Computational flow cytometry as a diagnostic tool in suspected-myelodysplastic syndromes.

ABSTRACT: 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 tra

SUBMITTER: Duetz C 

PROVIDER: S-EPMC8453916 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature