ArticleLeukemia2018
Automated database-guided expert-supervised orientation for immunophenotypic diagnosis and classification of acute leukemia.
Article in Leukemia, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 56 citations in OpenAlex.
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- Multiple Myeloma Laboratory Diagnostics Made Simple: Practical Insights and Key Recommendations.Journal of clinical medicine · 2025Review
- Mixed-phenotype acute leukemia, not otherwise specified, rare types B/T leukemia: a case report in the Jordanian Royal Medical Services.Journal of medicine and life · 2025Article
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- A Chronological Overview of Using Deep Learning for Leukemia Detection: A Scoping Review.Cureus · 2024Article
- Novel Tools for Diagnosis and Monitoring of AML.Current oncology (Toronto, Ont.) · 2023Review
- Flow cytometric analysis of myelodysplasia: Pre-analytical and technical issues-Recommendations from the European LeukemiaNet.Cytometry. Part B, Clinical cytometry · 2023Article
- Quality Assessment of a Large Multi-Center Flow Cytometric Dataset of Acute Myeloid Leukemia Patients-A EuroFlow Study.Cancers · 2022Article
- CombiFlow: combinatorial AML-specific plasma membrane expression profiles allow longitudinal tracking of clones.Blood advances · 2022Article
- Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection.Cancers · 2022Review
- Ferritin-based targeted delivery of arsenic to diverse leukaemia types confers strong anti-leukaemia therapeutic effects.Nature nanotechnology · 2021Article
- Article
- Computational flow cytometry as a diagnostic tool in suspected-myelodysplastic syndromes.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2021Article
- LncRNAs serve as novel biomarkers for diagnosis and prognosis of childhood ALL.Biomarker research · 2021Article
- Subgrouping by gene expression profiles to improve relapse risk prediction in paediatric B-precursor acute lymphoblastic leukaemia.Cancer medicine · 2021Article
- The potential of proliferative and apoptotic parameters in clinical flow cytometry of myeloid malignancies.Blood advances · 2021Review
- Monitoring minimal/measurable residual disease in B-cell acute lymphoblastic leukemia by flow cytometry during targeted therapy.International journal of hematology · 2021Review
- Database-Guided Analysis for Immunophenotypic Diagnosis and Follow-Up of Acute Myeloid Leukemia With Recurrent Genetic Abnormalities.Frontiers in oncology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
33 authors at 12 institutions in 9 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Precise classification of acute leukemia (AL) is crucial for adequate treatment. EuroFlow has previously designed an AL orientation tube (ALOT) to guide towards the relevant classification panel (T-cell acute lymphoblastic leukemia (T-ALL), B-cell precursor (BCP)-ALL and/or acute myeloid leukemia (AML)) and final diagnosis. Now we built a reference database with 656 typical AL samples (145 T-ALL, 377 BCP-ALL, 134 AML), processed and analyzed via standardized protocols. Using principal component analysis (PCA)-based plots and automated classification algorithms for direct comparison of single-cells from individual patients against the database, another 783 cases were subsequently evaluated. Depending on the database-guided results, patients were categorized as: (i) typical T, B or Myeloid without or; (ii) with a transitional component to another lineage; (iii) atypical; or (iv) mixed-lineage. Using this automated algorithm, in 781/783 cases (99.7%) the right panel was selected, and data comparable to the final WHO-diagnosis was already provided in >93% of cases (85% T-ALL, 97% BCP-ALL, 95% AML and 87% mixed-phenotype AL patients), even without data on the full-characterization panels. Our results show that database-guided analysis facilitates standardized interpretation of ALOT results and allows accurate selection of the relevant classification panels, hence providing a solid basis for designing future WHO AL classifications.
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