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Clichet V, Harrivel V, Delette C, Guiheneuf E, Gautier M, Morel P, Assouan D, Merlusca L, Beaumont M, Lebon D, Caulier A, Marolleau JP, Matthes T, Vergez F, Garçon L, Boyer T. Accurate classification of plasma cell dyscrasias is achieved by combining artificial intelligence and flow cytometry. Br J Haematol 2021; 196:1175-1183. [PMID: 34730236 DOI: 10.1111/bjh.17933] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 10/13/2021] [Accepted: 10/18/2021] [Indexed: 12/19/2022]
Abstract
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/).
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Affiliation(s)
- Valentin Clichet
- Service d'Hématologie Biologique, CHU Amiens-Picardie, Amiens, France
| | | | - Caroline Delette
- Service d'Hématologie Clinique et de Thérapie Cellulaire, CHU Amiens-Picardie, Amiens, France
| | - Eric Guiheneuf
- Service d'Hématologie Biologique, CHU Amiens-Picardie, Amiens, France
| | - Murielle Gautier
- Service d'Hématologie Biologique, CHU Amiens-Picardie, Amiens, France
| | - Pierre Morel
- Service d'Hématologie Clinique et de Thérapie Cellulaire, CHU Amiens-Picardie, Amiens, France
| | - Déborah Assouan
- Service d'Hématologie Clinique et de Thérapie Cellulaire, CHU Amiens-Picardie, Amiens, France
| | - Lavinia Merlusca
- Service d'Hématologie Clinique et de Thérapie Cellulaire, CHU Amiens-Picardie, Amiens, France
| | - Marie Beaumont
- Service d'Hématologie Clinique et de Thérapie Cellulaire, CHU Amiens-Picardie, Amiens, France
| | - Delphine Lebon
- Service d'Hématologie Clinique et de Thérapie Cellulaire, CHU Amiens-Picardie, Amiens, France.,Université Picardie Jules Verne, HEMATIM, UR 4666, F80025, Amiens, France
| | - Alexis Caulier
- Service d'Hématologie Clinique et de Thérapie Cellulaire, CHU Amiens-Picardie, Amiens, France.,Université Picardie Jules Verne, HEMATIM, UR 4666, F80025, Amiens, France
| | - Jean-Pierre Marolleau
- Service d'Hématologie Clinique et de Thérapie Cellulaire, CHU Amiens-Picardie, Amiens, France.,Université Picardie Jules Verne, HEMATIM, UR 4666, F80025, Amiens, France
| | - Thomas Matthes
- Service d'Hématologie, Hôpital Universitaire de Genève, Genève, Suisse
| | - François Vergez
- Laboratoire d'Hématologie, Institut Universitaire du Cancer de Toulouse, Toulouse, France
| | - Loïc Garçon
- Service d'Hématologie Biologique, CHU Amiens-Picardie, Amiens, France.,Université Picardie Jules Verne, HEMATIM, UR 4666, F80025, Amiens, France
| | - Thomas Boyer
- Service d'Hématologie Biologique, CHU Amiens-Picardie, Amiens, France.,Université Picardie Jules Verne, HEMATIM, UR 4666, F80025, Amiens, France
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