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.