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Acute leukemia is a proliferation of immature bone marrow-derived cells (blasts) that may
also involve peripheral blood or solid organs . The percentage of bone marrow blast cells required for a diagnosis of acute leukemia has traditionally been set arbitrarily at 30% or more.
However, more recently proposed classification systems have lowered th...
Citations
... Leukemia, a complex group of disorders consisting of myelogenous and lymphocytic types with acute and chronic subtypes, has undergone significant changes in its classification. These changes involve the inclusion of genetic and immunologic features in addition to morphology, resulting in a more comprehensive understanding of the disease and the role of genetic mutations [1][2][3]. Immunophenotyping, cytogenetics, and molecular analysis have become essential in the diagnosis and classification of leukemia [4,5], enhancing our ability to accurately categorize the disease for targeted treatments. ...
Background: Recent research has identified alternative transcript variants of LMNA/C (LMNA, LMNC, LMNAΔ10, and LMNAΔ50) and insulin receptors (INSRs) as potential biomarkers for various types of cancer. The objective of this study was to assess the expression of LMNA/C and INSR transcript variants in peripheral blood mononuclear cells (PBMCs) of leukemia patients to investigate their potential as diagnostic biomarkers. Methods: Quantitative TaqMan reverse transcriptase polymerase chain reaction (RT-qPCR) was utilized to quantify the mRNA levels of LMNA/C (LMNA, LMNC, LMNAΔ10, and LMNAΔ50) as well as INSR (IR-A and IR-B) variants in PBMCs obtained from healthy individuals (n = 32) and patients diagnosed with primary leukemias (acute myeloid leukemia (AML): n = 17; acute lymphoblastic leukemia (ALL): n = 8; chronic myeloid leukemia (CML): n = 5; and chronic lymphocytic leukemia (CLL): n = 15). Results: Only LMNA and LMNC transcripts were notably present in PBMCs. Both exhibited significantly decreased expression levels in leukemia patients compared to the healthy control group. Particularly, the LMNC:LMNA ratio was notably higher in AML patients. Interestingly, IR-B expression was not detectable in any of the PBMC samples, precluding the calculation of the IR-A:IR-B ratio as a diagnostic marker. Despite reduced expression across all types of leukemia, IR-A levels remained detectable, indicating its potential involvement in disease progression. Conclusions: This study highlights the distinct expression patterns of LMNA/C and INSR transcript variants in PBMCs of leukemia patients. The LMNC:LMNA ratio shows promise as a potential diagnostic indicator for AML, while further research is necessary to understand the role of IR-A in leukemia pathogenesis and its potential as a therapeutic target.
... 6) Detection of multiple lobes in the WBC nuclei: The promyelocytes are responsible for Acute Promyelocytic Leukemia, a subtype of AML (AML-M3). The cytoplasm of the cell has granules that are stained red to purple and the nuclei of the cell are bilobed to multiple lobed [59]. The presence of numerous lobes in nuclei and primary granules in the cytoplasm helps to differentiate the atypical promyelocyte from the other cells (Myeloblast, Promonocyte, Monoblast, and NRBC) [60]. ...
Acute myeloid leukemia (AML) is a cancer of the myeloid line of cells caused due to the rapid increase of abnormal cells that later interfere with healthy cells. One of the main reasons for the increase in mortality is the cost of the devices used for the determination and late diagnosis. The most effective treatment option can be provided by accurate medical diagnosis. Automated segmentation of blood smear images plays a crucial role in the identification of the AML. This article proposes a new computer-aided diagnosis model to segment the blood smear images and identifies the stage of AML. The methodology presented in this work consists of various stages: Image acquisition, image segmentation, feature extraction/selection, and classification. The model is trained using 800 blood smear images collected from Kasturba Medical College Manipal, and 200 images collected from the dataset of microscopic peripheral blood cell images for the development of automatic recognition systems. The model is tested on 500 images. A novel algorithm is designed to accurately segment the blood smear images to identify AML and its stages. The segmentation algorithm addresses critical issues in blast cell detection, including identifying the blast cell and extracting the cytoplasm of the cell without involving manual intervention. It can identify the multiple lobes and the nucleated red blood cells (NRBCs), separate the overlapped erythrocytes from white blood cells (WBCs), and discover the presence of Auer rods and granules. The feature selection is performed using the InfoGainAttributeEval and the ranker search method. This article compares the performance of the various machine learning algorithms exploited for the classification of different types of cells and hence determines AML. The model successfully differentiated between NRBC and WBC with an accuracy of 99.81%. The model obtained a classification accuracy of 99.48 %. It achieved a prediction accuracy of 99.2% while predicting the unknown stage of AML. The algorithm’s efficiency proves that it can be used by pathologists to form a prognosis.
... Acute lymphoblastic leukaemia is the commonest childhood leukaemia, this was the case in this study (70%), and in previous reports, though AML was more prevalent in some African series [5][6][7]14]. Though useful in making a preliminary diagnosis of acute leukaemias, morphology has its limitations and is subject to both intra-and inter-observer differences [15]. Thus, there may be misdiagnosis of acute leukaemias if further investigations such as immunophenotyping and cytogenetic analysis are not done. ...
Introduction: Acute leukaemias are the most common malignant neoplasms in childhood, presenting with a variety of nonspecific symptoms. Though many of the recent more sophisticated methods of diagnosis have important prognostic implications, they are often not available in low- and middle-income countries. Objective: To review the full blood count and bone marrow aspirations at presentation in children diagnosed with acute leukaemias at a teaching hospital in southern Nigeria. Methodology: A retrospective survey of children with acute leukaemias admitted into the Paediatric Oncology unit of the University of Port Harcourt Teaching Hospital (UPTH), from January 2014 to December 2020. Their clinical profile, full blood count and bone marrow aspirations were analyzed using SPSS version 25.0 Results: Forty-three children aged 8 months to 17 years, with a median age of 9 years, were diagnosed with acute leukaemia within the period under review, 28 (65.1%) were males and 15 (34.9%) females, giving a M:F ratio of 1.9:1. Commonest clinical features at presentation were fever (n=28, 65.1%), pallor (n=18, 41.9%) and gum bleeding (n=16, 37.2%); while 38 (88.4%) of them presented with anaemia, 20 (46.5%) had leukocytosis and 36 (83.7%) had thrombocytopoenia with a median platelet count of 42x109/L and circulating blasts were present in the peripheral blood film of most of the patients. Acute lymphoblastic leukaemia (ALL) was the diagnosis in 30 (70%) children, and AML in 9 (21%). The bone marrow was hypercellular in 30 cases (69.8%) and erythropoiesis was depressed in 39 (90.7%) children. Conclusion: At the UPTH, children with acute leukaemias were mostly males. Fever, pallor and gum bleeding were the commonest symptoms with most of them having circulating blasts. Acute lymphoblastic leukaemia was the commonest type and bone marrow was mainly hypercellular with depressed erythropoiesis.
... Abnormal leukocyte, thrombocytopenia and anemia are naturally present at diagnosis, indicating the degree to which leukemic lymphoblasts have replaced the bone marrow [16]. The first mark to an ALL diagnosis is typically an abnormal complete blood count result. ...
Background: Leukemias are classified as lymphoid or myeloid, dependent on the type of stem cell that is affected. In addition, leukemia is classified as chronic or acute. Acute leukemia is a production of bone marrow-derived immature cells (blasts), include solid organs or peripheral blood. The FAB Cooperative Group original classification scheme proposed to divide1 ALL into three subtypes (L1 - L3). Currently, the world health organization (WHO), modify FAB classification depending on immunophenotype. Symptoms presence of anemia, splenomegaly, and thrombocytopenia, and those are naturally present at diagnosis, indicating the degree to which leukemic lymphoblasts have replaced the bone marrow and the first mark to an ALL diagnosis is typically an abnormal complete blood count result. Objective: To introduce causes of acute lymphocytic leukemia, recent classification methods, diagnosis, and symptoms and diagnosis. Conclusion: Acute lymphocytic leukemia occurs due to a defect in the bone marrow and is classified into several types. The most important classification by the World Health Organization is depending on immunophenotype. The main symptoms are the increase in white blood cells with anemia and thrombocytopenia. Keywords: Acute Lymphoblastic Leukemia, Blood
... If not treated immediately, it can lead to death in a matter of weeks or even days. Furthermore, acute leukemia is divided into eight subtypes; they are M0, M1, M2, M3, M4, M5, M6, M7 [3] [4]. In some subtype of AML like M4, M5 dan M7 are affected by the same type of precursor cells. ...
Acute Myeloid Leukemia (AML) is one of cancer type that attack white blood cells in myeloid descendants. On the clinical examination of leukemia, the number of each blast cell in the laboratory is calculated. However, in some subtype of AML like M4, M5 dan M7 are affected by the same type of precursor cells. The precursor cell of them are myeloblast, monoblast and megakaryoblast, which needs more detailed analysis to distinguish. This research tries to help overcome the problem by doing cell type automatic classification from cells images. Classification is performed on cell types of precursors cells derived from bone marrow preparations. The stages that have been completed are preprocessing, segmentation, extraction and feature selection, and classification. Features used as input of classification stage are area, nucleus ratio, circularity, perimeter, mean, and standard deviation. The results showed the success rate of cell segmentation reached 87.72% of total 1710 cells. The support vector machine classification results in the best performance test data are achieved by Linear kernel. The performance was obtained by combining six features for eight cell types from the maturation of the three precursor cells. These cell types are myeloblast, promyelocyte, granulocyte, monoblast, promonocyte, monocyte, megakaryoblast and support cell with sequential accuracy of 98.67%, 98.01%, 84.05% 99.67%, 95.35%, 89.70%, 99.34% and 98.01% respectively.
... CD14 indicates myeloid lineage, is often positive in FAB M4 and M5. 52 Group '3' includes the largest proportion of positive staining NSE (nonspecific esterase), which indicates the presence of cells of monocytic origin, but can be positive across several FAB subtypes (www.pathologystudent.com). The KEGG Tuberculosis pathway depicts a macrophage-dendritic cell. ...
... CD14 is a marker for dendritic cell differentiation51 and presence of monocytes and macrophages. It indicates myeloid lineage, is often positive in FAB M4 and M5,52 and shorter survival in patients with secondary AML (non-de novo).59 Only group '2' and '3' members have any CD14 positive disease, and group '3' includes 80% of patients with this marker. ...
Acute Lymphocytic Leukemia (ALL) is a malignant hematological disease. It is also known as acute lymphoblastic leukemia. Being the most common type of childhood cancer, it requires prompt treatment to increase the chances of recovery. There are variant forms or ALL types in the diagnosis procedure, referred to as L1, L2, and L3. Hence, it is possible to apply effective treatment if leukemic cells are identified correctly, i.e., if their proper types are known. Clinical observations say that these subtypes have distinct geometric and color features. This work is mainly focused on classifying ALL type cells. We have used a novel combination of geometric and color-based features for efficient classification. Improved image enhancement and noise removal procedures are also employed as preprocessing. The proposed method has been tested on ALL-IDB data sets. The results corroborate the method’s usefulness in identifying ALL subtypes.KeywordsAcute lymphocytic leukemiaNucleoliSupport vector machine
Background
Hematological disorders are heterogeneous conditions ranging from malignant to non-malignant disorders. Hematological malignancies comprise a collection of heterogeneous conditions originating from cells of the bone marrow and the lymphatic system. Therefore, this study aimed to determine the pattern of bone marrow confirmed malignant and non-malignant hematological disorders in patients with abnormal hematological parameters.
Methods
Institutional-based cross-sectional study was conducted in Dessie town from April 2020 to June 2021. A total of 228 study participants who had abnormal hematological parameters and referred for bone marrow examination were included consecutively. About 1.5 mL of bone marrow sample and 3 mL of venous blood sample were collected for bone marrow examination, complete blood count analysis and peripheral blood morphology examination. Wright stain, Sudan black B, and Prussian blue stains were used for staining the bone marrow and peripheral blood smears. The result was expressed in mean and standard deviation and presented in texts and tables. Ratio, frequency, and percentage were used to express the magnitude of malignant and non-malignant hematological disorders.
Results
The overall prevalence of hematological malignancies among the study participants was 11.4% with 8.8% in male patients. The prevalence of hematological malignancies were 3.5% CML, 2.6% AML, 1.8% CLL and MM, 0.9% ALL and undifferentiated acute leukemia. On the other hand, 57.0% of the study participants had non-malignant hematological disorders. Regarding non-malignant hematological cases, 24.6% were erythroid hyperplasia, 10.5% aplastic anemia, 8.8% concomitant IDA and MBA, 7.0% MBA, 3.5% leukemoid reaction, 1.8% IDA, and 0.9% visceral leishmaniasis. In patients with HM, 66.7% of AML, 100% of CML and CLL, and 75% of MM patients had increased total WBC count, whereas 66.7% of AML, 62.5% of CML, 75% of CLL, and 50% of MM patients had decreased hemoglobin level. On the other hand, 66.7% of AML, and 50% of CML, ALL, and CLL patients had decreased platelet count.
Conclusion
In this study, 11.4% of the patients had hematological malignant cases, whereas 57% of the patients had non-malignant hematological cases. Therefore, in patients with hematological abnormalities and where conclusive diagnosis could not be made through clinical and other laboratory investigations, bone marrow examination should be done for definitive diagnosis, management and prognosis.
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.
Background:
The hematology analyzer, Sysmex XN-1000, generates white blood cell count with varying scattering intensities during a complete blood count (CBC) analysis.
Objectives:
The objectives of the study were to study the predictive role of median and coefficient of variation of neutrophil scattering items in blood samples for differentiation of leukemic subjects.
Methods:
We evaluated six neutrophil scattering parameters: neutrophil side scatter mean intensity, neutrophil side fluorescence light (SFL) mean intensity, neutrophil forward scatter mean intensity, neutrophil side scatter area distribution width (NE-WX), neutrophil SFL area distribution width (NE-WY), and neutrophil forward scatter area distribution width (NE-WZ), measured in white blood cell differential scattergram generated by the hematology analyzer (Sysmex XN-1000) at an academic medical center.
Results:
We collected 433 blood samples from acute myeloid leukemia (AML) and acute lymphoid leukemia (ALL) cases and normal controls. AML group showed highly significant differences in the mean values compared with the control group. Out of six neutrophil scattering items, NE-WX, NE-WY, and NE-WZ showed high efficiency, with area under the curve (AUC) values of 0.764, 0.748, and 0.757, respectively, to differentiate AML from ALL cases and control groups. When comparing combined acute leukemia cases (AML plus ALL) with the control group, NE-WX, NE-WY, and NE-WZ generated highly significant AUC values (0.840, 0.884, and 0.801, respectively).
Conclusion:
The neutrophil scattering parameters generated during CBC analysis provide a new tool for the prediction of acute leukemia and its lineage.
