Scientific article

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

Published inBritish journal of haematology, vol. 196, no. 5, p. 1175-1183
Publication date2022-03
First online date2021-11-03

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/).

  • Artificial intelligence
  • Monoclonal gammopathy of undetermined significance
  • Multiparametric flow cytometry
  • Multiple myeloma
  • Aged
  • Artificial Intelligence
  • Diagnosis, Computer-Assisted
  • Female
  • Flow Cytometry
  • Humans
  • Male
  • Monoclonal Gammopathy of Undetermined Significance / classification
  • Monoclonal Gammopathy of Undetermined Significance / diagnosis
  • Multiple Myeloma / classification
  • Multiple Myeloma / diagnosis
  • Paraproteinemias / classification
  • Paraproteinemias / diagnosis
  • Retrospective Studies
Citation (ISO format)
CLICHET, Valentin et al. Accurate classification of plasma cell dyscrasias is achieved by combining artificial intelligence and flow cytometry. In: British journal of haematology, 2022, vol. 196, n° 5, p. 1175–1183. doi: 10.1111/bjh.17933
Main files (1)
Article (Published version)
ISSN of the journal0007-1048

Technical informations

Creation03/22/2022 12:42:00 PM
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