency

by Maite Serrando Querol, Javier Nieto-Moragas, Anna Marull Arnall, Meritxell Deulofeu Figueras and Orlando Jiménez-Romero

Diagnostics 2021, 11(10), 1756; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11101756 - 24 Sep 2021

Cited by 4 | Viewed by 3219

Abstract

Efficiency and accuracy in automated hematology analyzers are very important for clinical laboratories. The purpose was to evaluate the flags and results reported by the newest Beckman Coulter analyzer DxH 900 compared to the Sysmex XN20 system. Samples were analyzed on the XN20 (Sysmex, Kobe, Japan) and on the Beckman Coulter DxH 900 (Beckman Coulter, Miami, Florida, USA). Slide reviews were performed microscopically. Morphologic criteria were used to identify abnormal cells as recommended by International Consensus Group for Hematology (ICSH): blasts, immature granulocytes (IG%), abnormal lymphocytes (ALs) and plasma cells. Results: there was a strong correlation between the analyzers in almost all clinical parameters tested. Both DxH 900 and XN20 showed an excellent degree of association for the leukocyte differential compared to the reference method (manual microscopy). When it comes to IG%, XN20 showed a positive bias for higher results. Related to platelets, there are no differences between the two methods for PLT count. For mean platelet volume (MPV), DxH 900 provided 100% results of the samples analyzed while XN20 while in the XN20 analyzer, 16% of the results were missing. From our results we came to the conclusion that both analyzers, DxH 900 and XN20 were clinically accurate and efficient. Abnormal Lymphocyte detection highlighted the differences between the two technologies as only minimal agreement was obtained. DxH 900 demonstrated higher sensitivity in detecting IG with good correlation with microscopic review. The DxH 900 for platelet clumps identification provides an excellent flag (PLT Clumps) with the highest sensitivity observed in our evaluation. Full article

(This article belongs to the Special Issue Advances in Hematology Laboratory)

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10 pages, 682 KiB

Open AccessArticle

The TVGH-NYCU Thal-Classifier: Development of a Machine-Learning Classifier for Differentiating Thalassemia and Non-Thalassemia Patients

by Yi-Kai Fu, Hsueng-Mei Liu, Li-Hsuan Lee, Ying-Ju Chen, Sheng-Hsuan Chien, Jeong-Shi Lin, Wen-Chun Chen, Ming-Hsuan Cheng, Po-Heng Lin, Jheng-You Lai, Chyong-Mei Chen and Chun-Yu Liu

Diagnostics 2021, 11(9), 1725; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11091725 - 20 Sep 2021

Cited by 13 | Viewed by 3262

Abstract

Thalassemia and iron deficiency are the most common etiologies for microcytic anemia and there are indices discriminating both from common laboratory simple automatic counters. In this study a new classifier for discriminating thalassemia and non-thalassemia microcytic anemia was generated via combination of exciting indices with machine-learning techniques. A total of 350 Taiwanese adult patients whose anemia diagnosis, complete blood cell counts, and hemoglobin gene profiles were retrospectively reviewed. Thirteen prior established indices were applied to current cohort and the sensitivity, specificity, positive and negative predictive values were calculated. A support vector machine (SVM) with Monte-Carlo cross-validation procedure was adopted to generate the classifier. The performance of our classifier was compared with original indices by calculating the average classification error rate and area under the curve (AUC) for the sampled datasets. The performance of this SVM model showed average AUC of 0.76 and average error rate of 0.26, which surpassed all other indices. In conclusion, we developed a convenient tool for primary-care physicians when deferential diagnosis contains thalassemia for the Taiwanese adult population. This approach needs to be validated in other studies or bigger database. Full article

(This article belongs to the Special Issue Advances in Hematology Laboratory)

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13 pages, 1283 KiB

Open AccessArticle

Neoteric Algorithm Using Cell Population Data (VCS Parameters) as a Rapid Screening Tool for Haematological Disorders

by Angeli Ambayya, Jameela Sathar and Rosline Hassan

Diagnostics 2021, 11(9), 1652; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11091652 - 09 Sep 2021

Cited by 1 | Viewed by 3263

Abstract

Hitherto, there has been no comprehensive study on the usefulness of cell population data (CPD) parameters as a screening tool in the discrimination of non-neoplastic and neoplastic haematological disorders. Hence, we aimed to develop an algorithm derived from CPD parameters to enable robust screening of neoplastic from non-neoplastic samples and subsequently to aid in differentiating various neoplastic haematological disorders. In this study, the CPD parameters from 245 subtypes of leukaemia and lymphoma were compared against 1103 non-neoplastic cases, and those CPD parameters that were vigorous discriminants were selected for algorithm development. We devised a novel algorithm: [(SD-V-NE*MN-UMALS-LY*SD-AL2-MO)/MN-C-NE] to distinguish neoplastic from non-neoplastic cases. Following that, the single parameter MN-AL2-NE was used as a discriminant to rule out reactive cases from neoplastic cases. We then assessed CPD parameters that were useful in delineating leukaemia subtypes as follows: AML (SD-MALS-NE and SD-UMALS-NE), APL (MN-V-NE and SD-V-MO), ALL (MN-MALS-NE and MN-LMALS-NE) and CLL (SD-C-MO). Prospective studies were carried out to validate the algorithm and single parameter, MN-AL2-NE. We propose these CPD parameter-based discriminant strategies to be adopted as an initial screening and flagging system in the preliminary evaluation of leukocyte morphology. Full article

(This article belongs to the Special Issue Advances in Hematology Laboratory)

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8 pages, 1035 KiB

Open AccessArticle

Mature and Immature/Activated Cells Fractionation: Time for a Paradigm Shift in Differential Leucocyte Count Reporting?

by Rana Zeeshan Haider, Najeed Ahmed Khan, Eloisa Urrechaga and Tahir Sultan Shamsi

Diagnostics 2021, 11(6), 922; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11060922 - 21 May 2021

Cited by 1 | Viewed by 2989

Abstract

Leucocytes, especially neutrophils featuring pro- and anti-cancerous characteristics, are involved in nearly every stage of tumorigenesis. Phenotypic and functional differences among mature and immature neutrophil fractions are well reported, and their correlation with tumor progression and therapy has emerging implications in modern oncology practices. Technological advancements enabled modern hematology analyzers to generate extended information (research parameters) during complete blood cell count (CBC) analysis. We hypothesized that neutrophil and lymphocyte fractions-related extended differential leucocytes count (DLC) parameters hold superior diagnostic utility over routine modalities. The present study was carried out over a four-and-a-half-year period wherein extended neutrophil (immature granulocyte [IG] and mature neutrophil [NEUT#&]), and lymphocyte (activated/high fluorescence lymphocyte count [HFLC] and resting lymphocyte [LYMP#&]) parameters were challenged over routine neutrophil [NEUT#] and lymphocyte [LYMP#] items in a study population of 1067 hematological neoplasm patients. Extending the classical statistical approaches, machine-learning-backed data visualization was used to explore trends in the study parameters. As a whole, extended neutrophil and lymphocyte count outperformed and was diagnostically more relevant than routine neutrophil and lymphocyte parameters by showing the least difference from their respective (gold-standard) manual DLC counts. The mature neutrophil count was compared to IG, and resting lymphocyte count was compared to HFLC by calling the function ‘correlation’ as a ‘clustering function’ for heatmap based visualization. The aforementioned study parameters displayed close clustering (rearrangement) for their respective study items by presenting distinct trends of equally valuable weights (deviated values), advocating fractions-based extended DLC reporting. Importantly, using a Bland and Altman analysis analogously to a manual neutrophil count, the mature neutrophil count [NEUT#&] remained unbiased since a routine neutrophil count [NEUT#] was found to be a negatively biased. The extended DLC-parameter-driven fractions-based reporting has superior diagnostic utility over classical routine approaches; this finding can largely minimize labor-intensive manual DLC practices, especially in hematology–oncology departments. Full article

(This article belongs to the Special Issue Advances in Hematology Laboratory)

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11 pages, 9380 KiB

Open AccessArticle

MYD88^L265P Detection in IgM Monoclonal Gammopathies: Methodological Considerations for Routine Implementation

by Martina Ferrante, Daniela Furlan, Silvia Zibellini, Michela Borriero, Chiara Candido, Nora Sahnane, Silvia Uccella, Elisa Genuardi, Beatrice Alessandria, Benedetta Bianchi, Barbara Mora, Daniele Grimaldi, Irene Defrancesco, Cristina Jiménez, Federica Cavallo, Dario Ferrero, Irene Dogliotti, Michele Merli, Marzia Varettoni, Simone Ferrero and Daniela Drandi Show full author list Hide full author list

Diagnostics 2021, 11(5), 779; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11050779 - 26 Apr 2021

Cited by 14 | Viewed by 2760

Abstract

In IgM monoclonal gammopathies MYD88^L265P is a prognostic and predictive biomarker of therapy response. MYD88^L265P detection is mainly performed by allele-specific quantitative PCR (ASqPCR), however recently, droplet digital PCR (ddPCR) has been proved to be suitable for MYD88^L265P screening and minimal residual disease monitoring (MRD). This study compared ASqPCR and ddPCR to define the most sensitive method for MYD88^L265P detection in bone marrow (BM), peripheral blood (PB) sorted or unsorted CD19+ cells, and in plasma cell-free DNA (cfDNA). Overall, the analysis showed a good concordance rate (74%) between the two methods, especially in BM samples, while discordances (26%) were mostly in favor of ddPCR (ddPCR+ vs. ASqPCR-) and were particularly evident in samples with low mutational burden, such as PB and cfDNA. This study highlights ddPCR as a feasible approach for MYD88^L265P detection across different specimen types (including cfDNA). Interestingly, its high sensitivity makes CD19+ selection dispensable. On the other hand, our results showed that MYD88^L265P detection on PB samples, especially with ASqPCR, is suboptimal for screening and MRD analysis. Finally, significantly different MYD88^L265P mutational levels observed between Waldenström Macroglobulinemia and IgM monoclonal gammopathy of undetermined significance patients suggest the need for further studies in order to identify possible correlations between mutational levels and risk of progression to Waldenström. Full article

(This article belongs to the Special Issue Advances in Hematology Laboratory)

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