Quantitative features to assist in the diagnostic assessment of chronic lymphocytic leukemia progression†.

The use of artificial intelligence methods in the image-based diagnostic assessment of hematological diseases is a growing trend in recent years. In these methods, the selection of quantitative features that describe cytological characteristics plays a key role. They are expected to add objectivity and consistency among observers to the geometric, color, or texture variables that pathologists usually interpret from visual inspection. In a recent paper in The Journal of Pathology, El Hussein, Chen et al proposed an algorithmic procedure to assist pathologists in the diagnostic evaluation of chronic lymphocytic leukemia (CLL) progression using whole-slide image analysis of tissue samples. The core of the procedure was a set of quantitative descriptors (biomarkers) calculated from the segmentation of cell nuclei, which was performed using a convolutional neural network. These biomarkers were based on clinical practice and easily calculated with reproducible tools. They were used as input to a machine learning algorithm that provided classification in one of the stages of CLL progression. Works like this can contribute to the integration into the workflow of clinical laboratories of automated diagnostic systems based on the morphological analysis of histological slides and blood smears. © 2021 The Pathological Society of Great Britain and Ireland.

Serous body fluid evaluation using the new automated haematology analyser Mindray BC-6800Plus.

OBJECTIVES: Cellular analysis of body fluids (BF) has clinical relevance in several medical conditions. The objective of this study is twofold: (1) evaluate the analytical performance of the BF mode of Mindray BC-6800 Plus compared to manual counts under microscopy and (2) analyse if the high-fluorescent cell counts provided by the analyser (HF-BF) are useful to detect malignancy. METHODS: A total of 285 BF was analysed: 250 corresponding to patients without neoplasia and 35 to patients with malignant diseases. Manual differential counts were performed in BF with ≥250 cells/µL. Percentages and absolute counts were obtained on the BC-6800Plus for total nucleated cells (TC-BF), mononuclear, polymorphonuclear and HF-BF. Statistical analysis was performed using Mann-Whitney U-test, Spearman's correlation, Passing-Bablok regression, Bland-Altman graph and ROC curve. RESULTS: To compare manual and automatic total cell counts, samples were divided in three groups: <250, 250-1,000 and >1,000 cells/µL. Correlation was good in all cases (r=0.72, 0.73 and 0.92, respectively) without significant differences between both methods (p=0.65, 0.39 and 0.30, respectively). The concordance between methods showed values of 90%. Considering malignant samples, median HF-BF values showed significant higher values (102 cells/µL) with respect to non-malignant (4 cells/µL) (p<0.001). The cut-off value of 8.5 HF-BF/µL was able to discriminate samples containing malignant cells showing sensitivity and specificity values of 89 and 71%, respectively. Considering both, HF-BF and TC-BF values, sensitivity and specificity values were 100 and 53%, respectively. CONCLUSIONS: This study reveals that the Mindray BC-6800Plus offers an accurate and acceptable performance, showing results consistent with the manual method. It is recommended to consider both HF-BF and TC-BF values for the screening of the microscopic evaluation to ensure the detection of all malignant samples.

A Deep Learning Approach for Segmentation of Red Blood Cell Images and Malaria Detection.

Malaria is an endemic life-threating disease caused by the unicellular protozoan parasites of the genus Plasmodium. Confirming the presence of parasites early in all malaria cases ensures species-specific antimalarial treatment, reducing the mortality rate, and points to other illnesses in negative cases. However, the gold standard remains the light microscopy of May-Grünwald-Giemsa (MGG)-stained thin and thick peripheral blood (PB) films. This is a time-consuming procedure, dependent on a pathologist's skills, meaning that healthcare providers may encounter difficulty in diagnosing malaria in places where it is not endemic. This work presents a novel three-stage pipeline to (1) segment erythrocytes, (2) crop and mask them, and (3) classify them into malaria infected or not. The first and third steps involved the design, training, validation and testing of a Segmentation Neural Network and a Convolutional Neural Network from scratch using a Graphic Processing Unit. Segmentation achieved a global accuracy of 93.72% over the test set and the specificity for malaria detection in red blood cells (RBCs) was 87.04%. This work shows the potential that deep learning has in the digital pathology field and opens the way for future improvements, as well as for broadening the use of the created networks.

Study of the analytical performance at different concentrations of hematological parameters using Spanish EQAS data.

Background External quality assessment programs are one of the currently available tools to evaluate the analytical performance of clinical laboratories, where the measurement error (ME) obtained can be compared with quality specifications to evaluate possible deviations. The objective of this work was to analyze the ME behavior over the analytical range to assess the need to establish concentration-dependent specifications. Methods A total of 389,000 results from 585 laboratories and 2628 analyzers were collected from the Spanish external quality assessment schemes (EQAS) in hematology during the years 2015-2016. The parameters evaluated included white blood cells, red blood cells, hemoglobin, hematocrit, mean corpuscular volume, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, platelets, prothrombin time, activated partial thromboplastin time, neutrophils, lymphocytes, monocytes, eosinophils, basophils, reticulocytes, hemoglobin A2, antithrombin, factor VIII, protein C and von Willebrand factor. The 90th percentile of ME was calculated for every concentration evaluated of each parameter. Results We found a significant variation in the analytical performance of leukocytes, platelets, neutrophils, lymphocytes, monocytes, eosinophils, basophils, prothrombin time, reticulocytes, hemoglobin A2, antithrombin and protein C. Furthermore, this ME variation may not allow complying with the same biological variability requirements within the whole analytical range studied. Conclusions Our work shows the importance of implementing concentration-dependent specifications which can help laboratories to use proper criteria for quality specifications selection and for a better external quality control results evaluation.

Feature Analysis and Automatic Identification of Leukemic Lineage Blast Cells and Reactive Lymphoid Cells from Peripheral Blood Cell Images.

BACKGROUND: Automated peripheral blood (PB) image analyzers usually underestimate the total number of blast cells, mixing them up with reactive or normal lymphocytes. Therefore, they are not able to discriminate between myeloid or lymphoid blast cell lineages. The objective of the proposed work is to achieve automatic discrimination of reactive lymphoid cells (RLC), lymphoid and myeloid blast cells and to obtain their morphologic patterns through feature analysis. METHODS: In the training stage, a set of 696 blood cell images was selected in 32 patients (myeloid acute leukemia, lymphoid precursor neoplasms and viral or other infections). For classification, we used support vector machines, testing different combinations of feature categories and feature selection techniques. Further, a validation was implemented using the selected features over 220 images from 15 new patients (five corresponding to each category). RESULTS: Best discrimination accuracy in the training was obtained with feature selection from the whole feature set (90.1%). We selected 60 features, showing significant differences (P < 0.001) in the mean values of the different cell groups. Nucleus-cytoplasm ratio was the most important feature for the cell classification, and color-texture features from the cytoplasm were also important. In the validation stage, the overall classification accuracy and the true-positive rates for RLC, myeloid and lymphoid blast cells were 80%, 85%, 82% and 74%, respectively. CONCLUSION: The methodology appears to be able to recognize reactive lymphocytes well, especially between reactive lymphocytes and lymphoblasts.

Women doctors and their careers in a large university hospital in Spain at the beginning of the 21st century.

BACKGROUND: The feminization of medicine has risen dramatically over the past decades. The aim of this article was to compare the advance of women with that of men and determine the differences between hierarchical status and professional recognition achieved by women in medicine. METHODS: A retrospective study was carried out in the Hospital Clinic Barcelona, Spain, of the period from 1996 to 2008. Data relating to temporary and permanent positions, hierarchy and career promotion achieved, specialty, age and the sex of the participants were analysed with the ANOVA test and logistic regression using the generalized estimated equation. RESULTS: After completion of specialist training, fewer women than men doctors obtained permanent positions. The ratios between the proportions of women and men remained 1.2 for permanent non-hierarchal medical positions and below 0.2 for higher hierarchal levels. Fewer women than men with hierarchy and fewer women than men achieved the rank of consultant. Promotion to consultant and senior consultant was lower than that to senior specialist, being higher in specialties with gender parity and in masculinised specialties. On comparing the two genders using a statistical model, the probability of continuous promotion decreased with the year of the application and the age of the applicant, except in women. CONCLUSIONS: Despite the number of women training as specialists having increased to 50%, women remained in temporary positions twofold longer than men. Compared to women, men showed significant representation in hierarchal medical positions, and women showed a lower adjusted probability of internal professional promotion throughout the study period.

A deep learning approach for automatic recognition of abnormalities in the cytoplasm of neutrophils.

BACKGROUND AND OBJECTIVES: This study aims to develop and evaluate NeuNN, a system based on convolutional neural networks (CNN) and generative adversarial networks (GAN) for the automatic identification of normal neutrophils and those containing several types of inclusions or showing hypogranulation. METHODS: From peripheral blood smears, a set of 5605 digital images was obtained with neutrophils belonging to seven categories: Normal neutrophils (NEU), Hypogranulated (HYP) or containing cryoglobulins (CRY), Döhle bodies (DB), Howell-Jolly body-like inclusions (HJBLI), Green-blue inclusions of death (GBI) and phagocytosed bacteria (BAC). The dataset utilized in this study has been made publicly available. The class of GBI was augmented using synthetic images generated by GAN. The NeuNN classification model is based on an EfficientNet-B7 architecture trained from scratch. RESULTS: NeuNN achieved an overall performance of 94.3% accuracy on the test data set. Performance metrics, including sensitivity, specificity, precision, F1-Score, Jaccard index, and Matthews correlation coefficient indicated overall values of 94%, 99.1%, 94.3%, 94.3%, 89.6%, and 93.6%, respectively. CONCLUSIONS: The proposed approach, combining data augmentation and classification techniques, allows for automated identification of morphological findings in neutrophils, such us inclusions or hypogranulation. The system can be used as a support tool for clinical pathologists to detect these specific abnormalities with clinical relevance.

Performance of the new MC-80 automated digital cell morphology analyser in detection of normal and abnormal blood cells: Comparison with the CellaVision DM9600.

INTRODUCTION: Mindray MC-80 is an automated system for digital imaging of white blood cells (WBCs) and their pre-classification. The objective of this work is to analyse its performance comparing it with the CellaVision® DM9600. METHODS: A total of 445 samples were used, 194 normal and 251 abnormal: acute leukaemia (100), myelodysplastic syndromes/myeloproliferative neoplasms (33), lymphoid neoplasms (50), plasma cell neoplasms (14), infections (49) and thrombocytopenia (5). WBC pre-classification values with the MC-80 and DM9600 were compared with (1) the microscope, (2) Mindray BC-6800Plus differentials in only normal samples, and (3) confirmed or reclassified images (post-classification). Pearson's correlation, Lin's concordance, Passing-Bablok regression, and Bland-Altman plots were used. Sensitivity, specificity, positive (PPV) and negative (NPV) predictive values for abnormal cells using the MC-80 were calculated. RESULTS: The PPV and NPV were above 98% and 99%, for normal samples. For immature granulocytes (IG), NPV and PPV were 100% and 74.2%. When comparing the WBC differentials using the MC-80, the microscope and the BC-6800Plus, no differences were found except for basophils and IG. Our results showed good agreement between the pre- and post-classification of normal WBC, including IG, quantified by high correlation and concordance values (0.91-1). Sensitivity and specificity for blasts were 0.984 and 0.640. The MC-80 detected abnormal lymphocytes in 30% of the smears from patients with lymphoid neoplasm. Plasma cell identification was better using the DM9600. The sensitivity and specificity for erythroblast detection were 1 and 0.890. CONCLUSION: We found that the MC-80 shows high performance for WBC differentials for both normal samples and patients with haematological diseases.

Automatic generation of artificial images of leukocytes and leukemic cells using generative adversarial networks (syntheticcellgan).

BACKGROUND AND OBJECTIVES: Visual analysis of cell morphology has an important role in the diagnosis of hematological diseases. Morphological cell recognition is a challenge that requires experience and in-depth review by clinical pathologists. Within the new trend of introducing computer-aided diagnostic tools in laboratory medicine, models based on deep learning are being developed for the automatic identification of different types of cells in peripheral blood. In general, well-annotated large image sets are needed to train the models to reach a desired classification performance. This is especially relevant when it comes to discerning between cell images in which morphological differences are subtle and when it comes to low prevalent diseases with the consequent difficulty in collecting cell images. The objective of this work is to develop, train and validate SyntheticCellGAN (SCG), a new system for the automatic generation of artificial images of white blood cells, maintaining morphological characteristics very close to real cells found in practice in clinical laboratories. METHODS: SCG is designed with two sequential generative adversarial networks. First, a Wasserstein structure is used to transform random noise vectors into low resolution images of basic mononuclear cells. Second, the concept of image-to-image translation is used to build specific models that transform the basic images into high-resolution final images with the realistic morphology of each cell type target: 1) the five groups of normal leukocytes (lymphocytes, monocytes, eosinophils, neutrophils and basophils); 2) atypical promyelocytes and hairy cells, which are two relevant cell types of complex morphology with low abundance in blood smears. RESULTS: The images of the SCG system are evaluated with four experimental tests. In the first test we evaluated the generated images with quantitative metrics for GANs. In the second test, morphological verification of the artificial images is performed by expert clinical pathologists with 100% accuracy. In the third test, two classifiers based on convolutional neural networks (CNN) previously trained with images of real cells are used. Two sets of artificial images of the SCG system are classified with an accuracy of 95.36% and 94%, respectively. In the fourth test, three CNN classifiers are trained with artificial images of the SCG system. Real cells are identified with an accuracy ranging from 87.7% to 100%. CONCLUSIONS: The SCG system has proven effective in creating images of all normal leukocytes and two low-prevalence cell classes associated with diseases such as acute promyelocyte leukemia and hairy cell leukemia. Once trained, the system requires low computational cost and can help augment high-quality image datasets to improve automatic recognition model training for clinical laboratory practice.

Automatic normalized digital color staining in the recognition of abnormal blood cells using generative adversarial networks.

BACKGROUND AND OBJECTIVES: Combining knowledge of clinical pathologists and deep learning models is a growing trend in morphological analysis of cells circulating in blood to add objectivity, accuracy, and speed in diagnosing hematological and non-hematological diseases. However, the variability in staining protocols across different laboratories can affect the color of images and performance of automatic recognition models. The objective of this work is to develop, train and evaluate a new system for the normalization of color staining of peripheral blood cell images, so that it transforms images from different centers to map the color staining of a reference center (RC) while preserving the structural morphological features. METHODS: The system has two modules, GAN1 and GAN2. GAN1 uses the PIX2PIX technique to fade original color images to an adaptive gray, while GAN2 transforms them into RGB normalized images. Both GANs have a similar structure, where the generator is a U-NET convolutional neural network with ResNet and the discriminator is a classifier with ResNet34 structure. Digitally stained images were evaluated using GAN metrics and histograms to assess the ability to modify color without altering cell morphology. The system was also evaluated as a pre-processing tool before cells undergo a classification process. For this purpose, a CNN classifier was designed for three classes: abnormal lymphocytes, blasts and reactive lymphocytes. RESULTS: Training of all GANs and the classifier was performed using RC images, while evaluations were conducted using images from four other centers. Classification tests were performed before and after applying the stain normalization system. The overall accuracy reached a similar value around 96% in both cases for the RC images, indicating the neutrality of the normalization model for the reference images. On the contrary, it was a significant improvement in the classification performance when applying the stain normalization to the other centers. Reactive lymphocytes were the most sensitive to stain normalization, with true positive rates (TPR) increasing from 46.3% - 66% for the original images to 81.2% - 97.2% after digital staining. Abnormal lymphocytes TPR ranged from 31.9% - 95.7% with original images to 83% - 100% with digitally stained images. Blast class showed TPR ranges of 90.3% - 94.4% and 94.4% - 100%, for original and stained images, respectively. CONCLUSIONS: The proposed GAN-based normalization staining approach improves the performance of classifiers with multicenter data sets by generating digitally stained images with a quality similar to the original images and adaptability to a reference staining standard. The system requires low computation cost and can help improve the performance of automatic recognition models in clinical settings.

Kimura disease as an uncommon cause of persistent hypereosinophilia: a diagnostic challenge.

Kimura disease (KD) is an unusual inflammatory disease of unknown etiology. Despite being described many years ago, KD might cause diagnostic difficulty or be confused with other conditions. Here, we present a 33-year-old Filipino woman who was referred to our hospital for evaluation of persistent eosinophilia and intense pruritus. Blood analysis and peripheral blood smear review showed high eosinophil counts (3.8 x109/L, 40%) that did not show morphological abnormalities. Besides, high serum IgE concentration was detected (33,528 kU/L). Serological tests were positive for Toxocara canis and treatment with albendazol was initiated. Nevertheless, increased eosinophil counts were still present after several months, alongside with high serum IgE concentrations and intense pruritus. During her follow-up, an inguinal adenopathy was detected. The biopsy revealed lymphoid hyperplasia with reactive germinal centers and massive eosinophil infiltration. Proteinaceous deposits of eosinophilic material were also observed. All these findings, together with peripheral blood eosinophilia and high IgE concentrations, confirmed the diagnosis of KD. The diagnosis of KD should be considered in the differential diagnosis of long-standing unexplained eosinophilia in association with high IgE concentrations, pruritus and lymphadenopathies.

A Deep Learning Approach for the Morphological Recognition of Reactive Lymphocytes in Patients with COVID-19 Infection.

Laboratory medicine plays a fundamental role in the detection, diagnosis and management of COVID-19 infection. Recent observations of the morphology of cells circulating in blood found the presence of particular reactive lymphocytes (COVID-19 RL) in some of the infected patients and demonstrated that it was an indicator of a better prognosis of the disease. Visual morphological analysis is time consuming, requires smear review by expert clinical pathologists, and is prone to subjectivity. This paper presents a convolutional neural network system designed for automatic recognition of COVID-19 RL. It is based on the Xception71 structure and is trained using images of blood cells from real infected patients. An experimental study is carried out with a group of 92 individuals. The input for the system is a set of images selected by the clinical pathologist from the blood smear of a patient. The output is the prediction whether the patient belongs to the group associated with better prognosis of the disease. A threshold is obtained for the classification system to predict that the smear belongs to this group. With this threshold, the experimental test shows excellent performance metrics: 98.3% sensitivity and precision, 97.1% specificity, and 97.8% accuracy. The system does not require costly calculations and can potentially be integrated into clinical practice to assist clinical pathologists in a more objective smear review for early prognosis.

Atypical lymphoid cells circulating in blood in COVID-19 infection: morphology, immunophenotype and prognosis value.

AIMS: Atypical lymphocytes circulating in blood have been reported in COVID-19 patients. This study aims to (1) analyse if patients with reactive lymphocytes (COVID-19 RL) show clinical or biological characteristics related to outcome; (2) develop an automatic system to recognise them in an objective way and (3) study their immunophenotype. METHODS: Clinical and laboratory findings in 36 COVID-19 patients were compared between those showing COVID-19 RL in blood (18) and those without (18). Blood samples were analysed in Advia2120i and stained with May Grünwald-Giemsa. Digital images were acquired in CellaVisionDM96. Convolutional neural networks (CNNs) were used to accurately recognise COVID-19 RL. Immunophenotypic study was performed throughflow cytometry. RESULTS: Neutrophils, D-dimer, procalcitonin, glomerular filtration rate and total protein values were higher in patients without COVID-19 RL (p<0.05) and four of these patients died. Haemoglobin and lymphocyte counts were higher (p<0.02) and no patients died in the group showing COVID-19 RL. COVID-19 RL showed a distinct deep blue cytoplasm with nucleus mostly in eccentric position. Through two sequential CNNs, they were automatically distinguished from normal lymphocytes and classical RL with sensitivity, specificity and overall accuracy values of 90.5%, 99.4% and 98.7%, respectively. Immunophenotypic analysis revealed COVID-19 RL are mostly activated effector memory CD4 and CD8 T cells. CONCLUSION: We found that COVID-19 RL are related to a better evolution and prognosis. They can be detected by morphology in the smear review, being the computerised approach proposed useful to enhance a more objective recognition. Their presence suggests an abundant production of virus-specific T cells, thus explaining the better outcome of patients showing these cells circulating in blood.