TY - JOUR
T1 - Computational flow cytometry as a diagnostic tool in suspected-myelodysplastic syndromes
AU - Duetz, Carolien
AU - van Gassen, Sofie
AU - Westers, Theresia M.
AU - van Spronsen, Margot F.
AU - Bachas, Costa
AU - Saeys, Yvan
AU - van de Loosdrecht, Arjan A.
N1 - Funding Information: European Union's Horizon 2020 research and innovation programme, Grant/Award Number: 634789 Funding information Funding Information: We would like to thank all technicians for collecting and analyzing the FC data, especially, A. Zevenbergen and C. Cali. We thank G.J. Ossenkoppele and J. Cloos for critical reading of the manuscript. This study was supported in part by research funding from MDS‐RIGHT, which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 634789 ‐ “Providing the right care to the right patient with MyeloDysplastic Syndrome at the right time” to Arjan A van de Loosdrecht. Yvan Saeys and Sofie Van Gassen are ISAC Marylou Ingram Scholars. Funding Information: We would like to thank all technicians for collecting and analyzing the FC data, especially, A. Zevenbergen and C. Cali. We thank G.J. Ossenkoppele and J. Cloos for critical reading of the manuscript. This study was supported in part by research funding from MDS-RIGHT, which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 634789 - ?Providing the right care to the right patient with MyeloDysplastic Syndrome at the right time? to Arjan A van de Loosdrecht. Yvan Saeys and Sofie Van Gassen are ISAC Marylou Ingram Scholars. Publisher Copyright: © 2021 The Authors. Cytometry Part A published by Wiley Periodicals LLC on behalf of International Society for Advancement of Cytometry. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/8
Y1 - 2021/8
N2 - 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).
AB - 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).
KW - diagnostic test
KW - flow cytometry
KW - hematological malignancies
KW - machine learning
KW - myelodysplastic syndromes
UR - http://www.scopus.com/inward/record.url?scp=85105647499&partnerID=8YFLogxK
U2 - https://doi.org/10.1002/cyto.a.24360
DO - https://doi.org/10.1002/cyto.a.24360
M3 - Article
C2 - 33942494
SN - 1552-4922
VL - 99
SP - 814
EP - 824
JO - Cytometry Part A
JF - Cytometry Part A
IS - 8
ER -