Importancia del diagnóstico inmunofenotípico por citometría de flujo de los síndromes mielodisplásicos Translated title: Importance of the immunophenotypic diagnosis by flow cytometry of myelodysplastic syndromes

Myelodysplastic syndromes (MDSs) are a heterogeneous group of hematopoietic stem cell diseases categorized by dysplasia in one or more hematopoietic cell lineages, as well as cytopenia and functional abnormalities in bone marrow cells. Several MDS classification methods have been proposed to categorize the disease and help professionals better plan in patients’ treatment. The World Health Organization classification, released in 2008 and revised in 2016, is the currently and the most used classification method worldwide. Recent advances in MDS molecular biology and innovations in flow cytometry have enabled the development of new parameters for MDS diagnosis and classification. Several groups have published flow cytometry scores and guidelines useful for the diagnosis and/or prognosis of MDS, which are mostly based on detecting immunophenotypic abnormalities in granulocyte, monocyte, and lymphoid lineages. Here, we review the current literature and discuss the main parameters that should be analyzed by flow cytometry with the aim of refining MDS diagnosis and prognosis. Furthermore, we discuss the critical role of flow cytometry and molecular biology in MDS diagnosis and prognosis, as well as the current challenges and future perspectives involving these techniques.

Myelodysplastic syndromes (MDS) are a challenging group of diseases for clinicians and researchers, as both disease course and pathobiology are highly heterogeneous. In (suspected) MDS patients, multi-parameter flow cytometry can aid in establishing diagnosis, risk stratification and choice of therapy. This review addresses the developments and future directions of multi-parameter flow cytometry scores in MDS. Additionally, we propose an integrated diagnostic algorithm for suspected MDS.

The diagnostic work‐up of patients suspected for myelodysplastic syndromes is challenging and mainly relies on bone marrow morphology and cytogenetics. In this study, we developed and prospectively validated a fully computational tool for flow cytometry diagnostics in suspected‐MDS. The computational diagnostic workflow consists of methods for pre‐processing flow cytometry data, followed by a cell population detection method (FlowSOM) and a machine learning classifier (Random Forest). Based on a six tubes FC panel, the workflow obtained a 90% sensitivity and 93% specificity in an independent validation cohort. For practical advantages (e.g., reduced processing time and costs), a second computational diagnostic workflow was trained, solely based on the best performing single tube of the training cohort. This workflow obtained 97% sensitivity and 95% specificity in the prospective validation cohort. Both workflows outperformed the conventional, expert analyzed flow cytometry scores for diagnosis with respect to accuracy, objectivity and time investment (less than 2 min per patient).