The hairy cell leukaemia oxymoron: monocytotic monocytopenia

Bigorra, Laura; Larriba, Iciar; Gutiérrez-Gallego, Ricardo

doi:10.1515/cclm-2020-0253

Cited by 5 publications

(5 citation statements)

References 7 publications

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“…As early as 2008, HCL was classified by WHO into classic hairy cell leukemia (cHCL) and hairy cell leukemia variant (HCL-v) based on immunophenotype and molecular biological characteristics. Generally speaking, mononucleosis in peripheral blood is a specific manifestation of cHCL [9], and related studies have shown that cHCL is prone to increase the difficulty of bone marrow puncture [10].…”

Section: Discussionmentioning

confidence: 99%

“…The specific mutation BRAF V600E is present in the vast majority of cHCL patients, but not in HCL-v. Of course, to avoid false negative results, high-sensitivity detection techniques (such as allele-specific polymerase chain reaction or second-generation sequencing) should be preferred for identification. In contrast to cHCL, HCL-v is usually positive for CD103, and its absence suggests splenic limbic lymphoma [9]. Correspondingly, HCL-v is often associated with cytogenetic abnormalities, such as tumor protein p53 (TP53) mutation, 7q deletion and chromosome 5 amplification, and so on.…”

Section: Discussionmentioning

confidence: 99%

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Clinical analysis of hairy cell leukemia: the rare indolent hematological malignancy

Huang

2024

Am J Transl Res

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Clinical analysis of hairy cell leukemia: the rare indolent hematological malignancy

Huang

2024

Am J Transl Res

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“…Pulmonary abnormalities are usually due to infections or adenopathy, but leukemic involvement of the lungs was reported in 2 of 21 autopsied patients [ 42 ]. Monocytopenia is a classic feature, although HCL cells are often mistaken for monocytes based on their size [ 43 ]. The most sensitive and specific method for diagnosis is blood or bone marrow aspirate flow cytometry, which should show bright positivity for CD11c, CD22, and CD20.…”

Section: How To Diagnose Hclmentioning

confidence: 99%

Diagnosis and treatment of hairy cell leukemia as the COVID-19 pandemic continues

Kreitman

Arons

2022

Blood Reviews

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Hairy cell leukemia (HCL) is an indolent B-cell malignancy, usually driven by the BRAF V600E mutation. For 30 years, untreated and relapsed HCL was successfully treated with purine analogs, but minimal residual disease (MRD) remained in most patients, eventually causing relapse. Repeated purine analogs achieve decreasing efficacy and increasing toxicity, particularly to normal T-cells. MRD-free complete remissions (CRs) are more common using rituximab with purine analogs in both 1 st -line and relapsed settings. BRAF inhibitors and Ibrutinib can achieve remission, but due to persistence of MRD, must be used chronically to prevent relapse. BRAF inhibition combined with Rituximab can achieve high MRD-free CR rates. Anti-CD22 recombinant immunotoxin moxetumomab pasudotox is FDA-approved in the relapsed setting and is unique in achieving high MRD-free CR rates as a single-agent. Avoiding chemotherapy and rituximab may be important in ensuring both recovery from COVID-19 and successful COVID-19 vaccination, an area of continued investigation.

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“…The former because hairy cells are misinterpreted as monocytes in the DxH 800, and previous studies have resulted in specific CPD flags for the identification of hairy cells within the abnormal monocyte cluster. 21 Mantle cell lymphoma B-cell neoplasms were disregarded because information on cyclin D1 was not available in the suspected cases for the diagnosis confirmation.…”

Section: Data Collectionmentioning

confidence: 99%

A Physician-in-the-Loop Approach by Means of Machine Learning for the Diagnosis of Lymphocytosis in the Clinical Laboratory

Bigorra

Larriba²,

Gutiérrez-Gallego

2021

Archives of Pathology &Amp; Laboratory Medicine

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Context.— The goal of the lymphocytosis diagnosis approach is its classification into benign or neoplastic categories. Nevertheless, a nonnegligible percentage of laboratories fail in that classification. Objective.— To design and develop a machine learning model by using objective data from the DxH 800 analyzer, including cell population data, leukocyte and absolute lymphoid counts, hemoglobin concentration, and platelet counts, besides age and sex, with classification purposes for lymphocytosis diagnosis. Design.— A total of 1565 samples were included from 10 different lymphoid categories grouped into 4 diagnostic categories: normal controls (458), benign causes of lymphocytosis (567), neoplastic lymphocytosis (399), and spurious causes of lymphocytosis (141). The data set was distributed in a 60-20-20 scheme for training, testing, and validation stages. Six machine learning models were built and compared, and the selection of the final model was based on the minimum generalization error and 10-fold cross validation accuracy. Results.— The selected neural network classifier rendered a global 10-class classification validation accuracy corresponding to 89.9%, which, considering the aforementioned 4 diagnostic categories, presented a diagnostic impact accuracy corresponding to 95.8%. Finally, a prospective proof of concept was performed with 100 new cases with a global diagnostic accuracy corresponding to 91%. Conclusions.— The proposed machine learning model was feasible, with a high benefit-cost ratio, as the results were obtained within the complete blood count with differential. Finally, the diagnostic impact with high accuracies in both model validation and proof of concept encourages exploration of the model for real-world application on a daily basis.

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The hairy cell leukaemia oxymoron: monocytotic monocytopenia

Cited by 5 publications

References 7 publications

Clinical analysis of hairy cell leukemia: the rare indolent hematological malignancy

Clinical analysis of hairy cell leukemia: the rare indolent hematological malignancy

Diagnosis and treatment of hairy cell leukemia as the COVID-19 pandemic continues

A Physician-in-the-Loop Approach by Means of Machine Learning for the Diagnosis of Lymphocytosis in the Clinical Laboratory

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