ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2021
Computational flow cytometry as a diagnostic tool in suspected-myelodysplastic syndromes.
Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 37 citations in OpenAlex.
- Toward integrated multimodal artificial intelligence in bone marrow examination: current state and open challenges.Blood research · 2026Review
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- Machine learning-assisted decoding of temporal transcriptional dynamics via fluorescent timer.Nature communications · 2025Article
- Using Artificial Intelligence to Enhance Myelodysplastic Syndrome Diagnosis, Prognosis, and Treatment.Biomedicines · 2025Review
- Validation of the diagnostic potential of 17 immunophenotypic core markers defined by theHemaSphere · 2025Article
- Article
- Diagnosis of myelodysplastic syndromes: the classic and the novel.Haematologica · 2025Review
- Artificial intelligence-based Myelodysplastic Syndromes Score, 2022 classifications, and the Molecular International Prognostic Scoring System: a perfect match.Haematologica · 2025Article
- TockyLocus: quantitative analysis of flow cytometric fluorescent timer data in Nr4a3-Tocky and Foxp3-Tocky mice.Biology methods & protocols · 2025Article
- Comparison of three machine learning algorithms for classification of B-cell neoplasms using clinical flow cytometry data.Cytometry. Part B, Clinical cytometry · 2024Article
- Loss of hematopoietic progenitors heterogeneity is an adverse prognostic factor in lower-risk myelodysplastic neoplasms.Leukemia · 2024Article
- Immunophenotyping myelodysplastic neoplasms: the role of flow cytometry in the molecular classification era.Frontiers in oncology · 2024Review
- Optimization of diagnosis and treatment of hematological diseases via artificial intelligence.Frontiers in medicine · 2024Review
- Integrating AI and ML in Myelodysplastic Syndrome Diagnosis: State-of-the-Art and Future Prospects.Cancers · 2023Review
- Challenging the status flow: how artificial intelligence is advancing diagnosis of myelodysplastic neoplasms.Haematologica · 2023Article
- Artificial intelligence to empower diagnosis of myelodysplastic syndromes by multiparametric flow cytometry.Haematologica · 2023Article
- Multiparameter flow cytometry in the evaluation of myelodysplasia: Analytical issues: Recommendations from the European LeukemiaNet/International Myelodysplastic Syndrome Flow Cytometry Working Group.Cytometry. Part B, Clinical cytometry · 2023Review
- Abnormal CD13/HLA-DR Expression Pattern on Myeloblasts Predicts Development of Myeloid Neoplasia in Patients With Clonal Cytopenia of Undetermined Significance.American journal of clinical pathology · 2022Article
- Challenges in translational machine learning.Human genetics · 2022Review
- Quality Assessment of a Large Multi-Center Flow Cytometric Dataset of Acute Myeloid Leukemia Patients-A EuroFlow Study.Cancers · 2022Article
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7 authors at 2 institutions in 2 countries.
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Abstract
The diagnostic work-up of patients suspected for myelodysplastic syndromes is challenging and mainly relies on bone marrow morphology and cytogenetics. In this study, we developed and prospectively validated a fully computational tool for flow cytometry diagnostics in suspected-MDS. The computational diagnostic workflow consists of methods for pre-processing flow cytometry data, followed by a cell population detection method (FlowSOM) and a machine learning classifier (Random Forest). Based on a six tubes FC panel, the workflow obtained a 90% sensitivity and 93% specificity in an independent validation cohort. For practical advantages (e.g., reduced processing time and costs), a second computational diagnostic workflow was trained, solely based on the best performing single tube of the training cohort. This workflow obtained 97% sensitivity and 95% specificity in the prospective validation cohort. Both workflows outperformed the conventional, expert analyzed flow cytometry scores for diagnosis with respect to accuracy, objectivity and time investment (less than 2 min per patient).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.