Swarm Learning for decentralized and confidential clinical machine learning.

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning-a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.

CRISPR-based rapid molecular diagnostic tests for fusion-driven leukemias.

ABSTRACT: Fusion oncogenes can be cancer-defining molecular alterations that are essential for diagnosis and therapy selection.1,2 Rapid and accessible molecular diagnostics for fusion-driven leukemias such as acute promyelocytic leukemia (APL), Philadelphia chromosome-positive acute lymphoblastic leukemia, and chronic myeloid leukemia (CML) are unavailable, creating a barrier to timely diagnosis and effective targeted therapy in many health care settings, including community hospitals and low-resource environments. We developed CRISPR-based RNA-fusion transcript detection assays using SHERLOCK (specific high-sensitivity enzymatic reporter unlocking) for the diagnosis of fusion-driven leukemias. We validated these assays using diagnostic samples from patients with APL and CML from academic centers and dried blood spots from low-resource environments, demonstrating 100% sensitivity and specificity. We identified assay optimizations to enable the use of these tests outside of tertiary cancer centers and clinical laboratories, enhancing the potential impact of this technology. Rapid point-of-care diagnostics can improve outcomes for patients with cancer by expanding access to therapies for highly treatable diseases that would otherwise lead to serious adverse outcomes due to delayed or missed diagnoses.

International Consensus Classification of Myeloid Neoplasms and Acute Leukemias: integrating morphologic, clinical, and genomic data.

The classification of myeloid neoplasms and acute leukemias was last updated in 2016 within a collaboration between the World Health Organization (WHO), the Society for Hematopathology, and the European Association for Haematopathology. This collaboration was primarily based on input from a clinical advisory committees (CACs) composed of pathologists, hematologists, oncologists, geneticists, and bioinformaticians from around the world. The recent advances in our understanding of the biology of hematologic malignancies, the experience with the use of the 2016 WHO classification in clinical practice, and the results of clinical trials have indicated the need for further revising and updating the classification. As a continuation of this CAC-based process, the authors, a group with expertise in the clinical, pathologic, and genetic aspects of these disorders, developed the International Consensus Classification (ICC) of myeloid neoplasms and acute leukemias. Using a multiparameter approach, the main objective of the consensus process was the definition of real disease entities, including the introduction of new entities and refined criteria for existing diagnostic categories, based on accumulated data. The ICC is aimed at facilitating diagnosis and prognostication of these neoplasms, improving treatment of affected patients, and allowing the design of innovative clinical trials.

Use, variability, and justification of eligibility criteria for phase II and III clinical trials in acute leukemia.

Clinical trial eligibility criteria can unfairly exclude patients or unnecessarily expose them to known risks if criteria are not concordant with drug safety. There are few data evaluating the extent to which acute leukemia eligibility criteria are justified. We analyzed criteria and drug safety data for front-line phase II and/or III acute leukemia trials with start dates 1/1/2010-12/31/2019 registered on clinicaltrials.gov. Multivariable analyses assessed concordance between criteria use and safety data (presence of criteria with a safety signal, or absence of criteria without a signal), and differences between criteria and safety-based limits. Of 250 eligible trials, concordant use of ejection fraction criteria was seen in 34.8%, corrected QT level (QTc) in 22.4%, bilirubin in 68.4%, aspartate transaminase/alanine aminotransferase (AST/ALT) in 58.8%, renal function in 68.4%, human immunodeficiency virus (HIV) in 54.8%, and hepatitis B and C in 42.0% and 41.2%. HIV and hepatitis B and C criteria use was concordant with safety data (adjusted Odds Ratios 2.04 [95%CI: 1.13, 3.66], 2.64 [95%CI: 1.38, 5.04], 2.27 [95%CI: 1.20, 4.32]) but organ function criteria were not (all P>0.05); phase III trials were not more concordant. Bilirubin criteria limits were the same as safety-based limits in 16.0% of trials, AST/ALT in 18.1%, and renal function in 13.9%; in 75.7%, 51.4%, and 56.5% of trials, criteria were more restrictive, respectively, by median differences of 0.2, 0.5, and 0.5 times the upper limits of normal. We found limited drug safety justifications for acute leukemia eligibility criteria. These data define criteria use and limits that can be rationally modified to increase patient inclusion and welfare.

Ubiquitin-specific proteases (USPs) in leukemia: a systematic review.

BACKGROUND: Leukemia, a type of blood cell cancer, is categorized by the type of white blood cells affected (lymphocytes or myeloid cells) and disease progression (acute or chronic). In 2020, it ranked 15th among the most diagnosed cancers and 11th in cancer-related deaths globally, with 474,519 new cases and 311,594 deaths (GLOBOCAN2020). Research into leukemia's development mechanisms may lead to new treatments. Ubiquitin-specific proteases (USPs), a family of deubiquitinating enzymes, play critical roles in various biological processes, with both tumor-suppressive and oncogenic functions, though a comprehensive understanding is still needed. AIM: This systematic review aimed to provide a comprehensive review of how Ubiquitin-specific proteases are involved in pathogenesis of different types of leukemia. METHODS: We systematically searched the MEDLINE (via PubMed), Scopus, and Web of Science databases according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PRISMA) to identify relevant studies focusing on the role of USPs in leukemia. Data from selected articles were extracted, synthesized, and organized to present a coherent overview of the subject matter. RESULTS: The review highlights the crucial roles of USPs in chromosomal aberrations, cell proliferation, differentiation, apoptosis, cell cycle regulation, DNA repair, and drug resistance. USP activity significantly impacts leukemia progression, inhibition, and chemotherapy sensitivity, suggesting personalized diagnostic and therapeutic approaches. Ubiquitin-specific proteases also regulate gene expression, protein stability, complex formation, histone deubiquitination, and protein repositioning in specific leukemia cell types. CONCLUSION: The diagnostic, prognostic, and therapeutic implications associated with ubiquitin-specific proteases (USPs) hold significant promise and the potential to transform leukemia management, ultimately improving patient outcomes.

Massive Bone Marrow Infiltration by Disseminated Alveolar Rhabdomyosarcoma Mimicking Acute Leukemia.

RMS is a malignant tumor of soft tissues affecting primarily children and adolescents. Around 6% to 23% RMS patients present bone marrow infiltration but leukemia-like involvement is very rare; in these patients cytomorphology on bone marrow smears can lead to misdiagnosis. Differential diagnosis with alveolar RMS should be kept in mind in every pediatric patient presenting with a marked bone marrow involvement in the absence of typical lymphoproliferative findings.

Leukemia cancer cells and immune cells derived-exosomes: Possible roles in leukemia progression and therapy.

Exosomes have a significant impact on tumor survival, proliferation, metastasis, and recurrence. They also open up new therapeutic options and aid in the pathological identification and diagnosis of cancers. Exosomes have been shown in numerous studies to be essential for facilitating cell-to-cell communication. In B-cell hematological malignancies, the proteins and RNAs that are encased by circulating exosomes are thought to represent prospective sources for therapeutic drugs as well as biomarkers for diagnosis and prognosis. Additionally, exosomes can offer a "snapshot" of the tumor and the metastatic environment at any given point in time. In this review study, we concluded that leukemia-derived exosomes could be utilized as prognostic, diagnostic, and therapeutic biomarkers for individuals suffering from leukemia. Moreover, clinical studies have demonstrated that immune cells like dendritic cells create exosomes, which have the ability to activate the immune system against leukemia.

Clinical Characteristics and Diagnosis of Ph-Positive Mixed Phenotype Acute Leukemia.

BACKGROUND: The goal was to improve the clinical cognition of Ph-positive mixed phenotype acute leukemia and avoid misdiagnosis or delayed diagnosis. METHODS: The clinical manifestations and laboratory results (bone marrow cell morphology, multiparameter flow cytometry, and cytogenetics) of a case of Ph-positive mixed phenotype acute leukemia were analyzed, and related literature was reviewed. RESULTS: Blood routine: WBC 386.35 x 109/L, HGB 117.00 g/L, PLT 31 x 109/L; 80% of the original cells can be seen by artificial classification. Morphological examination of bone marrow cells showed that the proliferation of nucleated cells was obviously active, and the original cells accounted for 76%. The size of the original cells was somewhat uniform, most of the cells had less mass, were stained light grayish blue, the cytoplasm particles were not obvious, the nuclei were mostly round or quasi-round, some of them showed distortion and nuclear notch, and the chromatin was coarse. Some of the cells were rich in mass, small azurin granules were seen, the nuclei were regular, most of them were round, the chromatin was fine, the myeloperoxidase and esterase staining were negative, the eosinophils accounted for 2.5%, and the basophils accounted for 0.5%. Flow cytometry immunotyping: Two groups of abnormal cells were seen in the bone marrow. 1. A group included 12.32% of nuclear cells and showed abnormal myeloid primitive cell phenotype. Main expression: CD117, CD34, CD38, HLA-DR, CD33, CD64, CD123, weak expression: CD13, CD19. 2. The other group included 45.61% of the nuclear cells and had a B-lymphoblastic phenotype. Main expression: CD34, CD38, HLA-DR, CD123, CD19, CD10, CD9, cCD79a, TDT, weak expression of CD13, CD22. Mixed phenotype acute leukemia (M/B) immunophenotype was considered. Chromosome: 46,XY,t(9; 22)(q34;q11.2) [20]. BCR-ABL (P210) fusion gene was positive. CONCLUSIONS: Mixed phenotype acute leukemia (MPAL) is a rare type of malignant hematologic disease. Its diagnosis is based on the comprehensive evaluation of bone marrow cell morphology, immunophenotype, molecular and cytogenetic features.

Patterns of the Leukemia Records that were Referred to the Regional Laboratory in Makkah, Saudi Arabia.

BACKGROUND: Leukemia is the fifth most common cancer in Saudi Arabia. The aim of this study was to assess the various patterns of leukemia associated with age, gender, and nationality in this region. METHODS: This cross-sectional study was conducted at the regional laboratory in Makkah, Saudi Arabia, from April to November 2023. Descriptive statistics were presented as frequencies and percentages by using the GraphPad Prism software. RESULTS: This study included 107 patients, and the results showed that leukemia cases were higher in males than females and more prevalent in older patients (above 50 years of age). Overall, acute myeloid leukemia (AML) and acute lymphocytic leukemia (ALL) were the most common types of leukemia among Saudi patients. In addition, AML was the most prevalent type of leukemia in males and females, followed by ALL. Data also revealed that ALL was the most common type in the younger population, whereas AML was highly prevalent in older patients. CONCLUSIONS: In conclusion, this study provides valuable information about the patterns of leukemia reported at the regional laboratory in Makkah and will help in designing management and preventive approaches for these patients. This epidemiological investigation is also valuable for establishing proper medical databases.

Utilizing Deep Feature Fusion for Automatic Leukemia Classification: An Internet of Medical Things-Enabled Deep Learning Framework.

Acute lymphoblastic leukemia, commonly referred to as ALL, is a type of cancer that can affect both the blood and the bone marrow. The process of diagnosis is a difficult one since it often calls for specialist testing, such as blood tests, bone marrow aspiration, and biopsy, all of which are highly time-consuming and expensive. It is essential to obtain an early diagnosis of ALL in order to start therapy in a timely and suitable manner. In recent medical diagnostics, substantial progress has been achieved through the integration of artificial intelligence (AI) and Internet of Things (IoT) devices. Our proposal introduces a new AI-based Internet of Medical Things (IoMT) framework designed to automatically identify leukemia from peripheral blood smear (PBS) images. In this study, we present a novel deep learning-based fusion model to detect ALL types of leukemia. The system seamlessly delivers the diagnostic reports to the centralized database, inclusive of patient-specific devices. After collecting blood samples from the hospital, the PBS images are transmitted to the cloud server through a WiFi-enabled microscopic device. In the cloud server, a new fusion model that is capable of classifying ALL from PBS images is configured. The fusion model is trained using a dataset including 6512 original and segmented images from 89 individuals. Two input channels are used for the purpose of feature extraction in the fusion model. These channels include both the original and the segmented images. VGG16 is responsible for extracting features from the original images, whereas DenseNet-121 is responsible for extracting features from the segmented images. The two output features are merged together, and dense layers are used for the categorization of leukemia. The fusion model that has been suggested obtains an accuracy of 99.89%, a precision of 99.80%, and a recall of 99.72%, which places it in an excellent position for the categorization of leukemia. The proposed model outperformed several state-of-the-art Convolutional Neural Network (CNN) models in terms of performance. Consequently, this proposed model has the potential to save lives and effort. For a more comprehensive simulation of the entire methodology, a web application (Beta Version) has been developed in this study. This application is designed to determine the presence or absence of leukemia in individuals. The findings of this study hold significant potential for application in biomedical research, particularly in enhancing the accuracy of computer-aided leukemia detection.

[Current situation and prospect of diagnosis and therapy of childhood acute leukemia in China].

Acute leukemia is the most common hematopoietic stem cell malignant tumor in children, which ranks in the top one of the incidence of tumor in children, it is a major disease that affects the growth and survival of children. With the continuous improvement of medical diagnosis and treatment and the extensive development of immunotherapy, the survival rate and quality of life of children with acute leukemia have been significantly improved. In recent years, three cooperative groups of childhood leukemia have been established in China, and a series of high-level research results have been published. In the future, efforts should be made to promote the process of standardization and homogenization in the diagnosis and treatment of children's acute leukemia, explore the monitoring targets of sensitive residual diseases, and find the best treatment for refractory/recurrent cases. Speeding up the clinical research of new drugs will be an urgent problem and development direction in the field of acute leukemia diagnosis and treatment in children.

[Basics of FCM analysis for leukemia diagnosis].

Flow cytometry (FCM) remains an essential test in the diagnosis of leukemia despite advances in genomic testing. However, the role of FCM results as a risk factor is already extremely limited. International diagnostic criteria for leukemia already prioritize diagnosis based on genetic abnormalities, with FCM diagnosis only serving as an aid to morphological diagnosis for subtypes without genetic abnormalities. However, rapid lineage diagnosis of leukemia by FCM remains important for selecting initial treatment. FCM is also an important tool for evaluating response to molecular targeted therapy, which requires repeated measurements and rapid results. Furthermore, FCM enables prediction of specific genetic abnormalities by immunophenotypic patterns, which could make it useful for verifying the clinical impact of genetic abnormalities detected by multi-gene panel testing.

Exploring core symptoms and interrelationships among symptoms in children with acute leukemia during chemotherapy: A network analysis.

PURPOSE: Children with acute leukemia have suffered from a considerable symptom burden during chemotherapy. However, few studies have focused on exploring the mechanisms among symptoms in children with acute leukemia. Our study aims to explore core symptoms and describe the interrelationships among symptoms in children with acute leukemia during chemotherapy. METHODS: From January 2021 to March 2023, 469 children with acute leukemia were recruited from 20 Chinese cities. The Memorial Symptom Assessment Scale 10-18 (MSAS 10-18) was used to evaluate the prevalence and severity of symptoms during chemotherapy. A network analysis was performed by the R software based on 31 symptoms. Centrality indices and density were used to explore core symptoms and describe interrelationships among symptoms in the network during chemotherapy. RESULTS: Worrying and feeling irritable were the central symptoms across the three centrality indices, including strength, closeness, and betweenness. Lack of energy was the most prevalent symptom; however, it was less central than other symptoms. The density of the "induction and remission" network significantly differed from other cycles' counterparts (p < 0.001). Global strength was greater in the " ≥ 8 years group " network than the " < 8 years group " network (p = 0.023). CONCLUSION: Network analysis provides a novel approach to identifying the core symptoms and understanding the interrelationships among symptoms. Our study indicates the need to assess emotional symptoms in children with acute leukemia during chemotherapy, especially during the induction and remission phases, as well as in older children. Future research is imperative to construct trajectories of dynamic symptom networks and centrality indices in longitudinal data to investigate the causal relationships among symptoms.

Leukemia Cutis in Skin of Color.

The various presentations of many dermatologic conditions among various skin types are slowly being elucidated throughout the recent years. These differences present as an issue as it leads to delayed diagnosis, treatment, and poorer quality of life. Herein, we present the characteristics of leukemia cutis in a skin of color patient with diagnosed chronic myelomonocytic leukemia. Adjei S, Temiz LA, Miller AC, et al. Leukemia cutis in skin of color. J Drugs Dermatol. 2023;22(7):687-689. doi:10.36849/JDD.7020.

Electrochemical and Optical Sensors for the Detection of Chemical Carcinogens Causing Leukemia.

The incidence and mortality due to neoplastic diseases have shown an increasing tendency over the years. Based on GLOBOCAN 2020 published by the International Agency for Research on Cancer (IARC), leukemias are the thirteenth most commonly diagnosed cancer in the world, with 78.6% of leukemia cases diagnosed in countries with a very high or high Human Development Index (HDI). Carcinogenesis is a complex process initiated by a mutation in DNA that may be caused by chemical carcinogens present in polluted environments and human diet. The IARC has identified 122 human carcinogens, e.g., benzene, formaldehyde, pentachlorophenol, and 93 probable human carcinogens, e.g., styrene, diazinone. The aim of the following review is to present the chemical carcinogens involved or likely to be involved in the pathogenesis of leukemia and to summarize the latest reports on the possibility of detecting these compounds in the environment or food with the use of electrochemical sensors.

Childhood leukemia: Brazilian reality in children under fifteen years.

Leukemia is among the most prevalent cancers in children and adolescents in Brazil, affecting about 75% of children's public, especially males. Therefore, the southeast region of Brazil has a higher prevalence of cases, in which from 2019 to 2022 there were 1710 leukemia cases. In addition, the average of general notifications had higher frequency in 2021 with 1761 diagnoses, with higher emphasis on the southeast region, presenting 495 cases this year. One of the goals of public entities for 2023 is to offer better quality in diagnostic services and rapid treatment of these children, since early diagnosis increases chances of cure by 90% and favorable outcomes in treatment.

Acute leukemia and lymphoma in pregnancy, a retrospective study from a tertiary center in Malaysia.

INTRODUCTION: Most evidence about the management of cancer and hematological malignancy in pregnancy are derived from retrospective observational studies with a small sample size. Availability of sufficiently large data has enabled evidence-based decision-making in this clinical dilemma. MATERIALS AND METHODS: Retrospective study looking into patients diagnosed with acute leukemia or lymphoma in pregnancy from 1st January 2014 to 1st January 2020 in Ampang General Hospital including newly or previously diagnosed and relapsed disease RESULTS: 37 cases of acute leukemia or lymphoma in pregnancy occurred in 34 patients. Majority of acute leukemia or lymphoma in pregnancy diagnosed in 1st trimester or in the setting of previously established or relapsed disease was therapeutically terminated. Thirteen pregnancies treated with antenatal chemotherapy resulted in livebirths except one stillbirth. More adverse obstetric outcomes are observed in pregnancies that did not receive antenatal chemotherapy, but association did not reach statistical significance. There was no significant difference in fetal outcome between cohort with and without antenatal chemotherapy. No treatment related mortality was observed in pregnancies with antenatal chemotherapy. Overall survival for newly diagnosed acute leukemia in pregnancy is significantly better with antenatal chemotherapy versus no antenatal chemotherapy. CONCLUSION: Treatment with chemotherapy in 2nd trimester of pregnancy onwards appears to have tolerable risks with favorable obstetric and fetal outcome. Deferment of treatment for acute leukemia in pregnancy to after delivery may cause increased risk of maternal and fetal adverse outcome.

Biomarker interaction selection and disease detection based on multivariate gain ratio.

BACKGROUND: Disease detection is an important aspect of biotherapy. With the development of biotechnology and computer technology, there are many methods to detect disease based on single biomarker. However, biomarker does not influence disease alone in some cases. It's the interaction between biomarkers that determines disease status. The existing influence measure I-score is used to evaluate the importance of interaction in determining disease status, but there is a deviation about the number of variables in interaction when applying I-score. To solve the problem, we propose a new influence measure Multivariate Gain Ratio (MGR) based on Gain Ratio (GR) of single-variate, which provides us with multivariate combination called interaction. RESULTS: We propose a preprocessing verification algorithm based on partial predictor variables to select an appropriate preprocessing method. In this paper, an algorithm for selecting key interactions of biomarkers and applying key interactions to construct a disease detection model is provided. MGR is more credible than I-score in the case of interaction containing small number of variables. Our method behaves better with average accuracy [Formula: see text] than I-score of [Formula: see text] in Breast Cancer Wisconsin (Diagnostic) Dataset. Compared to the classification results [Formula: see text] based on all predictor variables, MGR identifies the true main biomarkers and realizes the dimension reduction. In Leukemia Dataset, the experiment results show the effectiveness of MGR with the accuracy of [Formula: see text] compared to I-score with accuracy [Formula: see text]. The results can be explained by the nature of MGR and I-score mentioned above because every key interaction contains a small number of variables in Leukemia Dataset. CONCLUSIONS: MGR is effective for selecting important biomarkers and biomarker interactions even in high-dimension feature space in which the interaction could contain more than two biomarkers. The prediction ability of interactions selected by MGR is better than I-score in the case of interaction containing small number of variables. MGR is generally applicable to various types of biomarker datasets including cell nuclei, gene, SNPs and protein datasets.

Multiplex technologies for the assessment of minimal residual disease and low-level mutation detection in leukaemia: mass spectrometry versus next-generation sequencing.

With the focus of leukaemia management shifting to the implications of low-level disease burden, increasing attention is being paid on the development of highly sensitive methodologies required for detection. There are various techniques capable of identification of measurable residual disease (MRD) either evidencing as relevant mutation detection [e.g. nucleophosmin 1 (NPM1) mutation] or trace levels of leukaemic clonal populations. The vast majority of these methods only permit detection of a single clone or mutation. However, mass spectrometry and next-generation sequencing enable the interrogation of multiple genes simultaneously, facilitating a more complete genomic profile. In the present review, we explore the methodologies of both techniques in conjunction with the important advantages and limitations associated with each assay. We also highlight the evidence and the various instances where either technique has been used and propose future strategies for MRD detection.

OMIP - 081: A new 21-monoclonal antibody 10-color panel for diagnostic polychromatic immunophenotyping.

The 10-color panel consisting of 21 monoclonal antibodies (mAbs) is developed as a one-tube panel to detect leukemia and lymphoma cells in all hematopoietic cell lineages. In particular, this tube is mentioned for a fast screening to identify aberrant cells in samples suspected for malignant cell localization and to enable comprehensive immunophenotyping of samples with low cell counts. The panel contains mAbs for selection of the populations and mAbs against target antigens on the various hematopoietic maturation stages. Due to the limited number of PMTs in most used flow cytometers for clinical purposes, stacking of conjugates in one color is needed to include all relevant markers for simultaneous analysis of the aberrant cells. The 21-mAb panel is tested on peripheral blood (PB), and bone marrow (BM) samples and enables an efficient and correct identification of hematological malignancies. This panel improves the diagnostic potential.