Use of a hybrid intelligence decision tree to identify mature B‐cell neoplasms
Inès Vergnolle, Theo Ceccomarini, Alban Canali, Jean‐Baptiste Rieu, François Vergez- Cell Biology
- Histology
- Pathology and Forensic Medicine
Abstract
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.