Classification Of Leukemia Research Articles

Combining array-assisted SERS microfluidic chips and machine learning algorithms for clinical leukemia phenotyping

The disease progression and treatment options of leukemia between different subtypes vary considerably, emphasizing the importance of phenotyping. However, early typing of leukemia remains challenging due to the lack of highly sensitive and specific analytical tools. Herein, we propose a SERS-based platform for the classification of acute lymphoblastic T-cell leukemia (T-ALL) and chronic myeloid leukemia (CML) through the combination of machine learning and microfluidic chips. The ordered arrays in microfluidic channels reshape the microscopic flow field and contacting interfaces, facilitating the uniform and efficient capture of tumor cells. To enable phenotypic analysis, spectrally orthogonal SERS aptamer nanoprobes were applied, providing composite spectral signatures of individual cells in accordance with surface protein expression. Further, machine learning algorithms were employed to analyze the SERS signatures automatically, resulting in an accuracy of 98.6 % for 73 clinical blood samples. The results demonstrate that this platform holds promising potential for clinical leukemia diagnosis and precision medicine.

Future directions in myelodysplastic syndromes/neoplasms and acute myeloid leukaemia classification: from blast counts to biology.

Myelodysplastic syndromes/neoplasms (MDS) and acute myeloid leukaemia (AML) are neoplastic haematopoietic cell proliferations that are diagnosed and classified based on a combination of morphological, clinical and genetic features. Specifically, the percentage of myeloblasts in the blood and bone marrow is a key feature that has historically separated MDS from AML and, together with several other morphological parameters, defines distinct disease entities within MDS. Both MDS and AML have recurrent genetic abnormalities that are increasingly influencing their definitions and subclassification. For example, in 2022, two new MDS entities were recognised based on the presence of SF3B1 mutation or bi-allelic TP53 abnormalities. Genomic information is more objective and reproducible than morphological analyses, which are subject to interobserver variability and arbitrary numeric cut-offs. Nevertheless, the integration of genomic data with traditional morphological features in myeloid neoplasm classification has proved challenging by virtue of its sheer complexity; gene expression and methylation profiling also can provide information regarding disease pathogenesis, adding to the complexity. New machine-learning technologies have the potential to effectively integrate multiple diagnostic modalities and improve on historical classification systems. Going forward, the application of machine learning and advanced statistical methods to large patient cohorts can refine future classifications by advancing unbiased and robust previously unrecognised disease subgroups. Future classifications will probably incorporate these newer technologies and higher-level analyses that emphasise genomic disease entities over traditional morphologically defined entities, thus promoting more accurate diagnosis and patient risk stratification.

A deep transfer learning based approaches for the detection and classification of acute lymphocytic leukemia using microscopic images

A deep transfer learning based approaches for the detection and classification of acute lymphocytic leukemia using microscopic images

Distinct clinical profiles and patient outcomes in aCML and CNL.

The classification of atypical chronic myeloid leukemia (aCML) and chronic neutrophilic leukemia (CNL) as a single disease entity remains a topic of debate. To elucidate the characteristics of both entities, this retrospective cohort study was conducted, encompassing 36 cases of aCML and 18 cases of CNL. We discovered that aCML and CNL presented distinct blood counts, genetics, molecular profiles and outcomes. Specifically, hemoglobin levels (P < 0.001) and platelet counts (P < 0.001) were significantly lower in aCML cases than in CNL cases, with no significant difference in mean white blood cells (P = 0.637). The proportion of abnormal karyotypes was higher in aCML cases compared with CNL cases (P = 0.010). Notably, we found that aCML and CNL showed distinct gene expression profiles by transcriptome sequencing technology. The median follow-up duration for the entire cohort was 8months (rang 0.4 to 36.6months), and the median overall survival (OS) was significantly shorter in aCML cases (7.3months, 95%CI 5.4 to 20.5months) than in CNL cases (median OS not reached). The one-year OS rate for aCML patients was 31.0% (9/29), compared to 92.9% (13/14) for CNL patients. In conclusion, our study supports the notion that aCML and CNL are indeed distinct disease entities characterized by unique hematological features and clinical outcomes.

Lysine (K)-specific methyltransferase 2A (KMT2A) rearrangements among Iraqi de novo acute myeloid leukemia

Abstract: BACKGROUND: The classification of acute myeloid leukemia (AML) has evolved extensively over the last 20 years significantly, impacting the diagnosis and prognosis of the patients. The lysine (K)-specific methyltransferase 2A (KMT2A) gene, which has more than 80 gene fusions, is present in approximately 10% of all leukemias. Most KMT2A rearrangements are associated with adverse prognosis and need heavy chemotherapy protocol upfront. OBJECTIVE: This study aims to study the prevalence of KMT2A gene fusion among Iraqi patients with AML and its association with clinical and hematological parameters and patients’ outcomes. PATIENTS, MATERIALS, AND METHODS: A prospective cohort study conducted between December 2020 and May 2022 enrolled 115 Iraqi adults newly diagnosed with AML at the Hematology Unit of Baghdad Teaching Hospital. The patients were also monitored at this facility during the study period. Genetic rearrangements were detected using the Leukemia Q-Fusion Screening Kit through Real-Time Quantitative Reverse Transcription PCR (RT-qPCR) analysis. RESULTS: KMT2A rearrangements were identified in 23 (20%) patients. The most common was t(10;11) which presented in 15 (13%) patients, followed by t(9;11) in 5 (4.3%) patients and t(11;17) in 3 (2.6%) patients. Patients with KMT2A rearrangements were significantly older and more likely to have splenomegaly. At 1-month posttreatment, they had significantly lower red blood cell counts and hemoglobin levels and higher blast percentages. Only 4.3% achieved complete remission (CR) compared to 76.1% without KMT2A rearrangements, with a significantly higher mortality rate (30.4% vs. 5.4%, P = 0.0001). Regarding the treatment response, no significant differences were observed among the different fusion types of KMT2A rearrangements. CONCLUSION: KMT2A rearrangements are more prevalent among Iraqi AML patients compared to the global trend and are associated with older age, higher rates of splenomegaly, poorer hematological recovery, and worse outcomes, regardless of the KMT2A rearrangement fusion type.

Application of Gene Expression Microarray for the Classification of Ph-Like B-Cell Acute Lymphoblastic Leukemia.

Ph-like ALL has gene expression profile similar to Ph-positive ALL but without the BCR::ABL1 fusion. The disease presents higher rates of severe clinical features and is associated with unfavorable outcomes. There is still no standard pipeline for molecular characterization of the disease, and no valid predictor gene panel is available worldwide. We performed expression microarray on 25 B-cell ALL and 6 Ph-positive B-cell ALL to cluster and identify the transcriptional signature of Ph-like ALL. qRT-PCR was used to confirm the expression of candidate genes. Four out of 25 samples (16%) shared gene expression signatures related to and clustered with control Ph-positive samples. Analysis of genes differentially expressed in Ph-like B-cell ALL and evidentially functional in normal blood cell development and leukemogenesis, we selected genes as potential biomarkers for Ph-like B-cell ALL in our dataset: ADGRE2, CD9, EPHA7, FAM129C, TCL1A, and VPREB1. Those genes were filtered by Ph-like gene signatures obtained from distinct reliable data, resulting in five genes, CA6, CHN2, JAK1, JCHAIN, and PON2, selected for validation by qRT-PCR. The Ct values of genes, including CA6 (p = 0.0017), PON2 (p = 0.0210), TCL1A (p = 0.0064), and VPREB1 (p = 0.0338), were significant in Ph-like ALL. GSEA analysis identified VPREB1 as enrichment in the KRAS signaling pathway, and several genes that interact with VPREB1 were reported as critical molecules involved in the leukemogenesis of B-cell ALL. In summary, we demonstrate using a gene expression microarray for classifying Ph-like B-cell ALL and highlight VPREB1 as a potential biomarker for this disease.

Super learner model for classifying leukemia through gene expression monitoring

Leukemia is a form of cancer that affects the bone marrow and lymphatic system, and it requires complex treatment strategies that vary with each subtype. Due to the subtle morphological differences among these types, monitoring gene expressions is crucial for accurate classification. Manual or pathological testing can be time-consuming and expensive. Therefore, data-driven methods and machine learning algorithms offer an efficient alternative for leukemia classification. This study introduced a novel super learning model that leverages heterogeneous machine learning models to analyze gene expression data and classify leukemia cells. The proposed approach incorporates an entropy-based feature importance technique to identify the gene profiles most significant to the labeling process. The strength of this super learning model lies in its final super learner, Random Forest, which effectively classifies cross-validated data from the candidate learners. Validation on a gene expression monitoring dataset demonstrates that this model outperforms other state-of-the-art models in predictive accuracy. The study contributes to the knowledge regarding the use of advanced machine learning techniques to improve the accuracy and reliability of leukemia classification using gene expression data, addressing the challenges of traditional methods that rely on clinical features and morphological examination.

Leukemia detection and classification using computer-aided diagnosis system with falcon optimization algorithm and deep learning

Leukemia is a type of blood tumour that occurs because of abnormal enhancement in WBCs (white blood cells) in the bone marrow of the human body. Blood-forming tissue cancer influences the lymphatic and bone marrow system. The early diagnosis and detection of leukaemia, i.e., the accurate difference of malignant leukocytes with little expense at the beginning of the disease, is a primary challenge in the disease analysis field. Despite the higher occurrence of leukemia, there is a lack of flow cytometry tools, and the procedures accessible at medical diagnostics centres are time-consuming. Distinct researchers have implemented computer-aided diagnostic (CAD) and machine learning (ML) methods for laboratory image analysis, aiming to manage the restrictions of late leukemia analysis. This study proposes a new Falcon optimization algorithm with deep convolutional neural network for Leukemia detection and classification (FOADCNN-LDC) technique. The main objective of the FOADCNN-LDC technique is to classify and recognize leukemia. The FOADCNN-LDC technique utilizes a median filtering (MF) based noise removal process to eradicate the image noise. Besides, the FOADCNN-LDC technique employs the ShuffleNetv2 model for the feature extraction process. Moreover, the detection and classification of the leukemia process are performed by utilizing the convolutional denoising autoencoder (CDAE) model. The FOA is implemented to select the hyperparameter of the CDAE model. The simulation process of the FOADCNN-LDC approach is performed on a benchmark medical dataset. The investigational analysis of the FOADCNN-LDC approach highlighted a superior accuracy value of 99.62% over existing techniques.

Unlocking fresh perspectives: molecular breakthroughs in pediatric acute myeloid leukemia classification and prognosis.

Unlocking fresh perspectives: molecular breakthroughs in pediatric acute myeloid leukemia classification and prognosis.

Detection and Classification of Acute Lymphoblastic Leukemia using CNN

Acute lymphoblastic leukemia is a very important cancer in childhood but quite prominent in later years of life for the genetic defects in lymphoid progenitors, which are hallmarks of the disease. In children, ALL mostly affects those aged between 2 and 6 years old and, against the background of contemporary biology knowledge and treatment approaches, is associated with more than 80% cure rates. However, approximately 20% of children with ALL relapse; therefore, there is a huge need for better risk identification and treatment optimization. In adults, ALL mainly affects B-cell precursors and is treated with chemotherapy and, in some cases, stem cell transplantation. An accurate and early diagnosis of ALL is of key importance but difficult to realize due to morphological similarities between normal cells and leukemic cells. This study, therefore, proposes a CNN model to improve diagnostic accuracy. Furthermore, it exploits the capabilities of CNN in feature extraction with Adamax Optimizer and the Categorical Cross-Entropy Loss Function to deal with imbalances and noise in the dataset. RESNET50-CNN has achieved 98.63% accuracy in classification and is hence a very strong tool in ALL detection and classification.

7. Implementation and utility of gene expression profile (GEP)-Based classification of pediatric b-lymphoblastic leukemia

7. Implementation and utility of gene expression profile (GEP)-Based classification of pediatric b-lymphoblastic leukemia

An attention-based deep learning for acute lymphoblastic leukemia classification

The bone marrow overproduces immature cells in the malignancy known as Acute Lymphoblastic Leukemia (ALL). In the United States, about 6500 occurrences of ALL are diagnosed each year in both children and adults, comprising nearly 25% of pediatric cancer cases. Recently, many computer-assisted diagnosis (CAD) systems have been proposed to aid hematologists in reducing workload, providing correct results, and managing enormous volumes of data. Traditional CAD systems rely on hematologists’ expertise, specialized features, and subject knowledge. Utilizing early detection of ALL can aid radiologists and doctors in making medical decisions. In this study, Deep Dilated Residual Convolutional Neural Network (DDRNet) is presented for the classification of blood cell images, focusing on eosinophils, lymphocytes, monocytes, and neutrophils. To tackle challenges like vanishing gradients and enhance feature extraction, the model incorporates Deep Residual Dilated Blocks (DRDB) for faster convergence. Conventional residual blocks are strategically placed between layers to preserve original information and extract general feature maps. Global and Local Feature Enhancement Blocks (GLFEB) balance weak contributions from shallow layers for improved feature normalization. The global feature from the initial convolution layer, when combined with GLFEB-processed features, reinforces classification representations. The Tanh function introduces non-linearity. A Channel and Spatial Attention Block (CSAB) is integrated into the neural network to emphasize or minimize specific feature channels, while fully connected layers transform the data. The use of a sigmoid activation function concentrates on relevant features for multiclass lymphoblastic leukemia classification The model was analyzed with Kaggle dataset (16,249 images) categorized into four classes, with a training and testing ratio of 80:20. Experimental results showed that DRDB, GLFEB and CSAB blocks’ feature discrimination ability boosted the DDRNet model F1 score to 0.96 with minimal computational complexity and optimum classification accuracy of 99.86% and 91.98% for training and testing data. The DDRNet model stands out from existing methods due to its high testing accuracy of 91.98%, F1 score of 0.96, minimal computational complexity, and enhanced feature discrimination ability. The strategic combination of these blocks (DRDB, GLFEB, and CSAB) are designed to address specific challenges in the classification process, leading to improved discrimination of features crucial for accurate multi-class blood cell image identification. Their effective integration within the model contributes to the superior performance of DDRNet.

Optical genome mapping improves the accuracy of classification, risk stratification, and personalized treatment strategies for patients with acute myeloid leukemia.

Cytogenomic characterization is crucial for the classification and risk stratification of acute myeloid leukemia (AML), thereby facilitating therapeutic decision-making. We examined the clinical utility of optical genome mapping (OGM) in 159 AML patients (103 newly diagnosed and 56 refractory/relapsed), all of whom also underwent chromosomal banding analysis (CBA), fluorescence in situ hybridization, and targeted next-generation sequencing. OGM detected nearly all clinically relevant cytogenetic abnormalities that SCG identified with >99% sensitivity, provided the clonal burden was above 20%. OGM identified additional cytogenomic aberrations and/or provided information on fusion genes in 77 (48%) patients, including eight patients with normal karyotypes and four with failed karyotyping. The most common additional alterations identified by OGM included chromoanagenesis (n = 23), KMT2A partial tandem duplication (n = 11), rearrangements involving MECOM (n = 7), NUP98 (n = 2), KMT2A (n = 2), JAK2 (n = 2), and other gene fusions in 17 patients, with 10 showing novel fusion gene partners. OGM also pinpointed fusion genes in 17 (11%) patients where chromosomal rearrangements were concurrently detected by OGM and CBA. Overall, 24 (15%) aberrations were identified exclusively by OGM and had the potential to alter AML classification, risk stratification, and/or clinical trial eligibility. OGM emerges as a powerful tool for identifying fusion genes and detecting subtle or cryptic cytogenomic aberrations that may otherwise remain undetectable by CBA.

Real-World Clinical Utility of Targeted RNA Sequencing in Leukemia Diagnosis and Management.

Gene fusions are key drivers in acute leukemia, impacting diagnosis and treatment decisions. We analyzed 264 leukemia patients using targeted RNA sequencing with conventional karyotyping and reverse transcription polymerase chain reaction (RT-PCR). Leukemic fusions were detected in 127 patients (48.1%). The new guidelines introduced additional diagnostic criteria, expanding the spectrum of gene fusions. We discovered three novel fusions (RUNX1::DOPEY2, RUNX1::MACROD2, and ZCCHC7::LRP1B). We analyzed recurrent breakpoints for the KMT2A and NUP98 rearrangements. Targeted RNA sequencing showed consistent results with RT-PCR in all tested samples. However, when compared to conventional karyotyping, we observed an 83.3% concordance rate, with 29 cases found only in targeted RNA sequencing, 7 cases with discordant results, and 5 cases found only in conventional karyotyping. For the five cases where known leukemic gene rearrangements were suspected only in conventional karyotyping, we conducted additional messenger RNA sequencing in four cases and proved no pathogenic gene rearrangements. Targeted RNA sequencing proved advantageous for the rapid and accurate interpretation of gene rearrangements. The concurrent use of multiple methods was essential for a comprehensive evaluation. Comprehensive molecular analysis enhances our understanding of leukemia's genetic basis, aiding diagnosis and classification. Advanced molecular techniques improve clinical decision-making, offering potential benefits.

Leukemia Detection using Machine Learning Algorithms: Current trends and future directions

The process of medical diagnosis of leukemia cases is a complex process and requires diligent efforts. It is done by examining samples under a microscope and distinguishing the number, shape, size, and morphological features of white blood cells. This process takes a long time to predict leukemia. The professional skills and experience of the pathologist may also influence this procedure as a large number of overlapping structures and conditions, distractions, fatigue, and limitations in the human visual system can lead to an inappropriate diagnosis. Machine and deep learning approaches have been found to be effective strategies for enhancing the precision and efficiency of diagnosis and classification of medical images, including those of microscopic blood cells. In this paper, we will review the recent studies of leukemia detection and/or classification in the period of (2015 - 2023) in machine and deep learning and the combining of them. The review of these schemes is carried out in a systematic manner. In order to achieve the desired objective, segmentation schemes can be classified into four main categories: supervised machine learning techniques, unsupervised machine learning techniques, deep learning approaches, and traditional image processing techniques. Classification approaches can be categorized into three main groups: classical machine learning, deep learning, and a combination of both. CNN-based categorization systems can be further classified into three categories: conventional CNN, transfer learning, and additional developments in CNN. This paper also provides a concise analysis of these strategies and their significance in the categorization of leukemia. Ultimately, a comprehensive examination is conducted to elucidate the current state of research in this particular domain.

Leukemia Detection using Machine Learning Algorithms: Current trends and future directions

The process of medical diagnosis of leukemia cases is a complex process and requires diligent efforts. It is done by examining samples under a microscope and distinguishing the number, shape, size, and morphological features of white blood cells. This process takes a long time to predict leukemia. The professional skills and experience of the pathologist may also influence this procedure as a large number of overlapping structures and conditions, distractions, fatigue, and limitations in the human visual system can lead to an inappropriate diagnosis. Machine and deep learning approaches have been found to be effective strategies for enhancing the precision and efficiency of diagnosis and classification of medical images, including those of microscopic blood cells. In this paper, we will review the recent studies of leukemia detection and/or classification in the period of (2015 - 2023) in machine and deep learning and the combining of them. The review of these schemes is carried out in a systematic manner. In order to achieve the desired objective, segmentation schemes can be classified into four main categories: supervised machine learning techniques, unsupervised machine learning techniques, deep learning approaches, and traditional image processing techniques. Classification approaches can be categorized into three main groups: classical machine learning, deep learning, and a combination of both. CNN-based categorization systems can be further classified into three categories: conventional CNN, transfer learning, and additional developments in CNN. This paper also provides a concise analysis of these strategies and their significance in the categorization of leukemia. Ultimately, a comprehensive examination is conducted to elucidate the current state of research in this particular domain.

Improved convolutional neural network model for leukemia classification using EfficientNetV2M and bayesian optimization

Leukemia is a health condition in which the body produces too many abnormal white blood cells or leukocytes. Leukemia can affect both children and adults. Early diagnosis of leukemia faces significant challenges, as diagnostic methods are time consuming, require experienced medical experts, and are expensive. Previous studies have been conducted using deep learning approaches, but it is still rare to find a model that shows the best performance and uses optimization methods to classify leukemia diseases. Therefore, a Convolutional Neural Network (CNN) model with EfficientNetV2M architecture and Bayesian Optimization is proposed as the main method assisted by ImageDataGenerator in preprocessing. This study shows a significant impact of Bayesian optimization with good Accuracy, Precision, Recall and F1-Score results of 91.37%, 93.00%, 87.00%, 89.00%, respectively, which are expected to improve the performance of the model in previous studies in classifying leukemia diseases.

Acute leukemia prediction and classification using convolutional neural network and generative adversarial network

Acute Leukemia (AL) is a type of cancer that affects the blood cells and can be fatal if not detected and treated early. Flow cytometry is a key means of detecting AL, but requires manual processing by trained professionals, applying the manual gating method to huge amounts of raw data, making it time-consuming and labor-intensive. This study uses the deep learning Convolutional Neural Network (CNN) method for classification prediction based on two-dimensional graph for the parameter combinations of flow cytometry data to differentiate AL as normal, Acute Myeloid Leukemia (AML) and Acute Lymphoblastic Leukemia (ALL) three categories. This approach normalizes raw data, removes outliers, and further augments data using Generative Adversarial Networks (GAN) to generate two-dimensional scattergrams of different parameter combinations generated by four methods. We then assess post-training classification prediction accuracy to identify the most effective preprocessing method and parameter combination. Experimental results obtain accuracy rates of 73–86 %. In the two-dimensional scatter plot of different parameter combinations generated by the four methods, the combination of CD3 and CD7 cell population parameters had the best classification and prediction accuracy, with image classification and identification reaching a high of 86 % through the use of GAN-based data augmentation. Advances in medical testing technology has reduced analysis times and increased data throughput. At the same time, artificial intelligence techniques are increasingly used for analysis and detection, performing large numbers of repetitive actions to reduce the potential for subjective assessment error from manual analysis, thus allowing for the earlier detection and treatment of disease.

Leukemia Detection and Classification Based on Machine Learning and CNN: A Review

Advancements in data mining methods have significantly improved disease diagnosis, particularly in the realm of leukemia detection. Leukemia, a complex cancer affecting white blood cells, poses significant challenges in diagnosis and management due to its diverse manifestations. Various machine learning algorithms, including Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), Random Forests (RF), Decision Trees (DTs), K-Nearest Neighbors (K-NN), Logistic regression (LR) and Naïve Bayes (NB) classifiers, have been employed to accurately classify leukemia cases based on diverse datasets and image analyses. This paper provides a comprehensive overview and comparison of these classification techniques, highlighting their effectiveness in diagnosing different leukemia subtypes. Additionally, the paper discusses the methodology and findings of several studies focusing on leukemia detection, emphasizing the significance of machine learning in enhancing diagnostic accuracy and treatment planning. Furthermore, it explores the challenges and future directions in leveraging machine learning for leukemia diagnosis, including the need for standardized datasets, algorithm refinement, and integration with clinical data for personalized treatment strategies.

A Comprehensive Assessment and Classification of Acute Lymphocytic Leukemia

Leukemia is a form of blood cancer that results in an increase in the number of white blood cells in the body. The correct identification of leukemia at any stage is essential. The current traditional approaches rely mainly on field experts’ knowledge, which is time consuming. A lengthy testing interval combined with inadequate comprehension could harm a person’s health. In this situation, an automated leukemia identification delivers more reliable and accurate diagnostic information. To effectively diagnose acute lymphoblastic leukemia from blood smear pictures, a new strategy based on traditional image analysis techniques with machine learning techniques and a composite learning approach were constructed in this experiment. The diagnostic process is separated into two parts: detection and identification. The traditional image analysis approach was utilized to identify leukemia cells from smear images. Finally, four widely recognized machine learning algorithms were used to identify the specific type of acute leukemia. It was discovered that Support Vector Machine (SVM) provides the highest accuracy in this scenario. To boost the performance, a deep learning model Resnet50 was hybridized with this model. Finally, it was revealed that this composite approach achieved 99.9% accuracy.