Computational flow cytometry as a diagnostic tool in suspected-myelodysplastic syndromes.
Cytometry A
; 99(8): 814-824, 2021 08.
Article
em En
| MEDLINE
| ID: mdl-33942494
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).
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Síndromes Mielodisplásicas
Tipo de estudo:
Diagnostic_studies
/
Etiology_studies
/
Incidence_studies
/
Observational_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article