Diagnosis of Acute Leukemia in Under-Resourced Laboratories

Abdulsalam, Abbas Hashim

doi:10.5772/19800

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…The analysis of acute leukemia immunophenotypic done by flow cytometry, this method recently become a tool for distinguishing between myeloid and lymphoid lineages. Several types of acute leukemias identified morphologically with or without enzyme cytochemical analysis, but immunophenotyping stay a vital for the right identification of myeloid lineages in minimally differentiated acute myeloid leukemia (AML-M0) and the differentiation of B or T-cell lineages in acute lymphoblastic leukemia (ALL) 2 . Flow cytometry (FC) is a newer technology that can be used to detect many CD markers in or on a single malignant cell, and also possesses multi-parametric capabilities for combining the study of physical characteristics, such as cell size and granularity, alongside multiple CD markers.…”

Section: Introductionmentioning

confidence: 99%

Characterization of flow cytometric immuno-phenotyping of acute myeloid leukemia with minimal differentiation and acute T-cell lymphoblastic leukemia: A retrospective cross-sectional study

2020

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Background: Acute leukemias (ALs) are a heterogeneous group of malignancies with various clinical, morphological, immunophenotypic, and molecular characteristics. Distinguishing between lymphoid and myeloid leukemia is often performed by flow cytometry. This study aimed to evaluate the immunophenotypic characterization and expression of immuno-markers in both acute myeloid leukemia (AML-M0) and acute T-cell lymphoblastic leukemia (T-ALL). Methods: A retrospective cross-sectional study was conducted in the Pathology Department/Teaching Laboratories/Medical City/Iraq and included all patients newly diagnosed with AL from 5 January to 10 December 2018. Immunophenotypic analysis was performed on bone marrow samples, freshly collected in EDTA tubes. Flow cytometry (Canto-2 BD) was used, with laser excitation of blue and red wavelengths. A panel of monoclonal antibodies (MoAbs) was used for diagnosis, using a SSC/CD45 gating strategy. Results: The study showed 41.6% of AML-M0 patients had no aberrant antigen expression, while 33.3%, 16.6%, 8.3%, and 8.3% had aberrant CD7, CD56, CD2, and CD19, respectively. In 16.6% of AML-M0 cases more than one aberrant antigen was expressed. With regard to T-ALL, 7.0% were pro-T type, 58.0% were pre-T, 13.0% were cortical, and 22.0% were mature-T type. In 55.5% of patients with T-ALL there was no aberrant antigen expression. Conclusion: We concluded that most patients with AML-M0 have no aberrant antigen expression. In patients with T-ALL, the pre-T type is most common, according to the European Group for the Immunological Classification of Leukemias (EGIL) classification. Patients with T-ALL also generally lack aberrant antigen expression.

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Section: Introductionmentioning

confidence: 99%

Characterization of flow cytometric immuno-phenotyping of acute myeloid leukemia with minimal differentiation and acute T-cell lymphoblastic leukemia: A retrospective cross-sectional study

2020

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Matlab Based Potent Algorithm for WBc cancer Detection and classification

Nithyaa¹,

R²,

et al. 2021

Biomed. Pharmacol. J.

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This paper aims to automate the detection of cancer using digital image processing techniques in MATLAB software. The analysis of white blood cells (WBC) is a powerful diagnostic tool for the prediction of Leukemia. The automatic detection of leukemia is a challenging task, which remains an unresolved problem in the medical imaging field. This Automation in Biological laboratories can be done by extracting the features of the blood film images taken from the digital microscopes and processed using MATLAB software. The aim of this approach is to discover the WBC cancer cells in an earlier stage and to reduce the discrepancies in diagnosis, by improving the system learning methodology. This paper presents the potent algorithm, which will eliminate the dubiety, in diagnosing the cancers with similar symptoms. This Algorithm concentrates on major WBC cancers, such as Acute Lymphocytic Leukemia, Acute Myeloid Leukemia, Chronic Lymphocytic Leukemia and Chronic Myeloid Leukemia. As they are life threatening diseases, rapid and precise differentiation is necessary in clinical settings. These cancers are categorized by segmentation and feature extraction, which will be further, classified using Random forest classification (RFC). RFC will classify the cancer using a decision tree learning method, which uses predictors at each node to make better decision.

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Diagnosis of Acute Leukemia in Under-Resourced Laboratories

Cited by 2 publications

References 19 publications

Characterization of flow cytometric immuno-phenotyping of acute myeloid leukemia with minimal differentiation and acute T-cell lymphoblastic leukemia: A retrospective cross-sectional study

Characterization of flow cytometric immuno-phenotyping of acute myeloid leukemia with minimal differentiation and acute T-cell lymphoblastic leukemia: A retrospective cross-sectional study

Matlab Based Potent Algorithm for WBc cancer Detection and classification

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