{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T01:58:36Z","timestamp":1778637516267,"version":"3.51.4"},"reference-count":21,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,6,26]],"date-time":"2022-06-26T00:00:00Z","timestamp":1656201600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Myelodysplastic syndromes (MDSs) are clonal hematopoietic diseases of the elderly, characterized by chronic cytopenia, ineffective and dysplastic hematopoiesis, recurrent genetic abnormalities and increased risk of progression to acute myeloid leukemia. Diagnosis on a complete blood count (CBC) can be challenging due to numerous other non-neoplastic causes of cytopenias. New generations of hematology analyzers provide cell population data (CPD) that can be exploited to reliably detect MDSs from a routine CBC. In this review, we first describe the different technologies used to obtain CPD. We then give an overview of the currently available data regarding the performance of CPD for each lineage in the diagnostic workup of MDSs. Adequate exploitation of CPD can yield very strong diagnostic performances allowing for faster diagnosis and reduction of time-consuming slide reviews in the hematology laboratory.<\/jats:p>","DOI":"10.3390\/diagnostics12071556","type":"journal-article","created":{"date-parts":[[2022,6,26]],"date-time":"2022-06-26T09:00:13Z","timestamp":1656234013000},"page":"1556","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Automated Detection of Dysplasia: Data Mining from Our Hematology Analyzers"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2846-6681","authenticated-orcid":false,"given":"Jaja","family":"Zhu","sequence":"first","affiliation":[{"name":"Service d\u2019H\u00e9matologie-Immunologie-Transfusion, CHU Ambroise Par\u00e9, APHP.Paris Saclay, Universit\u00e9 Versailles Saint Quentin-Universit\u00e9 Paris Saclay, 92100 Boulogne-Billancourt, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sylvain","family":"Clauser","sequence":"additional","affiliation":[{"name":"Service d\u2019H\u00e9matologie-Immunologie-Transfusion, CHU Ambroise Par\u00e9, APHP.Paris Saclay, Universit\u00e9 Versailles Saint Quentin-Universit\u00e9 Paris Saclay, 92100 Boulogne-Billancourt, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicolas","family":"Freynet","sequence":"additional","affiliation":[{"name":"D\u00e9partement d\u2019H\u00e9matologie et Immunologie Biologiques, GHU Henri Mondor, Universit\u00e9 Paris-Cr\u00e9teil, 94000 Cr\u00e9teil, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0271-3357","authenticated-orcid":false,"given":"Val\u00e9rie","family":"Bardet","sequence":"additional","affiliation":[{"name":"Service d\u2019H\u00e9matologie-Immunologie-Transfusion, CHU Ambroise Par\u00e9, APHP.Paris Saclay, Universit\u00e9 Versailles Saint Quentin-Universit\u00e9 Paris Saclay, 92100 Boulogne-Billancourt, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,26]]},"reference":[{"key":"ref_1","unstructured":"Swerdlow, S.H., Campo, E., Harris, N.L., Jaffe, E.S., Pileri, S.A., Stein, H., Thiele, J., Arber, D.A., Hasserjian, R.P., and le Beau, M.M. 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