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

• Published in: Cytometry. Part A Vol. 99; no. 8; pp. 814 - 824
• Main Authors: Duetz, Carolien, Van Gassen, Sofie, Westers, Theresia M., Spronsen, Margot F., Bachas, Costa, Saeys, Yvan, Loosdrecht, Arjan A.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.08.2021; Wiley Subscription Services, Inc
• Subjects: Bone marrow; Computer applications; Cytogenetics; diagnostic test; Disorders; Flow cytometry; hematological malignancies; Learning algorithms; Machine learning; Morphology; Myelodysplastic syndrome; myelodysplastic syndromes; Original; Sensitivity; Software; Tubes; Workflow; hematological malignancies; flow cytometry; machine learning; diagnostic test; myelodysplastic syndromes
• DOI: 10.1002/cyto.a.24360
• ISSN: 1552-4922; 1552-4930

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).