Article (Scientific journals)
Clustering and Kernel Density Estimation for Assessment of Measurable Residual Disease by Flow Cytometry
Jacqmin, Hugues; Chatelain, Bernard; Louveaux, Quentin et al.
2020In Diagnostics, 10 (5)
Peer reviewed
 

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Keywords :
Acute myeloid leukemia; multiparametric data analysis; clustering
Abstract :
[en] Standardization, data mining techniques, and comparison to normality are changing the landscape of multiparameter flow cytometry in clinical hematology. On the basis of these principles, a strategy was developed for measurable residual disease (MRD) assessment. Herein, suspicious cell clusters are first identified at diagnosis using a clustering algorithm. Subsequently, automated multidimensional spaces, named “Clouds”, are created around these clusters on the basis of density calculations. This step identifies the immunophenotypic pattern of the suspicious cell clusters. Thereafter, using reference samples, the “Abnormality Ratio” (AR) of each Cloud is calculated, and major malignant Clouds are retained, known as “Leukemic Clouds” (L-Clouds). In follow-up samples, MRD is identified when more cells fall into a patient’s L-Cloud compared to reference samples (AR concept). This workflow was applied on simulated data and real-life leukemia flow cytometry data. On simulated data, strong patient-dependent positive correlation (R2 = 1) was observed between the AR and spiked-in leukemia cells. On real patient data, AR kinetics was in line with the clinical evolution for five out of six patients. In conclusion, we present a convenient flow cytometry data analysis approach for the follow-up of hematological malignancies. Further evaluation and validation on more patient samples and different flow cytometry panels is required before implementation in clinical practice.
Disciplines :
Oncology
Computer science
Author, co-author :
Jacqmin, Hugues;  Université Catholique de Louvain - UCL > Hematology Laboratory
Chatelain, Bernard;  Université Catholique de Louvain - UCL > Hematology Laboratory
Louveaux, Quentin ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation : Optimisation discrète
Jacqmin, Philippe;  MnS - Modeling and Simulation
Dogné, Jean-Michel;  Université de Namur - UNamur > Pharmacy Department
Graux, Carlos;  Université Catholique de Louvain - UCL > Department of Hematology
Mullier, François;  Université Catholique de Louvain - UCL > Hematology Laboratory
Language :
English
Title :
Clustering and Kernel Density Estimation for Assessment of Measurable Residual Disease by Flow Cytometry
Publication date :
2020
Journal title :
Diagnostics
Volume :
10
Issue :
5
Peer reviewed :
Peer reviewed
Available on ORBi :
since 18 May 2020

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