Classification of blast cell type on acute myeloid leukemia (AML) based on image morphology of white blood cells

Background: Acute myeloid leukemia is the collective name for different types of leukemias of myeloid origin affecting blood and bone marrow. The overproduction of immature myeloblasts (white blood cells) is the characteristic feature of AML, thus flooding the bone marrow and reducing its capacity to produce normal blood cells. USFDA on August 1, 2017, approved a drug named Enasidenib formerly known as AG-221 which is being marketed under the name Idhifa to treat R/R AML with IDH2 mutation. The present review depicts the broad profile of enasidenib including various aspects of chemistry, preclinical, clinical studies, pharmacokinetics, mode of action and toxicity studies. Methods: Various reports and research articles have been referred to summarize different aspects related to chemistry and pharmacokinetics of enasidenib. Clinical data was collected from various recently published clinical reports including clinical trial outcomes. Result: The various findings of enasidenib revealed that it has been designed to allosterically inhibit mutated IDH2 to treat R/R AML patients. It has also presented good safety and efficacy profile along with 9.3 months overall survival rates of patients in which disease has relapsed. The drug is still under study either in combination or solely to treat hematological malignancies. Molecular modeling studies revealed that enasidenib binds to its target through hydrophobic interaction and hydrogen bonding inside the binding pocket. Enasidenib is found to be associated with certain adverse effects like elevated bilirubin level, diarrhea, differentiation syndrome, decreased potassium and calcium levels, etc. Conclusion: Enasidenib or AG-221was introduced by FDA as an anticancer agent which was developed as a first in class, a selective allosteric inhibitor of the tumor target i.e. IDH2 for Relapsed or Refractory AML. Phase 1/2 clinical trial of Enasidenib resulted in the overall survival rate of 40.3% with CR of 19.3%. Phase III trial on the Enasidenib is still under process along with another trial to test its potency against other cell lines. Edasidenib is associated with certain adverse effects, which can be reduced by investigators by designing its newer derivatives on the basis of SAR studies. Hence, it may come in the light as a potent lead entity for anticancer treatment in the coming years.

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Human-level recognition of blast cells in acute myeloid leukemia with convolutional neural networks

10.1101/564039 ◽

2019 ◽

Cited By ~ 1

Author(s):

Christian Matek ◽

Simone Schwarz ◽

Karsten Spiekermann ◽

Carsten Marr

Keyword(s):

Acute Myeloid Leukemia ◽

Blood Cells ◽

Myeloid Leukemia ◽

White Blood Cells ◽

Cell Types ◽

Hematologic Malignancies ◽

Malignant Cell ◽

Morphological Examination ◽

Blast Cells ◽

Acute Myeloid

AbstractReliable recognition of malignant white blood cells is a key step in the diagnosis of hematologic malignancies such as Acute Myeloid Leukemia. Microscopic morphological examination of blood cells is usually performed by trained human examiners, making the process tedious, time-consuming and hard to standardise.We compile an annotated image dataset of over 18,000 white blood cells, use it to train a convolutional neural network for leukocyte classification, and evaluate the network’s performance. The network classifies the most important cell types with high accuracy. It also allows us to decide two clinically relevant questions with human-level performance, namely (i) if a given cell has blast character, and (ii) if it belongs to the cell types normally present in non-pathological blood smears.Our approach holds the potential to be used as a classification aid for examining much larger numbers of cells in a smear than can usually be done by a human expert. This will allow clinicians to recognize malignant cell populations with lower prevalence at an earlier stage of the disease.

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Classification of acute myeloid leukemia subtypes M1, M2 and M3 using active contour without edge segmentation and momentum backpropagation artificial neural network

MATEC Web of Conferences ◽

10.1051/matecconf/201815401041 ◽

2018 ◽

Vol 154 ◽

pp. 01041 ◽

Cited By ~ 3

Author(s):

Agus Harjoko ◽

Tri Ratnaningsih ◽

Esti Suryani ◽

Wiharto ◽

Sarngadi Palgunadi ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Acute Myeloid Leukemia ◽

Blood Cells ◽

Myeloid Leukemia ◽

Active Contour ◽

White Blood Cells ◽

Artificial Neural ◽

Backpropagation Artificial Neural Network ◽

Acute Myeloid

Acute Myeloid Leukemia (AML) is a type of cancer which attacks white blood cells from myeloid. AML has eight subtypes, namely: M0, M1, M2, M3, M4, M5, M6, and M7. AML subtypes M1, M2 and M3 are affected by the same type of cells, myeloblast, making it needs more detailed analysis to distinguish. To overcome these obstacles, this research is applying digital image processing with Active Contour Without Edge (ACWE) and Momentum Backpropagation artificial neural network for AML subtypes M1, M2 and M3 classification based on the type of the cell. Six features required as training parameters from every cell obtained by using feature extraction. The features are: cell area, perimeter, circularity, nucleus ratio, mean and standard deviation. The results show that ACWE can be used for segmenting white blood cells with 83.789% success percentage of 876 total cell objects. The whole AML slides had been identified according to the cell types predicted number through training with momentum backpropagation. Five times testing calibration with the best parameter generated averages value of 84.754% precision, 75.887% sensitivity, 95.090% specificity and 93.569% accuracy.

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A Comparative Study of Digital Image Segmentation Algorithms for Acute Myeloid Leukemia M1 White Blood Cells Images

IJIE (Indonesian Journal of Informatics Education) ◽

10.20961/ijie.v4i2.48448 ◽

2021 ◽

Vol 4 (2) ◽

pp. 101

Author(s):

Nurcahya Pradana Taufik Prakisya ◽

Andika Setiawan

Keyword(s):

Acute Myeloid Leukemia ◽

Image Segmentation ◽

Digital Image ◽

Blood Cells ◽

Myeloid Leukemia ◽

Active Contour ◽

White Blood Cells ◽

Seed Region ◽

Segmentation Algorithms ◽

Acute Myeloid

Various types of algorithms have been widely used for image segmentation in digital image processing. Every algorithm has features that make it unique to be applied to specific cases. One of the applications of image segmentation is to detect white blood cells. Certain objects such as blood cells must be able to be well segmented because their existence is very crucial to support the accuracy of disease detection related to haematology or the branch of medical science that studies the morphology of blood and blood-forming tissues. Three image segmentation algorithms were compared through this study: Seed Region Growing, Otsu Thresholding and Active Contour Without Edge. Comparative analysis of the three algorithms was done by counting the number of white blood cell objects that were successfully segmented with the actual number of cells that were counted manually. A total of 30 images of blood smears were taken from people suffering from acute myeloid leukemia M1. The average accuracy values from each algorithm were used to determine which image segmentation algorithm is the most suitable for application in the case of white blood cells segmentation. The results showed that Active Contour Without Edge is the most appropriate among the other algorithms

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Altered Growth Characteristics of Cord Blood Cells after in vivo Exposure to Maternal Acute Myeloid Leukemia and Chemotherapy

Acta Haematologica ◽

10.1159/000086588 ◽

2005 ◽

Vol 114 (2) ◽

pp. 121-124

Author(s):

T. Fietz ◽

R. Arnold ◽

G. Massenkeil ◽

K. Rieger ◽

B. Reufi ◽

...

Keyword(s):

Acute Myeloid Leukemia ◽

Cord Blood ◽

Blood Cells ◽

Myeloid Leukemia ◽

Growth Characteristics ◽

Cord Blood Cells ◽

Acute Myeloid

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PF232 PROGNOSTIC IMPACT OF THE 2017 EUROPEAN LEUKEMIANET (ELN) CLASSIFICATION OF YOUNGER ADULT PATIENTS WITH ACUTE MYELOID LEUKEMIA (AML) TREATED ON CANCER AND LEUKEMIA GROUP B (CALGB)/ALLIANCE PROTOCOLS

HemaSphere ◽

10.1097/01.hs9.0000559144.15501.7c ◽

2019 ◽

Vol 3 (S1) ◽

pp. 68-69

Author(s):

M. Bill ◽

K. Mrózek ◽

A.-K. Eisfeld ◽

J. Kohlschmidt ◽

D. Nicolet ◽

...

Keyword(s):

Acute Myeloid Leukemia ◽

Myeloid Leukemia ◽

Adult Patients ◽

Prognostic Impact ◽

Group B ◽

Leukemia Group ◽

Acute Myeloid

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Impact of Molecular Sequencing Information as Related to 2008 and 2016 World Health Organization Classification of Acute Myeloid Leukemia and Myelodysplasia

Archives of Pathology & Laboratory Medicine ◽

10.5858/arpa.2017-0577-le ◽

2018 ◽

Vol 142 (9) ◽

pp. 1017-1017

Author(s):

Laura N. Toth ◽

Francine B. de Abreu ◽

Jason D. Peterson ◽

Eric Y. Loo

Keyword(s):

Acute Myeloid Leukemia ◽

World Health Organization ◽

Myeloid Leukemia ◽

World Health ◽

World Health Organization Classification ◽

Health Organization ◽

Molecular Sequencing ◽

Acute Myeloid

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Acute Leukemia

10.2310/im.1247 ◽

2016 ◽

Author(s):

Richard A. Larson ◽

Roland B Walter

Keyword(s):

Acute Myeloid Leukemia ◽

Acute Lymphoblastic Leukemia ◽

Acute Leukemia ◽

Myeloid Leukemia ◽

Who Classification ◽

Lymphoblastic Leukemia ◽

World Health ◽

Acute Leukemias ◽

Acute Myeloid

The acute leukemias are malignant clonal disorders characterized by aberrant differentiation and proliferation of transformed hematopoietic progenitor cells. These cells accumulate within the bone marrow and lead to suppression of the production of normal blood cells, with resulting symptoms from varying degrees of anemia, neutropenia, and thrombocytopenia or from infiltration into tissues. They are currently classified by their presumed cell of origin, although the field is moving rapidly to genetic subclassification. This review covers epidemiology; etiology; classiﬁcation of leukemia by morphology, immunophenotyping, and cytogenetic/molecular abnormalities; cytogenetics of acute leukemia; general principles of therapy; acute myeloid leukemia; acute lymphoblastic leukemia; and future possibilities. The figure shows the incidence of acute leukemias in the United States. Tables list World Health Organization (WHO) classification of acute myeloid leukemia and related neoplasms, expression of cell surface and cytoplasmic markers for the diagnosis of acute myeloid leukemia and mixed-phenotype acute leukemia, WHO classification of acute lymphoblastic leukemia, WHO classification of acute leukemias of ambiguous lineage, WHO classification of myelodysplastic syndromes, European LeukemiaNet cytogenetic and molecular genetic subsets in acute myeloid leukemia with prognostic importance, cytogenetic and molecular subtypes of acute lymphoblastic leukemia, terminology used in leukemia treatment, and treatment outcome for adults with acute leukemia. This review contains 1 highly rendered figure, 9 tables, and 117 references.

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