Use of a hybrid intelligence decision tree to identify mature B-cell neoplasms.
Cytometry B Clin Cytom
; 2023 Aug 04.
Article
en En
| MEDLINE
| ID: mdl-37539849
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Contexto en salud:
1_ASSA2030
Problema de salud:
1_financiamento_saude
Tipo de estudio:
Health_economic_evaluation
/
Prognostic_studies
Idioma:
En
Revista:
Cytometry B Clin Cytom
Año:
2023
Tipo del documento:
Article
País de afiliación:
Francia