Inès Vergnolle, Theo Ceccomarini, Alban Canali, Jean-Baptiste Rieu, François Vergez
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Use of a hybrid intelligence decision tree to identify mature B-cell neoplasms.
Background: Mature B-cell 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.
Methods: 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-cell neoplasms. This includes chronic lymphocytic leukemia (CLL), for which cytometry has been extensively used, as well as other non-CLL subtypes.
Results: The decision tree is easy to use and proposes a diagnosis and classification of mature B-cell neoplasms to the users. It can identify the majority of CLL cases using just three markers: CD5, CD43, and CD200.
Conclusion: This approach has the potential to improve the accuracy and efficiency of mature B-cell neoplasm diagnosis.
期刊介绍：
Cytometry Part B: Clinical Cytometry features original research reports, in-depth reviews and special issues that directly relate to and palpably impact clinical flow, mass and image-based cytometry. These may include clinical and translational investigations important in the diagnostic, prognostic and therapeutic management of patients. Thus, we welcome research papers from various disciplines related [but not limited to] hematopathologists, hematologists, immunologists and cell biologists with clinically relevant and innovative studies investigating individual-cell analytics and/or separations. In addition to the types of papers indicated above, we also welcome Letters to the Editor, describing case reports or important medical or technical topics relevant to our readership without the length and depth of a full original report.
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