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

Carolien Duetz, Sofie van Gassen, Theresia M. Westers, Margot F. van Spronsen, Costa Bachas, Yvan Saeys, Arjan A. van de Loosdrecht*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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 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).
Original languageEnglish
JournalCytometry Part A
Early online date2021
DOIs
Publication statusE-pub ahead of print - 2021

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