Artificial intelligence-based flow cytometry for the diagnosis of B-cell chronic lymphoproliferative disorders.

ABSTRACT:

Abstract

Accurate diagnosis of B-cell chronic lymphoproliferative disorders (B-CLPDs) remains challenging due to overlapping phenotypes across subtypes. Machine learning (ML) offers promising tools to improve marker evaluation and refine flow cytometry analysis. We investigated the use of ML algorithms to evaluate the diagnostic value of incorporating CD148, CD180, and CD200 into standard B-CLPD phenotyping panel and to develop a diagnosis decision tree. We trained models with flow cytometry data from 480 patients with B-CLPDs using XGBoost and DecisionTree algorithms. The final models integrated 2 categorical markers (CD5 and CD10) and quantiles of fluorescence intensity of 4 quantitative markers (CD20, CD180, and CD200) to classify 6 B-CLPD subtypes. These trained models were app

SUBMITTER: Mauvieux L 

PROVIDER: S-EPMC12663500 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature