Titre :

Accurate classification of plasma cell dyscrasias is achieved by combining artificial intelligence and flow cytometry.

Auteur(s) :

Clichet, V. [Auteur]
Harrivel, V. [Auteur]
Delette, C. [Auteur]
Guiheneuf, E. [Auteur]
Gautier, M. [Auteur]
Morel, Pierre [Auteur]
METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694
Assouan, D. [Auteur]
Merlusca, L. [Auteur]
Beaumont, M. [Auteur]
Lebon, D. [Auteur]
Caulier, A. [Auteur]
Marolleau, J. P. [Auteur]
Matthes, T. [Auteur]
Vergez, F. [Auteur]
Garçon, L. [Auteur]
Boyer, T. [Auteur]

Titre de la revue :

British Journal of Haematology

Nom court de la revue :

Br J Haematol

Numéro :

196

Pagination :

p. 1175-1183

Date de publication :

2022-03

ISSN :

1365-2141

Mot(s)-clé(s) en anglais :

multiple myeloma
monoclonal gammopathy of undetermined significance
multiparametric flow cytometry
artificial intelligence

Discipline(s) HAL :

Sciences du Vivant [q-bio]

Résumé en anglais : [en]

Monoclonal gammopathy of unknown significance (MGUS), smouldering multiple myeloma (SMM), and multiple myeloma (MM) are very common neoplasms. However, it is often difficult to distinguish between these entities. In the ...
Lire la suite >Monoclonal gammopathy of unknown significance (MGUS), smouldering multiple myeloma (SMM), and multiple myeloma (MM) are very common neoplasms. However, it is often difficult to distinguish between these entities. In the present study, we aimed to classify the most powerful markers that could improve diagnosis by multiparametric flow cytometry (MFC). The present study included 348 patients based on two independent cohorts. We first assessed how representative the data were in the discovery cohort (123 MM, 97 MGUS) and then analysed their respective plasma cell (PC) phenotype in order to obtain a set of correlations with a hypersphere visualisation. Cluster of differentiation (CD)27 and CD38 were differentially expressed in MGUS and MM (P < 0·001). We found by a gradient boosting machine method that the percentage of abnormal PCs and the ratio PC/CD117 positive precursors were the most influential parameters at diagnosis to distinguish MGUS and MM. Finally, we designed a decisional algorithm allowing a predictive classification ≥95% when PC dyscrasias were suspected, without any misclassification between MGUS and SMM. We validated this algorithm in an independent cohort of PC dyscrasias (n = 87 MM, n = 41 MGUS). This artificial intelligence model is freely available online as a diagnostic tool application website for all MFC centers worldwide (https://aihematology.shinyapps.io/PCdyscrasiasToolDg/).Lire moins >

Langue :

Anglais

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

Université de Lille
CHU Lille

Collections :

• METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Date de dépôt :

2023-11-15T05:38:42Z
2024-04-22T09:25:30Z