Diagnosis Of Acute Lymphoblastic Leukemia Research Articles

Virtual reality as a non-medical tool in the treatment of anxiety, pain, and perception of time in children in the maintenance phase of acute lymphoblastic leukemia treatment.

Management of pediatric cancer patients involves invasive procedures such as punctures, injections, catheter placements, and chemotherapy which can generate fatigue, nausea, vomiting, anxiety, and pain. Virtual Reality (VR) is a nonpharmacological intervention classified as a cognitive-behavioral method to relieve symptoms. We designed a crossover protocol and included 20 patients between 9 and 12 years old; ten were male. All patients had acute lymphoblastic leukemia diagnosis and were treatedwith St. Jude's XV protocol in the maintenance phase. Pain and anxiety were measured with validated scales in the pediatric population. Although we used a small group of patients, we found statistical difference in the reduction of anxiety and perception of time. These results open a window to non-pharmacological treatments and show a strategy to improve quality of life in children inside the hospital.

Flow cytometric detection of leukemic blasts in Libyan pediatric patients with acute lymphoblastic leukemia

ABSTRACT The diagnosis of acute lymphoblastic leukemia (ALL), which is the most common type of cancer in children, has become more accurate with the use of flow cytometry. Here, this technology was used to immunophenotype leukemic cells in peripheral blood samples from Libyan pediatric ALL patients. We recruited 152 newly diagnosed patients at Tripoli Medical Center (Tripoli, Libya) by morphological examination of blood and bone marrow. Twenty-three surface and cytoplasmic antigen markers were used to characterize B and T cells in circulating blood cells by four-color flow cytometry. Six children (3.9%) turned out to have biphenotypic acute leukemia, 88 (57.9%) had B ALL, and 58 (38.1%) had T ALL. There were 68 cases of pro-B ALL CD10-positive (44.7%), 8 cases of pro-B ALL CD10-negative (5.2%), 6 cases of pre-B ALL (3.9%), and 6 of mature-B ALL (3.9%). CD13 was the most commonly expressed myeloid antigen in ALL. We present immunophenotypic data for the first time describing ALL cases in Libya. The reported results indicate that the most common subtype was pro-B ALL, and the frequency of T-ALL subtype was higher compared to previous studies. Six cases were positive for both myeloid and B lymphoid markers. Our findings may provide the basis for future studies to correlate immunophenotypic profile and genetic characteristics with treatment response among ALL patients.

The influence of cytotoxic drugs on the immunophenotype of blast cells in paediatric B precursor acute lymphoblastic leukaemia.

Flow cytometry plays is important in the diagnosis of acute lymphoblastic leukaemia (ALL) and when antigen-specific immunotherapy is indicated. We have investigated the effects of prednisolone, vincristine, daunorubicin, asparaginase and methotrexate on the antigen expression on blast cells that could influence the planning of antigen-specific therapy as well as risk-based treatment assignment. Patients aged ≤ 17 years with de novo B-cell ALL (B-ALL) were enrolled in the study. Blast cells were isolated and exposed in vitro to 5 individual cytotoxic drugs in logarithmically increasing concentrations. Then, the expression of CD10, CD19, CD20, CD27, CD34, CD45, CD58, CD66c and CD137 antigens was determined by quantitative flow cytometry. Cytotoxic drugs caused dose-dependent or dose-independent modulation of antigen expression. Daunorubicin caused a dose-dependent down-modulation of CD10, CD19, CD34, CD45 and CD58 and an up-modulation of CD137. Vincristine caused a dose-dependent down-modulation of CD19 and CD58 and an up-modulation of CD45. Daunorubicin also caused dose-independent down-modulation of CD27 and prednisolone down-modulation of CD10, CD19, CD27, CD34 and CD58. Down-modulation of CD20 was detected only in relation to the specific dose of daunorubicin. The results of the study have shown that cytotoxic drugs can alter the expression of antigens that are important for immunotherapy. Importantly, daunorubicin, prednisolone and vincristine caused down-modulation of CD19 and CD58, suggesting that these drugs are better avoided during bridging therapy prior to bispecific antibodies or CAR-T cell therapy. In addition, immunophenotypic changes on blast cells induced by different drugs could also influence risk-based treatment assignment.

Acute Lymphoblastic Leukemia: About a Case Observed at the Renaissance University Hospital in N'Djamena.

Introduction: Childhood acute lymphoblastic leukaemia is the most common paediatric cancer. The objective of our work is to focus on a haematological manifestation such as pancytopenia inaugurating the picture of acute lymphoblastic leukaemia. Observation: 11-year-old AI, who has been reporting repeated anaemia and haemoglobinuria for 6 months. The clinical examination reveals an alteration of the general condition, severe mucocutaneous pallor, splenomegaly. The initial blood count showed pancytopenia. The diagnosis of acute lymphoblastic leukaemia was confirmed by a myelogram requested in the presence of pancytopenia. Conclusion: Through this observation, we report the clinical and biological manifestations of acute lymphoblastic leukaemia, emphasizing the interest in eliminating haematological malignancy in the face of any pancytopenia.

An ensemble-acute lymphoblastic leukemia model for acute lymphoblastic leukemia image classification.

The timely diagnosis of acute lymphoblastic leukemia (ALL) is of paramount importance for enhancing the treatment efficacy and the survival rates of patients. In this study, we seek to introduce an ensemble-ALL model for the image classification of ALL, with the goal of enhancing early diagnostic capabilities and streamlining the diagnostic and treatment processes for medical practitioners. In this study, a publicly available dataset is partitioned into training, validation, and test sets. A diverse set of convolutional neural networks, including InceptionV3, EfficientNetB4, ResNet50, CONV_POOL-CNN, ALL-CNN, Network in Network, and AlexNet, are employed for training. The top-performing four individual models are meticulously chosen and integrated with the squeeze-and-excitation (SE) module. Furthermore, the two most effective SE-embedded models are harmoniously combined to create the proposed ensemble-ALL model. This model leverages the Bayesian optimization algorithm to enhance its performance. The proposed ensemble-ALL model attains remarkable accuracy, precision, recall, F1-score, and kappa scores, registering at 96.26, 96.26, 96.26, 96.25, and 91.36%, respectively. These results surpass the benchmarks set by state-of-the-art studies in the realm of ALL image classification. This model represents a valuable contribution to the field of medical image recognition, particularly in the diagnosis of acute lymphoblastic leukemia, and it offers the potential to enhance the efficiency and accuracy of medical professionals in the diagnostic and treatment processes.

Outcome Disparities Among Hispanic and Non-Hispanic Patients with Acute Lymphoblastic Leukemia

Introduction: Acute lymphoblastic leukemia (ALL) is the predominant type of leukemia diagnosed in children and accounts for approximately 20% of all cases of acute leukemia in adults. In pediatric cases, ALL generally exhibits a favorable prognosis and outcome. Unfortunately, this optimistic scenario does not extend to adults, as the majority of deaths related to ALL occur in this age group. The outcomes observed in children with ALL are linked to the favorable cytogenetic profile of their leukemia. However, as individuals age, the cytogenetic profile progressively shifts towards high-risk groups. Furthermore, several epidemiologic studies have identified ethnicity, specifically Hispanic and African American backgrounds, as independent factors associated with worse outcomes. The factors contributing to these outcomes are diverse and include limited healthcare access, insurance coverage, immigration status, treatment adherence, socioeconomic status, and timeliness of treatment initiation, among others. Additionally, individuals of Hispanic ethnicity are more prone to complex cytogenetics and Central Nervous System involvement, both of which are commonly associated with a poor prognosis. Our study aimed to assess the differences in treatment outcomes among adults diagnosed with ALL by conducting a specific comparison between non-Hispanic and Hispanic individuals. Methods: In this retrospective observational study, we collected data from the electronic medical records (EMR) of patients aged 18 years and older who received a diagnosis of Acute Lymphoblastic Leukemia between February 1, 2011, and February 28, 2020 at Ascension Seton Hospitals in Texas. The collected data included sociodemographic factors such as gender, ethnicity, and age at diagnosis, as well as clinical characteristics such as cytogenetics, immunophenotype, treatment, and overall survival. Bivariate and multivariate analyses were conducted using a chi-squared test. Statistical significance was defined as a P-value less than 0.05. Results: A total of 56 patients with ALL were identified, with 45% being non-Hispanic and 55% being Hispanic. The incidence of ALL did not show significant variation based on gender or age, but it exhibited a significant difference depending on insurance status. The analysis revealed that Hispanic patients had a significantly higher likelihood of lacking insurance coverage (75%) compared to non-Hispanic patients (25%), with a statistically significant p-value of 0.001. With the exception of one non-Hispanic patient diagnosed with T-cell ALL, almost all participants in the study had B-cell ALL. Among the patients who received chemotherapy, 44% were non-Hispanic, while 56% were Hispanic. Among patients with t(9;22)/BCR-ABL1 positive ALL, non-Hispanics represented 76% and Hispanics represented 23%. Other cytogenetic abnormalities, including CRLF2-IGH fusion, IgH rearrangement, CRLF2 rearrangement, PBX1/TCF3 fusion, t(1;19) with PBX1/TCF3 fusion, ETV6 deletion, near haploidy, and 9p deletion, were more prevalent in Hispanic patients (79%) compared to non-Hispanic patients (21%). The overall survival rates at 1 year were 64% for non-Hispanic patients and 67.7% for Hispanic patients. However, at 3 years, the overall survival rates decreased to 32% for non-Hispanic patients and 25.8% for Hispanic patients. Conclusions: Our study provides important insights into the demographic and clinical characteristics of patients with ALL and highlights notable disparities between non-Hispanic and Hispanic patients. A higher proportion of Hispanic patients lacked any form of health insurance coverage at the time of diagnosis. Interestingly, the incidence of Philadelphia-positive ALL was found to be higher among non-Hispanic patients, whereas other cytogenetic abnormalities were more prevalent among Hispanic patients. Additionally, Hispanic patients experienced lower rates of overall survival at 3 years, suggesting potential disparities in outcomes. The differences observed between Hispanic and non-Hispanic patients highlight the necessity for additional investigation and tailored interventions to address the specific challenges faced by Hispanic patients, including access to healthcare, insurance coverage, and the prevalence of unfavorable cytogenetic abnormalities.

Deep Learning Models for the Diagnosis of Acute Lymphoblastic Leukemia from Bone Marrow Images : A Comprehensive Literature Review

Introduction: Acute lymphoblastic leukemia (ALL) is an aggressive blood cancer that begins in the bone marrow. It is the most common childhood cancer and requires early and accurate diagnosis for optimal treatment outcomes. Through automated image analysis of peripheral blood smears and bone marrow biopsies, artificial intelligence (AI), particularly Deep Learning (DL), have opened new avenues for improving ALL diagnosis. While bone marrow testing is the gold standard for ALL confirmation, AI applications in diagnosing ALL from bone marrow images have received less attention in the literature than peripheral blood smears. This review aims to assess the current state of AI in ALL diagnosis using bone marrow aspirates and biopsies, with a focus on Deep Learning (DL) models such as Convolutional Neural Networks (CNNs). Methods: A comprehensive literature search, conducted on June 11th, 2023, covered major medical databases, including PubMed/MEDLINE, Scopus, Embase, and Web of Science. Relevant keywords like “acute lymphoblastic leukemia,” “deep learning,” and “neural network” were employed, without time frame restrictions. Articles were included if they assessed the metrics of AI applications for ALL diagnosis in bone marrow aspirates. Articles were excluded if they: (1) had different outcomes, (2) were reviews, (3) were abstracts only, or (4) only reported peripheral blood smear metrics. Results: The search yielded 496 articles. After eliminating duplicates (282), title and abstract screening on the Rayyan platform excluded 204 records, leaving 214 articles eligible for full-text screening. Ten relevant articles were ultimately included in the review. Diverse approaches were presented to enhance diagnostic accuracy and efficiency. One study achieved 100% accuracy in classifying leukemic cells using a unique Convolutional Leaky RELU with CatBoost and XGBoost (CLR-CXG) design. Another study employed transfer learning with CNNs, obtaining 95.3% accuracy for AML, ALL, and CML classification with DenseNet121. Furthermore, the novel “i-Net” model demonstrated 99.18% validation accuracy for white blood cancer segmentation and classification. Adaptive Multi-objective CAT algorithms achieved 99.45% accuracy in detecting bone marrow cancer cells. An AI-based system using deep learning exhibited 97.2% accuracy in diagnosing ALL, while a robust fuzzy logic algorithm combined with a radial basis function neural network achieved 82.93% accuracy in diagnosing ALL in developing countries. Additionally, AI technologies based on computer microscopy demonstrated 95% and 97.5% accuracy in diagnosing ALL and minimal residual disease, respectively. A computer-aided system achieved 97.78% accuracy in classifying ALL subtypes. Conclusion: The potential of AI-driven approaches, particularly deep learning models like CNNs, in improving ALL diagnosis from bone marrow images is highlighted in this review. Because of their superior sensitivity and specificity over peripheral blood smears, bone marrow samples are the gold standard for ALL confirmation. The morphological and cellular properties of bone marrow samples provide important information for disease classification and monitoring. While DL and CNNs have shown promise in diagnosing ALL from peripheral blood smears, their use in bone marrow samples has yet to be investigated deeply. More research and external validation are required to fully realize AI's potential in ALL diagnosis from bone marrow specimens.

A combined neural network mechanism for categorizing the normal and cancer cells

The dangerous form of acute lymphocytic leukemia damages the bone marrow tissue and white blood cells. Unformed white blood cells multiply and exchange healthy cells in the bone marrow. Everything spreads fast and, if not noticed, can be dangerous in some months. As a result, computer assisted diagnosis of acute lymphocytic leukemia has the possible to protect several lives, but it needs a high-precision categorization of malignant cells, that is difficult due to the graphical similarity of malignant and normal cells. Though deep convolutional neural networks and the classic machine learning algorithms have exposed excellent results in classifying blood cell images, they have not been able to make effective use of the long-term correlation between some important image attributes and image labeling. This study introduced Long short-term memory (LSTM) to solve this difficulty. This study actually combines VGG16 neural networks and LSTM, which provide the VGG16-LSTM framework, which improves the recognizing of image content and learns the structural features of images, as well as initiate big data training in clinical image processing. The transfer learning approach was used to transfer pre-trained weight parameters to the VGG16 area in the ImageNet database, and the custom loss function was used to train and integrate the network quickly and accurately with the weight parameters. Experimental findings reveal that the suggested network approach is more relevant and effective in categorizing cancer cell images than other existing methods.

Disseminated aspergillosis due to Aspergillus flavus in a pediatric patient with a recent diagnosis of acute lymphoblastic leukemia

Disseminated aspergillosis due to Aspergillus flavus in a pediatric patient with a recent diagnosis of acute lymphoblastic leukemia

Peripheral Blood Smear Images Classification for Acute Lymphoblastic Leukemia Diagnosis with an Improved Convolutional Neural Network

Peripheral Blood Smear Images Classification for Acute Lymphoblastic Leukemia Diagnosis with an Improved Convolutional Neural Network

B-242 Fast and Cost-effective RNA Extraction in Onco-hematology Laboratories

Abstract Background Chronic myeloid leukemia (CML) is a myeloproliferative neoplasm characterized by the presence of a BCR-ABL1 rearrangement; its detection and quantification by real-time quantitative PCR (RQ-PCR) plays a central role in CML diagnosis, therapy monitoring, and sequencing to identify resistance mutations in case of therapy failure. To evaluate the molecular response of CML, BCR-ABL1 quantification must be calculated on the international scale (IS%) for which RQ-PCR has the necessary sensitivity (up to 10−5). Currently, BCR-ABL1 quantifications are ubiquitous in onco-hematology laboratories (OHLs; ∼70% of tests performed in our OHL), whereby quality, cost, and turn-around-time are of critical importance. Today, the majority of OHLs in Brazil rely on laborious manual RNA-extraction methods (e.g., Trizol) that usually require 8 mL of peripheral blood (PB) to monitor CML. Here, we report a strategy that requires only 2 mL of PB in combination with the automated magnetic DNA/RNA-extraction kit Extracta-MPTA (Loccus do Brasil) for CML diagnosis and monitoring. Methods: BCR:ABL1 was quantified and its IS% determined using our developed kit, which was calibrated using ERM-AD263 and UK-NEQAS (external quality assessment). Total RNA was extracted from 40 PB samples, after erythrocyte lysis using two different methods: 1) Leukocytes from 8 mL of PB were extracted manually using Trizol; 2) leukocytes from 2 mL of PB were automatically extracted using Extracta-MPTA (Loccus do Brasil). BCR-ABL1 quantification, BCR-ABL1 IS%, and BCR quantification (internal control) were compared statistically (paired t-test). The reproducibility was evaluated by analyzing 20 replicates; the low limit of the leucocyte number to BCR:ABL1 quantification using Extracta-MPTA were also evaluated. Median and moving means of 135 unpaired samples were used to monitor BCR-ABL1 IS% between Trizol, Extracta-MPTA, while EQA from UK-NEQAS was also evaluated using both methods. In parallel, RNA was extracted by both methods from 20 samples for the diagnosis of acute lymphoblastic leukemia (ALL) (BCR-ABL p190 transcript) and acute promyelocytic leukemia (APL) (PML-RARA transcript). Results A comparison of BRC-ABL1, BCR, and %IS in 40 paired samples showed that the results obtained from Trizol or Extracta-MPTA are comparable (paired t-test: P = 0.4222, P = 0.9263, and P = 0.071, respectively). The reproducibility was evaluated by BCR quantification (24%). A comparison of the medians from 135 unpaired samples extracted using Trizol and Extracta-MPTA revealed that BCR-ABL1 IS% distribution among our patients is stable over time (P = 0.7704; Mann Whitney: r2 = 0.0012). EQA was extracted using both methods and the results were identical. A comparison between Trizol and EXTRACTA_MPTA for ALL and APL was 100% identical (Fischer; P = 1.000). Conclusion Both RNA extraction methods are suitable for OHLs for PML-RARA and BCR-ABL (p190 and p210) detection and CML diagnosis/monitoring by BCR-ABL1 detection/quantification. By using Extracta-MPTA the amount of PB used to obtain RNA can be reduced by 75%, test results can be obtained within 4 h, and direct reagent costs are reduced by 25% with a minimum of hands-on time.

Leukocyte classification for acute lymphoblastic leukemia timely diagnosis by interpretable artificial neural network

Leukemia is a blood cancer characterized by leukocyte overproduction. Clinically, the reference for acute lymphoblastic leukemia diagnosis is a blood biopsy that allows obtain microscopic images of leukocytes, whose early-stage classification into leukemic (LEU) and healthy (HEA) may be disease predictor. Thus, the aim of this study is to propose an interpretable artificial neural network (ANN) for leukocyte classification to timely diagnose acute lymphoblastic leukemia. The “ALL_IDB2” dataset was used. It contains 260 microscopic images showing leukocytes acquired from 130 LEU and 130 HEA subjects. Each microscopic image shows a single leukocyte that was characterized by 8 morphological and 4 statistical features. An ANN was developed to distinguish microscopic images acquired from LEU and HEA subjects, considering 12 features as inputs and the local-interpretable model-agnostic explanatory (LIME) algorithm as an interpretable post-processing algorithm. The ANN was evaluated by the leave-one-out cross-validation procedure. The performance of our ANN is promising, presenting a testing area under the curve of the receiver operating characteristic equal to 87%. Being implemented using standard features and having LIME as a post-processing algorithm, it is clinically interpretable. Therefore, our ANN seems to be a reliable instrument for leukocyte classification to timely diagnose acute lymphoblastic leukemia, guaranteeing a high clinical interpretability level.

Differences between leukemic arthritis and juvenile idiopathic arthritis

ObjectivesTo determine the clinical and laboratory differences between leukemic arthritis (LA) and juvenile idiopathic arthritis (JIA) at the onset of the disease.Material and methodsPatients under 16 years of age, both genders, who presented for the first time to the pediatric rheumatology service with a diagnosis of probable JIA, with arthritis and without peripheral blood blasts, in which the final diagnosis was acute lymphoblastic leukemia (ALL) or JIA. The clinical and laboratory characteristics of the patients were compared, chi-square and relative risk were used for categorical variables, and the Mann–Whitney U and T-test for the comparison of means between groups. A binary logistic regression model was developed to differentiate leukemic arthritis from JIA.ResultsA total of 76 patients, 14 with LA and 62 with JIA, were analyzed. The mean age at diagnosis was lower in the leukemic arthritis group, the female gender prevailed in the JIA group, and the time to onset of symptoms was lower in the leukemic arthritis group. Patients with leukemic arthritis showed increased pain intensity, fever, weight loss, nocturnal diaphoresis, lymph node enlargement, hepatosplenomegaly, and pain that did not improve with analgesic administration. Laboratory parameters with statistical significance were the presence of anemia, leukopenia, and neutropenia. The platelet count was significant but in a low normal value, compared to the JIA. A binary logistic regression model was developed to differentiate leukemic arthritis from JIA. The probability associated with the statistic (Chi-square) was 0.000, and the Cox and Snell R2 and Nagelkerke R2 values were 0.615 and 1, respectively. The developed model correctly classified 100% of the cases.ConclusionsThe diagnosis of acute lymphoblastic leukemia should be ruled out in patients who present with arthritis and hematological alterations, mainly leukopenia and neutropenia, with joint pain disproportionate to the degree of arthritis, predominantly at night and that does not improve with the use of analgesics, fever, lymph nodes, and hepatosplenomegaly. Criteria are suggested to differentiate both diseases.

Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features.

Acute lymphoblastic leukemia (ALL) is one of the deadliest forms of leukemia due to the bone marrow producing many white blood cells (WBC). ALL is one of the most common types of cancer in children and adults. Doctors determine the treatment of leukemia according to its stages and its spread in the body. Doctors rely on analyzing blood samples under a microscope. Pathologists face challenges, such as the similarity between infected and normal WBC in the early stages. Manual diagnosis is prone to errors, differences of opinion, and the lack of experienced pathologists compared to the number of patients. Thus, computer-assisted systems play an essential role in assisting pathologists in the early detection of ALL. In this study, systems with high efficiency and high accuracy were developed to analyze the images of C-NMC 2019 and ALL-IDB2 datasets. In all proposed systems, blood micrographs were improved and then fed to the active contour method to extract WBC-only regions for further analysis by three CNN models (DenseNet121, ResNet50, and MobileNet). The first strategy for analyzing ALL images of the two datasets is the hybrid technique of CNN-RF and CNN-XGBoost. DenseNet121, ResNet50, and MobileNet models extract deep feature maps. CNN models produce high features with redundant and non-significant features. So, CNN deep feature maps were fed to the Principal Component Analysis (PCA) method to select highly representative features and sent to RF and XGBoost classifiers for classification due to the high similarity between infected and normal WBC in early stages. Thus, the strategy for analyzing ALL images using serially fused features of CNN models. The deep feature maps of DenseNet121-ResNet50, ResNet50-MobileNet, DenseNet121-MobileNet, and DenseNet121-ResNet50-MobileNet were merged and then classified by RF classifiers and XGBoost. The RF classifier with fused features for DenseNet121-ResNet50-MobileNet reached an AUC of 99.1%, accuracy of 98.8%, sensitivity of 98.45%, precision of 98.7%, and specificity of 98.85% for the C-NMC 2019 dataset. With the ALL-IDB2 dataset, hybrid systems achieved 100% results for AUC, accuracy, sensitivity, precision, and specificity.

An exploratory study of distractions during the induction phase of pediatric procedural sedation with propofol.

Distractions are a leading cause of disturbance to workflow during medical care. Distractions affecting the anesthetic team in the operating room are frequent and have a negative impact on patient care one-fifth of the time. The objective of this study was to evaluate the frequency, source, target, and impact of distractions during the induction phase of pediatric procedural sedation outside the operating room. Distractions were analyzed during propofol induction for oncology procedures from 45 video recordings. Distraction was defined as any event that disturbs or has potential to disturb the sedation team from performing their primary tasks. The type of distraction was cataloged into communication, coordination, extraneous events, equipment, layout, and usability. A five-point Likert scale was used to quantify the impact on the sedation team or its members. All patients had a diagnosis of acute lymphocytic leukemia and had a mean age of 8.4 years. Five hundred and sixty-seven distractions occurred and averaged 12.6 events (±5.6) per induction (mean induction time 3 min 12 s). Extraneous events were most common, accounting for 55% (312/567) of all distractions. Most distractions had an impact on the sedation team's workflow, resulting in multitasking (46%, n = 262), and in either brief or complete disruption from a primary task (17%). Sedation nurses were impacted most often, 62% of the time. Coordination and usability issues resulted in the greatest negative impact, mean ± SD, 3.7 ± 1.0 and 3.5 ± 0.9, respectively. There was no significant association between distractions and adverse events or induction length. Distractions are common during procedural sedation, with extraneous events being most frequent. Coordination issues within the team and usability problems had the greatest negative impact on sedation team workflow. Nurses were the most frequent target. Distractions impacted sedation team workflow but had no association with patient outcomes.

Unusual Diffuse and Heterogeneous FDG Uptake in Appendicular Bone Marrow Space in a Patient with Acute Lymphoblastic Leukemia.

We describe a case of F-18-2-fluoro-2-deoxyglucose (FDG) uptake in the appendicular bones on a positron emission tomography/computed tomography (PET/CT) scan in a 20-year-old woman with a diagnosis of acute lymphoblastic leukemia. An FDG PET/CT was performed on this patient because of fever of unknown origin, revealing diffuse and heterogeneous FDG uptake in the bone marrow space of both humeri, femurs, and tibiae. The patient underwent magnetic resonance imaging, which demonstrated bone infarction with heterogeneous high, intermediate, and dark signal intensities on T1- and T2-weighted images in the same areas.

Вирусные инфекции – причина или триггер развития гемобластоза?

In recent decades, there has been an increase in the number of oncological cases in children. Most common diseases are hemoblastoses, which are characterized by clinical polymorphism in the disease onset, so differential diagnosis should be made. This article presents a clinical case of serous meningitis in a child with increasing dynamics of lymphocytic pleocytosis. The clinical diagnosis of lymphocytic choriomeningitis was based on clinical and anamnestic data (accommodation in the private sector, moderate severity of the general infectious syndrome in combination with severe cerebral and meningeal symptoms), laboratory data (increasing lymphocytic pleocytosis and cell-protein dissociation in the cerebrospinal fluid). The therapy correction included antiviral therapy, which helped to achieve normalization of the cerebrospinal fluid and clinical recovery of the child from an acute neuroinfectious disease in a short time. This clinical case is unique in that 2.5 months after the disease onset, the patient developed convergent strabismus and was hospitalized. Routine blood analysis revealed 25% of blast forms. Further, the diagnosis of acute lymphoblastic leukemia was confirmed, and specific therapy was initiated. It is generally recognized that a prolonged infectious process can be a trigger for both autoimmune and paraneoplastic processes, so long-time patient observation should be provided by a group of specialists (pediatrician, neurologist, infectionist, etc.), and control laboratory examination should be done. Key words: herpesviruses, children, leukemia, lymphocytic choriomeningitis, meningitis, cytoflavin

Latent Space Representational Learning of Deep Features for Acute Lymphoblastic Leukemia Diagnosis

Acute Lymphoblastic Leukemia (ALL) is a fatal malignancy that is featured by the abnormal increase of immature lymphocytes in blood or bone marrow. Early prognosis of ALL is indispensable for the effectual remediation of this disease. Initial screening of ALL is conducted through manual examination of stained blood smear microscopic images, a process which is time-consuming and prone to errors. Therefore, many deep learning-based computer-aided diagnosis (CAD) systems have been established to automatically diagnose ALL. This paper proposes a novel hybrid deep learning system for ALL diagnosis in blood smear images. The introduced system integrates the proficiency of autoencoder networks in feature representational learning in latent space with the superior feature extraction capability of standard pretrained convolutional neural networks (CNNs) to identify the existence of ALL in blood smears. An augmented set of deep image features are formed from the features extracted by GoogleNet and Inception-v3 CNNs from a hybrid dataset of microscopic blood smear images. A sparse autoencoder network is designed to create an abstract set of significant latent features from the enlarged image feature set. The latent features are used to perform image classification using Support Vector Machine (SVM) classifier. The obtained results show that the latent features improve the classification performance of the proposed ALL diagnosis system over the original image features. Moreover, the classification performance of the system with various sizes of the latent feature set is evaluated. The retrieved results reveal that the introduced ALL diagnosis system superiorly compete the state of the art.

Escalated doses of methotrexate and L‐asparaginase in critically ill children with a concurrent diagnosis of acute lymphoblastic leukemia and appendicitis

Escalated doses of methotrexate and L‐asparaginase in critically ill children with a concurrent diagnosis of acute lymphoblastic leukemia and appendicitis

Risk Factors for Readmission Following Febrile Neutropenia in Pediatric Oncology Patients.

Febrile neutropenia is the most common reason for admission from the emergency department for pediatric oncology patients. We identified pediatric inpatients age 1 to 21 years with an International Classification of Diseases, Ninth Revision (ICD-9) diagnosis code of malignancy and either fever with neutropenia or fever alone over a 6-year period (2007-2012) using the PHIS+ database. We evaluated factors associated with readmission within 7 days after index hospitalization. There were 4029 index hospitalizations among 2349 patients in 6 hospitals, 294 encounters (7.3%) were followed by readmission within 7 days. Factors associated with increased odds of readmission included being in the lowest quartile for median household income (odds ratio [OR]=1.64, P =0.009), diagnosis of acute lymphoblastic leukemia (OR=1.37, P =0.016), lack of anerobic coverage during index hospitalization (OR=1.48, P =0.026), and absolute neutrophil count <200cells/μL at discharge from index hospitalizations (OR=1.55, P =0.008). Patients who required readmission had a longer median length of stay and greater hospitalization costs during the index hospitalization. There was a trend towards increasing hospitalization rates for febrile neutropenia over time. While absolute neutrophil count is incorporated into many risk stratification strategies for fever management, further work should focus on addressing socioeconomic factors which may impact readmission rates.