1L. Firrera, B. Podvin, J. Herlem, et al., “A New Diagnostic Approach for Myelodysplastic Neoplasms Using a Combination of Scores Based on Flow Cytometry and Automated Hematology Sysmex XN Analyzers,” International Journal of Laboratory Hematology 47 (2024): 236–245.
10.1111/ijlh.14404
PubMed Web of Science® Google Scholar
2R. Boutault, P. Peterlin, M. Boubaya, et al., “A Novel Complete Blood Count-Based Score to Screen for Myelodysplastic Syndrome in Cytopenic Patients,” British Journal of Haematology 183 (2018): 736–746.
10.1111/bjh.15626
CAS PubMed Web of Science® Google Scholar
3V. Bardet, O. Wagner-Ballon, J. Guy, et al., “Multicentric Study Underlining the Interest of Adding CD5, CD7 and CD56 Expression Assessment to the Flow Cytometric Ogata Score in Myelodysplastic Syndromes and Myelodysplastic/Myeloproliferative Neoplasms,” Haematologica 100 (2015): 472–478.
10.3324/haematol.2014.112755
PubMed Web of Science® Google Scholar
4B. Kárai, J. Bedekovics, Z. Miltényi, et al., “A Single-Tube Flow Cytometric Procedure for Enhancing the Diagnosis and Prognostic Classification of Patients With Myelodysplastic Syndromes,” International Journal of Laboratory Hematology 39 (2017): 577–584.
10.1111/ijlh.12700
CAS PubMed Web of Science® Google Scholar
5S. G. C. Mestrum, E. M. P. Cremers, N. C. J. de Wit, et al., “Integration of the Ki-67 Proliferation Index Into the Ogata Score Improves Its Diagnostic Sensitivity for Low-Grade Myelodysplastic Syndromes,” Leukemia Research 113 (2022): 106789.
10.1016/j.leukres.2022.106789
CAS PubMed Web of Science® Google Scholar
6M. Heron, E. Dovern, L. E. Bakker-Jonges, et al., “Translating the MDS Flow Cytometric Score Into Clinical Practice,” Cytometry Part B: Clinical Cytometry 88 (2015): 207–209.
10.1002/cyto.b.21208
PubMed Web of Science® Google Scholar
7S. M. H. Matzen, K. K. Raaschou-Jensen, and K. Kallenbach, “Implementation of the Ogata Flow Cytometric Scoring System in Routine Diagnostics of Myelodysplastic Syndrome,” Health Science Reports 1 (2018): e90.
10.1002/hsr2.90
PubMed Google Scholar
8J. G. Lijmer, “Empirical Evidence of Design-Related Bias in Studies of Diagnostic Tests,” JAMA 282 (1999): 1061.
10.1001/jama.282.11.1061
CAS PubMed Web of Science® Google Scholar
9A. Muyldermans, L. Florin, H. Devos, B. Cauwelier, and J. Emmerechts, “Diagnostic Utility of the Lymphoid Screening Tube Supplemented With CD34 for Ogata Score Calculation in Patients With Peripheral Cytopenia,” Hematology 24 (2019): 166–172.
10.1080/10245332.2018.1535536
PubMed Web of Science® Google Scholar
10T. Raskovalova, M.-C. Jacob, C.-E. Bulabois, et al., “Flow Cytometric Analysis of Peripheral Blood Neutrophil Myeloperoxidase Expression for Ruling out Myelodysplastic Syndromes: A Prospective Validation Study,” Annals of Hematology 100 (2021): 1149–1158.
10.1007/s00277-021-04446-7
CAS PubMed Web of Science® Google Scholar
11M. Sorigue, “Diagnosis of Erythroid Dysplasia by Flow Cytometry: A Review,” Expert Review of Hematology 16 (2023): 1049–1062.
10.1080/17474086.2023.2289534
CAS PubMed Web of Science® Google Scholar
12C. Duetz, S. Van Gassen, T. M. Westers, et al., “Computational Flow Cytometry as a Diagnostic Tool in Suspected-Myelodysplastic Syndromes,” Cytometry Part A 99 (2021): 814–824.
10.1002/cyto.a.24360
CAS PubMed Web of Science® Google Scholar

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Volume47, Issue4

August 2025

Pages 760-762

Streamlining the Diagnosis of MDS: Navigating Growing Pains Towards Greater Accuracy and Efficiency

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Streamlining the Diagnosis of MDS: Navigating Growing Pains Towards Greater Accuracy and Efficiency

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