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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

ISBN: 978-989-758-276-9

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 evaluat ed 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. (More)

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Paper citation in several formats:
Licandro, R.; Reiter, M.; Diem, M.; Dworzak, M.; Schumich, A. and Kampel, M. (2018). Application of Machine Learning for Automatic MRD Assessment in Paediatric Acute Myeloid Leukaemia.In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-276-9, pages 401-408. DOI: 10.5220/0006595804010408

@conference{icpram18,
author={Roxane Licandro. and Michael Reiter. and Markus Diem. and Michael Dworzak. and Angela Schumich. and Martin Kampel.},
title={Application of Machine Learning for Automatic MRD Assessment in Paediatric Acute Myeloid Leukaemia},
booktitle={Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2018},
pages={401-408},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006595804010408},
isbn={978-989-758-276-9},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Application of Machine Learning for Automatic MRD Assessment in Paediatric Acute Myeloid Leukaemia
SN - 978-989-758-276-9
AU - Licandro, R.
AU - Reiter, M.
AU - Diem, M.
AU - Dworzak, M.
AU - Schumich, A.
AU - Kampel, M.
PY - 2018
SP - 401
EP - 408
DO - 10.5220/0006595804010408

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