Immature White Blood Cells Research Articles

A-156 Comparison of blast and variant lymphocyte flagging between the Alinity hq from Abbott and XN-1000 from Sysmex

Abstract Background Blast cells are immature white blood cells (WBC). The presence of blasts in the peripheral blood may be associated with certain diseases such as myelodysplastic syndrome and acute myelogenous leukemia. Variant lymphocytes (i.e., reactive lymphocytes, atypical lymphocytes) are WBCs produced by the immune system in reaction to infections caused by pathogens such as Epstein-Barr virus, cytomegalovirus, etc. It is clinically useful that the automated hematology analyzer is capable of flagging these cells. In this study, the blast (shown as ‘BLAST’ on the Alinity hq and ‘Blast/Abn Lympho?’ or ‘Blast?’ on XN-1000) and variant lymphocyte (shown as ‘VAR LYM’ on the Alinity hq, and ‘Atypical Lympho?’ on XN-1000) flagging was compared between the Alinity hq from Abbott and XN-1000 from Sysmex. Alinity hq has different sensitivity settings available for BLAST and VAR LYM flagging. In this study, the default setting for Alinity hq was used. For XN-1000, the Q-Flag default setting is 100. In this study, the setting was 120 for ‘Blast/Abn Lympho’ and 100 for “Atypical lympho?’. Methods Blood specimens were tested on the Alinity hq and XN-1000 analyzers. Two smears per specimen were made manually or by the Alinity hs slide maker stainer and read by two independent readers. The specimens showing any of the following flagging were included in the analysis: ‘BLAST’ on Alinity hq, ‘VAR LYM’ on Alinity hq, ‘Blast/Abn Lympho?’ or ‘Blast?’ on XN-1000, ‘Atypical lympho?’ on XN-1000. The flagging reports of these samples from both analyzers were compared to the smear review to determine if each analyzer's blast and variant lymphocyte flagging were true positive, false positive, true negative, or false negative. Results A total of 520 specimens were tested, yielding 41 specimens for blast flagging analysis and 80 specimens for variant lymphocyte flagging analysis. Due to the scarce availability of true positive samples from the manual review, true positive and false negative rate were not compared. For blast flagging using smear results of >=1% or >=5% as the positive criterion, Alinity hq yielded a 9.8% (=4/41) false positive rate and 87.8% (=36/41) true negative rate. XN-1000 yielded a 75.6% (=31/41) false positive rate and 22.0% (=9/41) true negative rate. For variant lymphocyte flagging using smear results of >=5% as the positive criterion, Alinity hq yielded a 6.3% (=5/80) false positive rate and 92.5% (=74/80) true negative rate. XN-1000 yielded a 91.3% (=73/80) false positive rate and 7.5% (=6/80) true negative rate. Conclusions This study demonstrated that Alinity hq achieved a much lower false positive rate and much higher true negative rate for both blast and variant lymphocyte flagging when compared to the XN-1000. Manual blast and variant lymphocyte morphology classification is laboratory- and technician-dependent. Both analyzers allow different sensitivity/specificity levels to flag blasts and variant lymphocytes. Each individual laboratory should evaluate and choose the setting that is most suitable for their specific patient populations to obtain the desired flagging rate.

CAE-ResVGG FusionNet: A Feature Extraction Framework Integrating Convolutional Autoencoders and Transfer Learning for Immature White Blood Cells in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a highly aggressive cancer form that affects myeloid cells, leading to the excessive growth of immature white blood cells (WBCs) in both bone marrow and peripheral blood. Timely AML detection is crucial for effective treatment and patient well-being. Currently, AML diagnosis relies on the manual recognition of immature WBCs through peripheral blood smear analysis, which is time-consuming, prone to errors, and subject to inter-observers' variation. This study aimed to develop a computer-aided diagnostic framework for AML, called "CAE-ResVGG FusionNet", that precisely identifies and classifies immature WBCs into their respective subtypes. The proposed framework leverages an integrated approach, by combining a convolutional autoencoder (CAE) with finely tuned adaptations of the VGG19 and ResNet50 architectures to extract features from CAE-derived embeddings. The process begins with a binary classification model distinguishing between mature and immature WBCs followed by a multiclassifier further classifying immature cells into four subtypes: myeloblasts, monoblasts, erythroblasts, and promyelocytes. The CAE-ResVGG FusionNet workflow comprises four primary stages, including data preprocessing, feature extraction, classification, and validation. The preprocessing phase involves applying data augmentation methods using geometric transformations and synthetic image generation using the CAE to address imbalance in the WBC distribution. Feature extraction involves image embedding and transfer learning, where CAE-derived image representations are used by a custom integrated model of VGG19 and ResNet50 pretrained models. The classification phase employs a weighted ensemble approach that leverages VGG19 and ResNet50, where the optimal weighting parameters are selected using a grid search. The model performance was assessed during the validation phase using the overall accuracy, precision, and sensitivity, while the area under the receiver characteristic curve (AUC) was used to evaluate the model's discriminatory capability. The proposed framework exhibited notable results, achieving an average accuracy of 99.9%, sensitivity of 91.7%, and precision of 98.8%. The model demonstrated exceptional discriminatory ability, as evidenced by an AUC of 99.6%. Significantly, the proposed system outperformed previous methods, indicating its superior diagnostic ability.

Survey on the Oxidative Stress Status and the Role of Antioxidants in Acute Myeloid Leukemia Therapy

Acute myeloid leukemia (AML) is a heterogeneous disease with multiple mutations in hematopoietic stem cells characterized by abnormality increases in immature or dysfunctional white blood cells. The precise mechanisms of AML development have not been clear but there are many factors increase to developing leukemia such as familial history of leukemia, elderly, life style, genetics and some chronic disease like diabetic. Multiple mutilations happen in the hematopoietic stem cells, acquire some form of genetic instability lead to improve expression of some protein kinase and transduction protein following that increase ROS formation, which is associated with increased DNA damage. Nowadays, the use antioxidant drugs are growingly accepted in universal due to their safety and efficiency to diminish the adverse effects of free radicals and treatment of the many diseases such as cancer. In this article, we discussed the correlation between acute myeloid leukemia incidence and the oxidant biomarkers (oxidative stress), and more focusing on the great role of antioxidant biomarkers, whether the non-enzymatic or the enzymatic in the protection of the cells against harmful effects of free radicals in the acute myeloid leukemia patients.

ALL classification using neural ensemble and memetic deep feature optimization.

Acute lymphoblastic leukemia (ALL) is a fatal blood disorder characterized by the excessive proliferation of immature white blood cells, originating in the bone marrow. An effective prognosis and treatment of ALL calls for its accurate and timely detection. Deep convolutional neural networks (CNNs) have shown promising results in digital pathology. However, they face challenges in classifying different subtypes of leukemia due to their subtle morphological differences. This study proposes an improved pipeline for binary detection and sub-type classification of ALL from blood smear images. At first, a customized, 88 layers deep CNN is proposed and trained using transfer learning along with GoogleNet CNN to create an ensemble of features. Furthermore, this study models the feature selection problem as a combinatorial optimization problem and proposes a memetic version of binary whale optimization algorithm, incorporating Differential Evolution-based local search method to enhance the exploration and exploitation of feature search space. The proposed approach is validated using publicly available standard datasets containing peripheral blood smear images of various classes of ALL. An overall best average accuracy of 99.15% is achieved for binary classification of ALL with an 85% decrease in the feature vector, together with 99% precision and 98.8% sensitivity. For B-ALL sub-type classification, the best accuracy of 98.69% is attained with 98.7% precision and 99.57% specificity. The proposed methodology shows better performance metrics as compared with several existing studies.

Apoptosis Response in Acute Lymphoblastic Leukemia Cells after Methotrexate Treatment-A Systematic Review

Background: Acute lymphoblastic leukemia (ALL) is a malignancy characterized by the overproduction of immature white blood cells in the bone marrow. Methotrexate (MTX) is a cornerstone chemotherapy agent utilized in ALL treatment due to its ability to induce apoptosis in cancerous cells. MTX’s action involves inhibiting dihydrofolate reductase (DHFR), leading to impaired DNA and RNA synthesis, yet resistance to MTX and its exact apoptotic mechanisms remain areas of ongoing research. Objective: This systematic review aims to elucidate the mechanisms by which MTX induces apoptosis in ALL cells and to assess the clinical implications of these findings for improving therapeutic strategies. Methods: Adhering to PRISMA guidelines, databases including Google Scholar, Embase, PubMed, and Web of Science were searched for studies published between 2000 and 2023. Studies were included if they involved MTX treatment of ALL cell lines and assessed apoptosis. Critical Appraisal Skills Programme (CASP) tools were used for quality assessment. Data extraction and thematic analysis were employed to synthesize findings. Results: Twenty-six studies met the inclusion criteria. Key mechanisms of MTX-induced apoptosis included disruption of mitochondrial membrane potential, activation of the JNK signaling pathway, modulation of BCL-2 family proteins, and interference with folate metabolism. Resistance mechanisms and the therapeutic potential of MTX polyglutamates were also identified. Conclusion: MTX induces apoptosis in ALL cells through multiple pathways, primarily by disrupting folate metabolism and mitochondrial function. The findings reinforce the necessity for personalized approaches in MTX-based therapy, considering the emerging resistance patterns, and highlight the potential of MTX polyglutamates in enhancing treatment efficacy.

An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization.

In the field of medicine, decision support systems play a crucial role by harnessing cutting-edge technology and data analysis to assist doctors in disease diagnosis and treatment. Leukemia is a malignancy that emerges from the uncontrolled growth of immature white blood cells within the human body. An accurate and prompt diagnosis of leukemia is desired due to its swift progression to distant parts of the body. Acute lymphoblastic leukemia (ALL) is an aggressive type of leukemia that affects both children and adults. Computer vision-based identification of leukemia is challenging due to structural irregularities and morphological similarities of blood entities. Deep neural networks have shown promise in extracting valuable information from image datasets, but they have high computational costs due to their extensive feature sets. This work presents an efficient pipeline for binary and subtype classification of acute lymphoblastic leukemia. The proposed method first unveils a novel neighborhood pixel transformation method using differential evolution to improve the clarity and discriminability of blood cell images for better analysis. Next, a hybrid feature extraction approach is presented leveraging transfer learning from selected deep neural network models, InceptionV3 and DenseNet201, to extract comprehensive feature sets. To optimize feature selection, a customized binary Grey Wolf Algorithm is utilized, achieving an impressive 80% reduction in feature size while preserving key discriminative information. These optimized features subsequently empower multiple classifiers, potentially capturing diverse perspectives and amplifying classification accuracy. The proposed pipeline is validated on publicly available standard datasets of ALL images. For binary classification, the best average accuracy of 98.1% is achieved with 98.1% sensitivity and 98% precision. For ALL subtype classifications, the best accuracy of 98.14% was attained with 78.5% sensitivity and 98% precision. The proposed feature selection method shows a better convergence behavior as compared to classical population-based meta-heuristics. The suggested solution also demonstrates comparable or better performance in comparison to several existing techniques.

Survivorship Struggles: Navigating Etiologies and Clinical Parameters of Febrile Neutropenia During Induction Chemotherapy in Acute Leukemia Patients.

Background Acute leukemia, characterized by the uncontrolled proliferation of immature white blood cell precursors, poses significant challenges during induction chemotherapy, including the elevated risk of febrile neutropenia and its associated complications. Our study aims to explain the clinical and etiological parameters of these patients in a resource-limited setting. Methods This retrospective study focused on a total of 102 adult patients with acute leukemia who developed febrile neutropenia during the induction chemotherapy phase. Patients with disease relapse, prior bone marrow transplantation, and cases of acute promyelocytic leukemia were excluded from the study. Demographical characteristics, symptoms at presentation, diagnoses, infectious causes, and outcomes were systematically reported. Infectious etiologies and detailed culture reports were meticulously tabulated, and subsequent data were analyzed. Results Of the 102 patients, 43 (42.2%) were males, with a mean age of 31.9 ± 6.5 years. During the induction chemotherapy, a total of 31 patients died of complicated febrile neutropenia. Severe vomiting was the most common symptom present in 37 (36.2%), followed by cough in 35 (34.3%) and loose stools in 28 (27.5%). Community-acquired pneumonia, neutropenic sepsis, and neutropenic colitis were among the most common etiologies of febrile neutropenia. A total of 72 (70.6%) patients had culture-proven multidrug-resistant Gram-negative bacteremia that contributed to poor outcomes. Conclusions Acute leukemia patients undergoing induction chemotherapy face high infection-associated mortality due to their immunocompromised state. Inadequate infection control measures and antimicrobial resistancecontribute to the emergence of multidrug-resistant organisms. Enhanced infection prevention strategies and evidence-based antibiotic prescription guidelines are need of time in resource-limited settings such as Pakistan to address febrile neutropenia complications and bridge the existing care gap in its management.

T-Acute Lymphoblastic Leukemia

Abstract Acute lymphoblastic leukemia (ALL) is a condition in which immature white blood cells (WBCs) accumulate in the bone marrow, resulting in the crowding of normal WBCs and buildup in the liver, spleen, and lymph nodes. ALL is a malignancy of B or T lymphoblasts. B lymphocytes protect the body against bacteria and viruses through production of antibodies, which can directly destroy target cells or trigger others to do so. T lymphocytes directly destroy bacteria or cells infected with viruses. Approximately 20% of all ALL patients are categorized specifically to suffer from T-cell ALL (T-ALL), and it is seen to be more prevalent in the adult population in comparison with children, with incidences shown to diminish with age. Among T-ALL cases in the pediatric population, a median onset of age 9 has been identified and the disease is particularly prominent among adolescents. The disease stems from cytogenic and molecular abnormalities, resulting in disruption of developmental pathways controlling thymocyte development, tumor suppressor development, and alterations in control of cell growth and proliferation. Distinct from adult T-cell leukemia where T-cell lymphotropic virus type I causes malignant maturation of T cells, T-ALL is a precursor for lymphoid neoplasm. Its clinical presentation most commonly includes infiltration of the central nervous system and further identifies mediastinal mass presence originating from the thymus, along with extramedullary involvement of multiple organs including the lymph node as a result of hyperleukocytosis.

Applications, Efficacies, Potential Side Effects, and Expectations of CAR-T Cell Therapy in the Treatment of Acute Lymphoblastic Leukemia

CAR-T cell therapy is a highly effective and powerful treatment for acute lymphoblastic leukemia (ALL), showcasing remarkable efficacy in combating this disease. Currently, several CAR-T cell therapies are available for ALL, including Kymriah and Yescarta. CAR-T is a novel targeted therapy for tumor treatment, marking a shift in modern medicine from molecular therapy to cellular therapy. ALL is a malignant blood disorder characterized by an abnormal proliferation of immature white blood cells known as lymphoblasts. These abnormal cells may be found in bone marrow, lymph nodes, spleen, and other organs. Acute lymphoblastic leukemia (ALL) predominantly affects adolescents and children, with a higher incidence observed in males. Although immunotherapy has shown promising efficacy in hematologic malignancies, the emergence of antigen escape poses a significant obstacle, limiting its use in treating solid tumors. Extensive scientific research is underway to improve the construction and manufacturing techniques of CAR-T cells and investigate novel cell configurations. This review aims to summarize the applications, efficacy, side effects, and expected outcomes of CAR-T therapy in treating ALL. It also explores strategies to enhance CAR-T therapy and provides insights into its current status and future directions in the background of ALL.

Arsenic As a Therapy in Cancer: A Short Review

Acute myeloid leukemia (AML) is a cancer of the blood and bone marrow in which the bone marrow produces immature white blood cells, called myeloblasts. AML (acute myeloid leukemia) prevents these myeloblasts from developing into mature, healthy blood cells, as a result they accumulate and crowd out other healthy cells, such as red blood cells, white blood cells, and platelets. Sometimes acute myeloid leukemia(AML) can also spread to other organs, such as the brain, spinal cord, liver, or spleen. Without prompt treatment, AML can be life-threatening. There has been 2–3 cases per 100,000 people annually. Compared to women, men are more likely to experience it. acute myeloid leukemia(AML) makes up roughly 3% of all malignancies, with a median age of 32 years, according to the population-based Cancer Registry for Delhi. Among the various drugs for treatment of leukemia arsenic has been one of the most effective in treating it. Arsenic has been known as poison over 4000years.Long-term exposure to as has led to various adverse effects on health, but after the discovery of chemo therapeutic properties of As the understanding has been changed from poison to drug. Arsenic trioxide (ATO) is the most active antileukemia agent in treatment of acute promyelocytic leukemia(APL). Arsenic trioxide (ATO) cause degradation of PML RAR alpha promoting differentiation. Not only this but also arsenic induces apoptosis via mitochondrial caspase or formation of reactive oxygen species (ROS) and deleting GSH content of cell. This article focuses on the therapeutic effect of arsenic on acute myeloid leukemia

Therapy-Related Acute Myeloid Leukemia After Chemotherapy For Osteosarcoma: Report of 2 Cases and Review of The Literature

Abstract Introduction/Objective Therapy-related acute myeloid leukemia (t-AML) and myelodysplastic syndrome (t-MDS) are rare but well-established late complications of chemotherapeutic agents or radiation therapy used to treat various malignancies. Many cases of t-AML/t-MDS have been described secondary to Hodgkin, non-Hodgkin lymphoma, and other solid tumors. However, only a few cases of t-AML and t-MDS have been reported following osteosarcoma treatment. We describe two patients diagnosed with osteosarcoma who developed t-AML after receiving multiple chemotherapeutic agents. Methods/Case Report A 12-year-old male with Li-Fraumeni syndrome was diagnosed with osteosarcoma of the left ilium and sacrum with pulmonary metastases. Despite multiple lines of chemoradiation therapy including doxorubicin and ifosfamide, tumor progressed at the primary tumor site. The patient subsequently presented with altered mental status and pancytopenia. Bone marrow biopsy demonstrated AML. He died despite supportive care. A 14-year-old female was diagnosed with left tibial osteosarcoma with bony and pulmonary metastases. She was treated on standard osteosarcoma chemotherapy including doxorubicin, cisplatin, methotrexate, ifosfamide and etoposide, surgery, and radiation. 1 year following therapy, she had local recurrence of osteosarcoma without progression despite not starting chemotherapy. However, 3 years later, she developed pancytopenia, and immature white blood cells were identified on peripheral smear. Bone marrow biopsy revealed AML-M7. Despite supportive care and zoledronic acid, she ultimately died from sepsis and disseminated intravascular coagulation. Results (if a Case Study enter NA) NA Conclusion With improved therapy and survival for osteosarcoma patients, these patients should be monitored for secondary malignancies. T-AML is rare following osteosarcoma treatment, but outcome remains poor, despite treatment. Underlying Li-Fraumeni syndrome may influence the risk of development of secondary malignant neoplasms. Literature review of types and prognosis of t-AML following osteosarcoma will be reviewed and compared to our cases, including related genetic predisposing factors and chemotherapeutic agents. Our second patient is the first reported case of AML-M7 secondary to osteosarcoma treatment.

A Deep Learning Framework for Classification of Immature White Blood Cells in Acute Myeloid Leukemia Using Convolutional Autoencoder and Transfer-Learning Ensemble

Acute myeloid leukemia (AML) is a type of acute leukemia that affects myeloid cells. It is characterized by uncontrolled proliferation of immature WBCs causing an accumulation of immature WBCs in the bone marrow and peripheral circulation. AML is an aggressive cancer that can be life-threatening if not treated promptly, therefore, early detection is crucial. Identification of immature WBCs and their subtypes is the first step towards AML diagnosis. Microscopic blood smear image analysis is commonly used to diagnose AML as it is less expensive non-invasive diagnostic tool compared to bone marrow biopsy and immunophenotype. Nonetheless, classifying immature WBCs presents challenges due to their high similarity and minimal interclass variation, especially for intermediate stages of myelopoiesis which make is susceptible to misclassification. The current diagnostic methods of AML are based on manual identification of immature WBCs using peripheral blood smear. These methos are time-consuming, prone to errors, and subject to inter-observer variation. Therefore, this study aims to develop a computer-aided diagnostic framework to accurately identify immature WBCs and classify them into various subtypes. The proposed framework consists of two main components: a binary classification system to classify cells into mature versus immature cells and a multiclassification system to further classify immature cells into four subtypes including myeloblast, monoblast, and promyelocytes. The proposed framework was designed to undergo four distinct phases: the first phase involves preprocessing, which includes data augmentation techniques aimed at addressing the imbalance distribution of WBCs. Augmentation encompasses geometric transformation and generation of synthetic images using convolutional Autoencoder (CAE). Feature extraction phase involves using image embedding and transfer-learning. Image embedding representation was obtained using CAE and then utilized by two pre-trained models: VGG19 and Resnet50. Classification phase was carried out using weighted ensemble of the VGG19 and Resnet50 with the optimal weights determined through Grid search. The validation phase evaluated the model performance using overall accuracy, precision, and sensitivity. The area under the ROC curve (AUC) was used to assess model discrimination ability. The proposed framework demonstrated excellent results, achieving an average accuracy of 99.5%, a sensitivity of 97.97%, and a precision of 94.12%. The model exhibited an excellent discriminatory capability, with an AUC of 99.9%. Furthermore, the proposed framework outperformed previous methods, showcasing improved performance. Key word: Acute myeloid leukemia, peripheral blood smear, immature white blood cells, deep learning, feature extraction, computer-aided diagnosis.

Chronic Myeloid Leukemia (CML) with Permanent Hearing Loss

Objective: The present case study aims to discuss a rare case of chronic myeloid leukemia causing bilateral hearing impairment, tinnitus, and vertigo. Introduction: Chronic Myeloid Leukemia (CML) or chronic granulocytic leukemia is a type of blood cancer that affects the bone marrow. It results in an overproduction of immature White Blood Cells (WBCs) that typically happens throughout or after middle age, and adolescents are rarely affected. The genetic mutation that causes CML is known as the BCR-ABL fusion gene. Case Presentation: In this case report, a 30-year-old female presented to the hospital of Jazan, Saudi Arabia, with sudden hearing impairment and other symptoms, leading to a CML diagnosis. The patient had no prior hearing impairment background, and the otoscopic scanning was normal. The Pure Tone Audiometry (PTA) showed profound sensorineural hearing loss on both sides. Blood tests revealed hyperleukocytosis with marked neutrophilia, mild basophilia, and eosinophilia and a BCR-ABL quantitation of 85%. Bone marrow aspiration showed granulocytic hyperplasia, mild left-shifted maturation, and less than 1% blasts. Results: The patient was started with options including Tyrosine Kinase Inhibitors (TKIs) such as Imatinib, which target the BCR-ABL fusion gene, reducing the number of leukemia cells and improving her white blood cell count. However, her deafness persisted, and she became dependent on hearing aids. Discussion: CML presenting with hearing loss is rare, with only a few reported cases in the literature. It is believed to be related to the infiltration of leukemic cells in the inner ear or the microvascular complications associated with the disease. Hearing loss can be reversible or permanent, depending on the severity and duration of the disease. Treatment of CML with tyrosine kinase inhibitors such as Imatinib can improve the hematologic parameters, but the effect on hearing loss is uncertain.

Automatic myeloblast segmentation in acute myeloid leukemia images based on adversarial feature learning

The diagnosis of acute myeloid leukemia (AML) requires reliable detection and counting of immature white blood cells called myeloblasts on bone marrow aspirate and biopsy slides. Experienced hematologists/oncologists typically spend many hours manually reviewing blood cells and performing a microscopic morphological examination of myeloblasts on glass slides to determine this diagnosis. While manual counting of myeloblasts is considered the gold standard for an accurate diagnosis of AML, the process is time-consuming and difficult to standardize due to high intra- and inter-observer variability. Machine learning algorithms coupled with deep learning techniques represent potentially promising approaches for automated counting. Since myeloblasts possess very similar texture patterns to other hematopoietic cells (OHCs), accurately differentiating myeloblasts from other blood cells is a challenge for most segmentation models. We present a new conditional generative adversarial network (cGAN)-based segmentation model (AMLcGAN) to efficiently segment myoblasts from slides of patients with AML. Validation of the approach on 204 cytological images reveals a mean dice coefficient (mDICE) of 82.51 % with respect to manual segmentations. We also compared the new AMLcGAN method with three deep learning models: Unet, Segnet and Pix2pix, the corresponding mDICE values were 77.4 %, 78.2 %, and 79.53 %, respectively. We also employed the segmentation results from 4 different models to extract blast statistics feature, Haralick texture feature, Fractal dimension feature and myeloblast shape features to create 4 different prognostic models to predict risk of recurrence in AML patients. TheConcordance Index values for AMLcGAN and the three other models respectively were 0.63, 0.53, 0.58, and 0.56. The comprehensive evaluation results presented suggest that the AMLcGAN approach can enable accurate blast segmentation and for construction of accurate prognostic models for predicting risk of recurrence in AML patients.

AUTOMATED ACUTE LYMPHOBLASTIC LEUKEMIA CELL CLASSIFICATION USING OPTIMIZED CONVOLUTIONAL NEURAL NETWORK

Acute lymphoblastic leukemia (ALL) is the most common variant of paediatric cancer that creates numerous immature white blood cells affecting the bone marrow. Manual diagnosis of leukemia from microscopic evaluation of stained sample slides is an exhausting process, which is less accurate and susceptible to human errors. Additionally, identifying the leukemic blast cells under the microscope is complicated due to morphological similarity with the normal cell images. In this paper, we proposed an automated method to analyse the blood smear images using Local Binary Pattern (LBP) and classify the leukemic blast cells and normal cells. We have analysed the performance of machine learning and deep learning models such as Support Vector Machine (SVM), k-Nearest Neighbor algorithm (kNN), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN). For classifying ALL and normal cell images, kNN achieved an accuracy of 94.4%, SVM, and ANN achieved an accuracy of 98.6%, and CNN achieved an accuracy of 99.6%. SVM achieved the highest sensitivity of 100%.

Evaluating the effects of chemotherapy drugs and thiosemicarbazone complexes on the alteration of SNHG16 expression in Acute Lymphoblastic Leukemia

Background: Acute lymphoblastic leukemia (ALL) is a malignant disease that afflicts both children and adults. It starts from the bone marrow (soft and spongy tissue inside the bone) where immature white blood cells or lymphocytes are formed. This study aims to compare chemotherapy drugs, methotrexate (MTX), cyclophosphamide, cytarabine (Ara-C) and mercaptopurine (6mp) with nickel (Ni) and copper (Cu) complexes of thiosemicarbazone (TSCZ) in terms of their effects on the changes of Small Nucleolar RNA Host Gene 16(SNHG16) long non coding RNA (lncRNA) expression in the ALL cell line.
 Materials and Methods: this experimental study was conducted on various concentrations of chemotherapy drugs including MTX, CP, Ara-C, 6mp and Ni and Cu complexes of TSCZ. The Jurkat E6.1 cell lines were subjected to passage in different groups and times (24, 48, and 72h) and then treated with the chemotherapy drugs. After RNA extraction and cDNA synthesis, the SNHG16 gene expression was evaluated by Real-Time PCR, and the results were analyzed by the Rest software.
 Results: As the results indicated, within 24h, MTX (0.77, 0.72), CP (0.61, 0.7), ara-C (0.78, 0.87), Cu (0.91, 0.95) and Ni (0.94, 0.79) complexes as well as complex 1(0.73) and 2 (0.94) decreased the expression of SNHG16 gene significantly (P<0.001). Furthermore within 48h, under the influence of CP (0.86, 0.89) and Ara-C (0.94, 0.97) as well as Cu (0.96) and Ni (0.98) complexes, the gene expression continued to decline(P<0.001).The greatest effect of chemotherapy drugs belonged to the combination of 1μM of ara-C and 5μM of 6mp.(P<0.001). 
 Conclusion: It was found that gene expression analysis is a feasible method to identify the pathways affected by the standard induction chemotherapy in ALL patients. This finding promotes the development of novel targeted drugs and biomarkers to categorize disease aggressiveness and evaluate treatment responses.

An efficient EGWO algorithm as feature selection for B-ALL diagnoses and its subtypes classification using peripheral blood smear images

Acute lymphoblastic leukemia (ALL) is the most serious type of leukemia that develops because it causes an abnormal increase in the production of immature white blood cells in the bone marrow. ALL spreads rapidly in children's bodies and leads to their death. The main objective of this paper is to introduce an enhanced methodology based on the k-means clustering algorithm for classifying all subtypes of ALL. Image preprocessing is the first step of the proposed methodology. For obtaining the images' descriptive features, feature extraction is the second step that has been used. To select the most vital features that can characterize the histology of the blood cells, Enhanced Grey Wolf Optimization (EGWO) algorithm has been used in the third step. Hence, EGWO will start to select the search agents based on certain criteria as the best cluster center by using the k-means clustering algorithm. Several supervised classifiers as Random Forest (RF), K Nearest Neighbors (KNN), Support Vector Machine (SVM), and Naïve Bayes (NB) have been compared. The proposed methodology achieves a high degree of accuracy of99.22%,precision of99%,and sensitivity of99%.A comparative study has been established in order to verify the effectiveness of the proposed methodology.

Pattern Recognition of Acute Lymphoblastic Leukemia (ALL) Using Computational Deep Learning

Leukemia is a cancer of blood-producing cells, including the bone marrow. Abnormal white blood cells travel through blood vessels and multiply rapidly. Healthy cells in the body become a minority, and the imbalance increases the chances of infection in the body. Leukemia or blood cancer is the most common cancer in children ages 2 - 14. Most leukemia in children is treated. Acute lymphocytic leukemia (ALL) is a type of cancer in the blood and bone marrow. It progresses rapidly when immature white blood cells are formed instead of mature ones. Treatments for acute lymphocytic leukemia include drugs and blood transfusions directly into veins, chemotherapy, and all transplantation, which involve transferring organs or tissues within the body or from one person to another. In this paper, Pattern Recognition of Acute Lymphoblastic Leukemia has been proposed using Computational Deep Learning. Pattern recognition technology uses mathematical algorithms to identify patterns in large datasets of data. Analyzing the data, the algorithms can identify patterns indicative of certain states or conditions. In the case of ALL, the algorithm would look for patterns in white blood cell count data that indicate the presence of ALL. These patterns may include changes in the number of white blood cells over time, changes in the composition of the white blood cells, or changes in the levels of certain proteins or gene expressions associated with ALL. The proposed ALLDM model achieved 81.53% (DDS) and 87.92% (SDS) of chemotherapy management, 79.16% (DDS) and 94.31% (SDS) of Stem Cell Transplantation Management, 63.77% (DDS) and 87.37% (SDS) of Radiation therapy Management and 88.92% (DDS) and 85.86% (SDS) of Targeted therapy drugs management.

Lightweight EfficientNetB3 Model Based on Depthwise Separable Convolutions for Enhancing Classification of Leukemia White Blood Cell Images

Acute lymphoblastic leukemia (ALL) occurs when undeveloped lymphocytes are grown excessively in the bone marrow due to the formation of many immature white blood cells (WBC) in the bone marrow to destroy the healthy cell, which may lead to death. White blood cells are part of the immune system fighting against germs. A timely and accurate cancer diagnosis is important for effective treatment to improve survival rates. Since the image of acute lymphoblastic leukemia cells (cancer cells) under the microscope is complicated to recognize the difference between ALL cancer cells and normal cells. In order to reduce the severity of this disease, it is necessary to classify immature cells at an early stage. In recent years, different classification models have been introduced based on machine learning (ML) and deep learning (DL) algorithms, but they need to be improved. This work enhances the diagnosis of ALL with a computer-aided system that yields accurate results by using deep learning techniques. This research proposed a lightweight DL-assisted robust model based on EfficientNet-B3 using depthwise separable convolutions for classifying acute lymphoblastic leukemia and normal cells in the white blood cell images dataset. The main objective of the proposed lightweight EfficientNet-B3 is to utilize less trainable parameters to enhance the performance and efficiency of the classification of leukemia. Furthermore, two publicly available datasets are considered to evaluate the effectiveness and generalization of the proposed lightweight EfficientNet-B3. In addition, different measures are employed to evaluate the effectiveness and efficiency of the proposed classifier for the accurate and reliable classification of leukaemia cells. In addition, a detailed analysis is given to evaluate and compare the performance and efficiency of the proposed with existing pre-trained and ensemble DL classifiers. Based on experimental results, it is investigated that the proposed model for image classification achieves better performance and outperforms the existing benchmark DL and other ensemble classifiers. Moreover, our finding suggests that the proposed lightweight EfficientNet-B3 model is reliable and generalized to facilitate clinical research and practitioners for leukemia detection.

The Role of Mutations Genes NPM1, FLT3, DNMT3A, CEBPA, IDH1& IDH2 in Cytogenetically Normal Acute Myeloid Leukemia

Cytogenetically normal acute myeloid leukemia (CN-AML) is a type of hematopoietic tissue (bone marrow) cancer called acute myeloid leukemia. In normal bone marrow, early blood cells called hematopoietic stem cells develop into several types of blood cells: white blood cells (leukocytes) that protect the body against infection, red blood cells (erythrocytes) that carry oxygen and platelets (thrombocytes) play a role in blood coagulation, in acute myeloid leukemia, the bone marrow produces a large number of abnormal and immature white blood cells called myeloid blasts. Instead of becoming normal white blood cells, myeloid blasts turn into blood cancer cells.