{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T00:26:49Z","timestamp":1759364809233,"version":"build-2065373602"},"reference-count":24,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T00:00:00Z","timestamp":1691107200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cytometry Part B Clinical"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Mature B\u2010cell neoplasms are challenging to diagnose due to their heterogeneity and overlapping clinical and biological features. In this study, we present a new workflow strategy that leverages a large amount of flow cytometry data and an artificial intelligence approach to classify these neoplasms.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>By combining mathematical tools, such as classification algorithms and regression tree (CART) models, with biological expertise, we have developed a decision tree that accurately identifies mature B\u2010cell neoplasms. This includes chronic lymphocytic leukemia (CLL), for which cytometry has been extensively used, as well as other non\u2010CLL subtypes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The decision tree is easy to use and proposes a diagnosis and classification of mature B\u2010cell neoplasms to the users. It can identify the majority of CLL cases using just three markers: CD5, CD43, and CD200.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This approach has the potential to improve the accuracy and efficiency of mature B\u2010cell neoplasm diagnosis.<\/jats:p><\/jats:sec>","DOI":"10.1002\/cyto.b.22136","type":"journal-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T07:40:47Z","timestamp":1691134847000},"page":"342-350","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Use of a hybrid intelligence decision tree to identify mature B\u2010cell neoplasms"],"prefix":"10.1002","volume":"108","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7395-8891","authenticated-orcid":false,"given":"In\u00e8s","family":"Vergnolle","sequence":"first","affiliation":[{"name":"Laboratoire d'H\u00e9matologie, Centre Hospitalier Universitaire de Toulouse Institut Universitaire du Cancer de Toulouse Oncopole  Toulouse France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Theo","family":"Ceccomarini","sequence":"additional","affiliation":[{"name":"Laboratoire d'H\u00e9matologie, Centre Hospitalier Universitaire de Toulouse Institut Universitaire du Cancer de Toulouse Oncopole  Toulouse France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1609-3307","authenticated-orcid":false,"given":"Alban","family":"Canali","sequence":"additional","affiliation":[{"name":"Laboratoire d'H\u00e9matologie, Centre Hospitalier Universitaire de Toulouse Institut Universitaire du Cancer de Toulouse Oncopole  Toulouse France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean\u2010Baptiste","family":"Rieu","sequence":"additional","affiliation":[{"name":"Laboratoire d'H\u00e9matologie, Centre Hospitalier Universitaire de Toulouse Institut Universitaire du Cancer de Toulouse Oncopole  Toulouse 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