Authors:
Roxane Licandro
1
;
Michael Reiter
2
;
Markus Diem
2
;
Michael Dworzak
3
;
Angela Schumich
4
and
Martin Kampel
2
Affiliations:
1
TU Wien and Medical University of Vienna, Austria
;
2
TU Wien, Austria
;
3
Medical University of Vienna and Labdia Labordiagnostik GmbH, Austria
;
4
Labdia Labordiagnostik GmbH, Austria
Keyword(s):
Clustering, Machine Learning, Flow Cytometry, Acute Myeloid Childhood Leukaemia, Minimal Residual Disease.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Bioinformatics and Systems Biology
;
Clustering
;
Medical Imaging
;
Pattern Recognition
;
Software Engineering
;
Theory and Methods
Abstract:
Acute Myeloid Leukaemia (AML) is a rare type of blood cancer in children. This disease originates from
genetic alterations of hematopoetic progenitor cells, which are involved in the hematopoiesis process, and
leads to the proliferation of undifferentiated (leukaemic) cells. Flow CytoMetry (FCM) measurements enable
the assessment of the Minimal Residual Disease (MRD), a value which clinicians use as powerful predictor
for treatment response and diagnostic tool for planning patients’ individual therapy. In this work we propose
machine learning applications for the automatic MRD assessment in AML. Recent approaches focus on childhood
Acute Lymphoblastic Leukaemia (ALL), more common in this population. We perform experiments
regarding the performance of state-of-the-art algorithms and provide a novel GMM formulation to estimate
leukaemic cell populations by learning background (non-cancer) populations only. Additionally, combination
of backgrounds of different leukaemia types are evalua
ted regarding their ability to predict MRD in AML. The
results suggest that background populations and combinations of these are suitable to assess MRD in AML.
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