Automatic recognition of different types of acute leukaemia in peripheral blood by image analysis

Boldú, Laura; Merino, Anna; Alférez, Santiago; Molina, Ángel; Acevedo, Andrea; Rodellar, José

doi:10.1136/jclinpath-2019-205949

Cited by 49 publications

(18 citation statements)

References 30 publications

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“…In contrast, in our B‐ALL surveys containing small sized blast cells with “lymphoid” morphology in the smear, even though most of laboratories reported blast cells, guiding the diagnosis correctly towards acute leukaemia, they were interpreted as blasts of myeloid origin, and in consequence, low percentages of the proposed diagnosis were coincident with the RR. It is well known that morphological differentiation among different blast cell lineages (myeloid or lymphoid) is a difficult task 12,13 . With respect to our results in T‐ALL, other publications have previously shown that lymphoid blast cells and abnormal lymphoid cells are the most difficult to identify by the participants in proficiency testing surveys 1,3,5 …”

Section: Discussionsupporting

confidence: 55%

Peripheral blood morphology review and diagnostic proficiency evaluation by a new Spanish EQAS during the period 2011‐2019

Alcaraz‐Quiles

Molina

Laguna³

et al. 2020

Int J Lab Hematology

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Introduction The Spanish Haematology and Haemotherapy Society organizes peripheral blood smear review scheme, focused on the evaluation of diagnostic proficiency of participants by blood cell morphology analysis. The objective was to evaluate the efficacy of this scheme as an educational tool to improve the diagnostic proficiency of the participants. Methods During 2011‐2019, 54 peripheral blood smears, alongside with patient details such as age, sex, blood cell counts and relevant clinical information, were sent to an average of 125 ± 13 laboratories per year. A number of 44 shipments were selected to analyse whether successive surveys of the same disease may lead to an improvement in the diagnostic success rate proposed by the laboratories. Participants were asked to select the most relevant morphological abnormalities, alongside the diagnostic orientation. Agreement of participant responses with RR was evaluated. Results Spanish laboratories showed a diagnostic proficiency greater than 80% in acute myeloid leukaemia, including acute promyelocytic leukaemia, mature B‐cell neoplasms (hairy cell leukaemia and splenic marginal zone lymphoma), chronic myeloid leukaemia, sickle cell disease, Bernard‐Soulier syndrome and infectious mononucleosis. It was important to note the significant improvement over the time in the successive shipments of the same disease, with a 31% and 13% increase in their diagnostic orientation success rate for acute myeloid leukaemia and acute promyelocytic leukaemia cases, respectively, 15% for mantle cell lymphoma and 6% for sickle cell disease. Conclusions The present study provides evidence that peripheral blood smear review scheme can be a valid educational tool to improve the clinical pathologist skills in blood morphology and haematological diagnosis.

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Section: Discussionsupporting

confidence: 55%

Peripheral blood morphology review and diagnostic proficiency evaluation by a new Spanish EQAS during the period 2011‐2019

Alcaraz‐Quiles

Molina

Laguna³

et al. 2020

Int J Lab Hematology

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“…Examples are the classification of abnormal lymphocytes and blasts associated with lymphomas and leukaemia, respectively. 21 30 …”

Section: Discussionmentioning

confidence: 99%

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

Merino¹,

Vlagea

Molina

et al. 2020

J Clin Pathol

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AimsAtypical 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.MethodsClinical 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.ResultsNeutrophils, 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.ConclusionWe 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.

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“…Besides, Shafique et al [6] further classified samples with ALL subtypes, according to the size of the cell and the nature of its nucleus. However, dealing with the identification of all subtypes of leukemia is more challenging task than simple binary classification [13]. To the best of our knowledge, there is no automatic recognition approach dealing with all subtypes of leukemia.…”

Section: Introductionmentioning

confidence: 99%

Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network

et al. 2019

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Leukemia is a fatal cancer and has two main types: Acute and chronic. Each type has two more subtypes: Lymphoid and myeloid. Hence, in total, there are four subtypes of leukemia. This study proposes a new approach for diagnosis of all subtypes of leukemia from microscopic blood cell images using convolutional neural networks (CNN), which requires a large training data set. Therefore, we also investigated the effects of data augmentation for an increasing number of training samples synthetically. We used two publicly available leukemia data sources: ALL-IDB and ASH Image Bank. Next, we applied seven different image transformation techniques as data augmentation. We designed a CNN architecture capable of recognizing all subtypes of leukemia. Besides, we also explored other well-known machine learning algorithms such as naive Bayes, support vector machine, k-nearest neighbor, and decision tree. To evaluate our approach, we set up a set of experiments and used 5-fold cross-validation. The results we obtained from experiments showed that our CNN model performance has 88.25% and 81.74% accuracy, in leukemia versus healthy and multiclass classification of all subtypes, respectively. Finally, we also showed that the CNN model has a better performance than other wellknown machine learning algorithms.

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Automatic recognition of different types of acute leukaemia in peripheral blood by image analysis

Cited by 49 publications

References 30 publications

Peripheral blood morphology review and diagnostic proficiency evaluation by a new Spanish EQAS during the period 2011‐2019

Peripheral blood morphology review and diagnostic proficiency evaluation by a new Spanish EQAS during the period 2011‐2019

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

Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network

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