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

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.

Download Full-text

Enasidenib: First Mutant IDH2 Inhibitor for the Treatment of Refractory and Relapsed Acute Myeloid Leukemia

Anti-Cancer Agents in Medicinal Chemistry ◽

10.2174/1871520618666181025091128 ◽

2019 ◽

Vol 18 (14) ◽

pp. 1936-1951 ◽

Cited By ~ 5

Author(s):

Raghav Dogra ◽

Rohit Bhatia ◽

Ravi Shankar ◽

Parveen Bansal ◽

Ravindra K. Rawal

Keyword(s):

Clinical Trial ◽

Bone Marrow ◽

Overall Survival ◽

Acute Myeloid Leukemia ◽

Adverse Effects ◽

Blood Cells ◽

Myeloid Leukemia ◽

White Blood Cells ◽

Phase Iii ◽

Acute Myeloid

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.

Download Full-text

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.

Download Full-text

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

TELKOMNIKA (Telecommunication Computing Electronics and Control) ◽

10.12928/telkomnika.v17i2.8666 ◽

2018 ◽

Vol 17 (2) ◽

pp. 645 ◽

Cited By ~ 1

Author(s):

Wiharto Wiharto ◽

Esti Suryani ◽

Yuda Rizki Putra

Keyword(s):

Acute Myeloid Leukemia ◽

Blood Cells ◽

Myeloid Leukemia ◽

White Blood Cells ◽

Blast Cell ◽

Cell Type ◽

Image Morphology ◽

Acute Myeloid

Download Full-text

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

Download Full-text

Differentiation of leukemic blasts is not completely blocked in acute myeloid leukemia

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1904091116 ◽

2019 ◽

Vol 116 (49) ◽

pp. 24593-24599 ◽

Cited By ~ 3

Author(s):

Anupriya Agarwal ◽

William J. Bolosky ◽

David B. Wilson ◽

Christopher A. Eide ◽

Susan B. Olson ◽

...

Keyword(s):

Acute Myeloid Leukemia ◽

Stem Cell ◽

Hematopoietic Stem Cell ◽

Blood Cells ◽

Myeloid Leukemia ◽

Myeloid Cells ◽

Hematopoietic Stem ◽

Blast Cells ◽

The Impact ◽

Acute Myeloid

Hematopoiesis, the formation of blood cells, involves the hierarchical differentiation of immature blast cells into mature, functional cell types and lineages of the immune system. Hematopoietic stem cells precisely regulate self-renewal versus differentiation to balance the production of blood cells and maintenance of the stem cell pool. The canonical view of acute myeloid leukemia (AML) is that it results from a combination of molecular events in a hematopoietic stem cell that block differentiation and drive proliferation. These events result in the accumulation of primitive hematopoietic blast cells in the blood and bone marrow. We used mathematical modeling to determine the impact of varying differentiation rates on myeloblastic accumulation. Our model shows that, instead of the commonly held belief that AML results from a complete block of differentiation of the hematopoietic stem cell, even a slight skewing of the fraction of cells that differentiate would produce an accumulation of blasts. We confirmed this model by interphase fluorescent in situ hybridization (FISH) and sequencing of purified cell populations from patients with AML, which showed that different leukemia-causing molecular abnormalities typically thought to block differentiation were consistently present in mature myeloid cells such as neutrophils and monocytes at similar levels to those in immature myeloid cells. These findings suggest reduced or skewed, rather than blocked, differentiation is responsible for the development of AML. Approaches that restore normal regulation of hematopoiesis could be effective treatment strategies.

Download Full-text

Evaluation of related flow cytometry parameters with FAB subtype of acute myeloid leukemia associated with histological assessment

Samarra Journal of Pure and Applied Science ◽

10.54153/sjpas.2020.v2i3.126 ◽

2021 ◽

Vol 2 (3) ◽

pp. 84-94

Author(s):

Rasha Hamid ◽

Ayad Hameed Ibraheem

Keyword(s):

Flow Cytometry ◽

Acute Myeloid Leukemia ◽

Acute Leukemia ◽

Myeloid Leukemia ◽

Complete Blood Count ◽

White Blood Cells ◽

Clinical Feature ◽

Blast Cells ◽

Adults And Children ◽

Acute Myeloid

The acute leukemia is a group of malignant disorders of the haemopoietic cells, characteristically related with increases of number of leucocytes in the blood. The present study is aimed to evaluation the expression of immunophenic antibodies which used on blast cells to diagnosis and classification acute leukemia by flow cytometry to define their relationship with age, gender and French-American–British (FAB) subtypes, and assessment complete blood count (CBC) including white blood cells WBCs, hemoglobin HGB, and platelets PLTs, addition to define histological changes of bone marrow biopsy (BMB) of acute leukemia patients. This study included 73 patients were newly diagnosed with acute myeloid leukemia (AML), 60 of them was adults (range 15 –77 years), and the other 13 cases were children (range 1–14 years). The patients divided into three groups: 15-35, 36-55, and 56-77 years, while children patients divided into: 0-4, 5-8 and 9-14 years. The results shown that fever and paleness were the most common clinical feature among acute myeloid leukemia patients ( adults and children).

Download Full-text

Single-cell map of diverse immune phenotypes in the acute myeloid leukemia microenvironment

Biomarker Research ◽

10.1186/s40364-021-00265-0 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Rongqun Guo ◽

Mengdie Lü ◽

Fujiao Cao ◽

Guanghua Wu ◽

Fengcai Gao ◽

...

Keyword(s):

Acute Myeloid Leukemia ◽

Single Cell ◽

Myeloid Leukemia ◽

Immune Cell ◽

Immune Surveillance ◽

Cell Types ◽

Developmental Trajectory ◽

Significant Difference ◽

Cell Phenotypes ◽

Acute Myeloid

Abstract Background Knowledge of immune cell phenotypes, function, and developmental trajectory in acute myeloid leukemia (AML) microenvironment is essential for understanding mechanisms of evading immune surveillance and immunotherapy response of targeting special microenvironment components. Methods Using a single-cell RNA sequencing (scRNA-seq) dataset, we analyzed the immune cell phenotypes, function, and developmental trajectory of bone marrow (BM) samples from 16 AML patients and 4 healthy donors, but not AML blasts. Results We observed a significant difference between normal and AML BM immune cells. Here, we defined the diversity of dendritic cells (DC) and macrophages in different AML patients. We also identified several unique immune cell types including T helper cell 17 (TH17)-like intermediate population, cytotoxic CD4+ T subset, T cell: erythrocyte complexes, activated regulatory T cells (Treg), and CD8+ memory-like subset. Emerging AML cells remodels the BM immune microenvironment powerfully, leads to immunosuppression by accumulating exhausted/dysfunctional immune effectors, expending immune-activated types, and promoting the formation of suppressive subsets. Conclusion Our results provide a comprehensive AML BM immune cell census, which can help to select pinpoint targeted drug and predict efficacy of immunotherapy.

Download Full-text