The diagnosis of myelodysplastic syndromes (MDS) might be challenging and relies on the convergence of cytological, cytogenetic, and molecular arguments. Multiparametric flow cytometry (MFC) helps diagnose MDS, especially when other features are non-contributory, but 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 by elasticnet algorithm trained on a cohort of 191 patients and only based on flow cytometry parameters selected by Boruta algorithm, to build a simple but reliable prediction score with 5 parameters. Our MDS prediction score assisted by AI greatly improves the sensitivity of Ogata score while keeping an excellent specificity validated on an external cohort of 89 patients with an AUC = 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 myelodysplastic syndromes.
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