Artificial intelligence to empower diagnosis of myelodysplastic syndromes by multiparametric flow cytometry
Valentin Clichet,
Delphine Lebon,
Nicolas Chapuis,
Jaja Zhu,
Valérie Bardet,
Jean-Pierre Marolleau,
Loïc Garçon,
Alexis Caulier,
Thomas Boyer
Affiliations
Valentin Clichet
Service d’Hématologie Biologique, CHU Amiens-Picardie, Amiens
Delphine Lebon
Service d’Hématologie Clinique et de Thérapie Cellulaire, CHU Amiens-Picardie, Amiens, France; HEMATIM, EA 4666, Université Picardie Jules Verne, Amiens
Nicolas Chapuis
Assistance Publique-Hôpitaux de Paris. Centre-Université Paris Cité, Service d’hématologie biologique, Hôpital Cochin, Paris
Jaja Zhu
Service d’Hématologie-Immunologie-Transfusion, CHU Ambroise Paré, INSERM UMR 1184, AP-HP, Université Paris Saclay, 92100 Boulogne Billancourt
Valérie Bardet
Service d’Hématologie-Immunologie-Transfusion, CHU Ambroise Paré, INSERM UMR 1184, AP-HP, Université Paris Saclay, 92100 Boulogne Billancourt
Jean-Pierre Marolleau
Service d’Hématologie Clinique et de Thérapie Cellulaire, CHU Amiens-Picardie, Amiens, France; HEMATIM, EA 4666, Université Picardie Jules Verne, Amiens
Loïc Garçon
Service d’Hématologie Biologique, CHU Amiens-Picardie, Amiens, France; HEMATIM, EA 4666, Université Picardie Jules Verne, Amiens
Alexis Caulier
Service d’Hématologie Clinique et de Thérapie Cellulaire, CHU Amiens-Picardie, Amiens, France; HEMATIM, EA 4666, Université Picardie Jules Verne, Amiens, France; Broad Institute of MIT and Harvard, Cambridge, MA; Division of Hematology/Oncology, Boston Children’s Hospital, Harvard Medical School, Cambridge, MA
Thomas Boyer
Service d’Hématologie Biologique, CHU Amiens-Picardie, Amiens, France; HEMATIM, EA 4666, Université Picardie Jules Verne, Amiens
The diagnosis of myelodysplastic syndromes (MDS) might be challenging and relies on the convergence of cytological, cytogenetic, and molecular factors. Multiparametric flow cytometry (MFC) helps diagnose MDS, especially when other features do not contribute to the decision-making process, but its usefulness remains underestimated, mostly due to a lack of standardization of cytometers. We present here an innovative model integrating artificial intelligence (AI) with MFC to improve the diagnosis and the classification of MDS. We develop a machine learning model through an elasticnet algorithm directed on a cohort of 191 patients, only based on flow cytometry parameters selected by the Boruta algorithm, to build a simple but reliable prediction score with five parameters. Our AI-assisted MDS prediction score greatly improves the sensitivity of the Ogata score while keeping an excellent specificity validated on an external cohort of 89 patients with an Area Under the Curve of 0.935. This model allows the diagnosis of both high- and low-risk MDS with 91.8% sensitivity and 92.5% specificity. Interestingly, it highlights a progressive evolution of the score from clonal hematopoiesis of indeterminate potential (CHIP) to highrisk MDS, suggesting a linear evolution between these different stages. By significantly decreasing the overall misclassification of 52% for patients with MDS and of 31.3% for those without MDS (P=0.02), our AI-assisted prediction score outperforms the Ogata score and positions itself as a reliable tool to help diagnose MDS.
