Commenced in January 2007
Paper Count: 30458
Development of a Telemedical Network Supporting an Automated Flow Cytometric Analysis for the Clinical Follow-up of Leukaemia
Abstract:In patients with acute lymphoblastic leukaemia (ALL), treatment response is increasingly evaluated with minimal residual disease (MRD) analyses. Flow Cytometry (FCM) is a fast and sensitive method to detect MRD. However, the interpretation of these multi-parametric data requires intensive operator training and experience. This paper presents a pipeline-software, as a ready-to-use FCM-based MRD-assessment tool for the daily clinical practice for patients with ALL. The new tool increases accuracy in assessment of FCM-MRD in samples which are difficult to analyse by conventional operator-based gating since computer-aided analysis potentially has a superior resolution due to utilization of the whole multi-parametric FCM-data space at once instead of step-wise, two-dimensional plot-based visualization. The system developed as a telemedical network reduces the work-load and lab-costs, staff-time needed for training, continuous quality control, operator-based data interpretation. It allows dissemination of automated FCM-MRD analysis to medical centres which have no established expertise for the benefit of an even larger community of diseased children worldwide. We established a telemedical network system for analysis and clinical follow-up and treatment monitoring of Leukaemia. The system is scalable and adapted to link several centres and laboratories worldwide.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1127082Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1101
 S. Holzmüller-Laue, et. al., "A Highly Scalable Information System as Extendable Framework Solution for Medical R&D Projects. XXIInd International Congress of the European Federation for Medical Informatics, ISBN 978-1-60750-044-5.
 R-D. Berndt, C. Takenga et. al. “SaaS-Platform for Mobile Health Applications”, in Proceedings of the IEEE-International Multi-Conference on Systems, Signals and Devices, Chemnitz, Germany March 2012, DOI: 10.1109/SSD.2012.6198120, pp.1-4.
 J. Toedling, P. Rhein, R. Ratei, L. Karawajew, R. Spang (2006) Automated in-silico detection of cell populations in FCM cytometry read-outs and its application to leukaemia disease monitoring. BMC Bioinformatics, 7:282.
 K. Fiser, T. Sieger, A. Schumich, B. Wood, J. Irving, E. Mejstrikova, M. Dworzak. Detection and Monitoring of Normal and Leukemic Cell Populations with Hierarchical Clustering of FCM Cytometry Data (2011). Cytometry A. 2011.
 K. Lo, RR. Brinkman and R. Gottardo, Automated Gating of FCM Cytometry Data via Robust Model-based Clustering (2008). Cytometry A. 2008 Apr;73(4):321-32.
 S. Pyne, X. Hu, K. Wang, E. Rossin, TI. Lin, KM. Maier, C. Baecher-Allan, GJ. McLachlan, P. Tamayo, DA. Hafler, PL. De Jager, JP. Mesirov (2009). Automated high-dimensional flow cytometric data analysis. Proceedings of the National Academy of Sciences of the United States of America, Volume: 106, Issue: 21, Publisher: National Academy of Sciences; 8519-8524.
 CE. Pedreira, ES. Costa, J. Almeida, C. Fernandez, S. Quijano, J. Flores, S. Barrena, Q. Lecrevisse, JJ. Van Dongen, A. Orfao; EuroFCM Consortium (2008). A probabilistic approach for the evaluation of minimal residual disease by multiparameter FCM cytometry in leukemic B-cell chronic lymphoproliferative disorders. Cytometry Part A Volume 73A, Issue 12; 1141–1150.
 C. Chan, F. Feng, J. Ottinger, D. Foster, M. West, TB. Kepler. Statistical mixture modeling for cell subtype identification in FCM cytometry. Cytometry A. 2008 Aug;73(8): 693-701.
 M. Wilkins, L. Boddy, C. Morris, R. Jonker. A comparison of some neural and non-neural methods for identification of phytoplankton from FCM cytometry data. Comput Appl Biosci (1996) 12(1): doi:10.1093/bioinformatics/12.1.9, pp 9-18.
 K. Lo, F. Hahne, R. Brinkman, R. Gottardo. FCMClust: A Bioconductor package for automated gating of FCM cytometry data (2009). BMC Bioinformatics 2009, 10:145 doi:10.1186/1471-2105-10-145.
 J. Frelinger, TB. Kepler, C. Chan, FCM: Statistics, visualization and informatics for FCM cytometry (2008). Source Code for Biology and Medicine 2008, 3:10 doi:10.1186/1751-0473-3-10.
 A. Bashashati, R. Brinkman (2009). A Survey of FCM Cytometry Data Analysis Methods. Advances in Bioinformatics. Volume 2009, Article ID 584603.
 P. Rota, S. Groeneveld-Krentz, M. Reiter. On Automated Flow Cytometric Analysis for MRD Estimation of Acute Lymphoblastic Leukaemia: A Comparison Among Different Approaches, The IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2015.
 M. Reiter, M. Gau, P. Rota, F. Kleber, S. Groeneveld-Krentz, A. Schumich, M. Dworzak: An Automated Flowcytometry Data Analysis Support System, CYTO 2015, Glasgow.
 M. Reiter, J. Hoffmann, F. Kleber, A. Schumich, G. Peter, M. Kampel, M. Dworzak Towards Automation of Flow Cytometric Analysis for Quality-Assured Follow-up Assessment to Guide Curative Therapy for Acute Lymphoblastic Leukaemia in Children, memo – Magazine of European Medical Oncology 7(4). 2014.
 F. Kromp, M. Reiter, S. Taschner-Mandl, P. Ambros, A. Hanbury. Classification of cellular populations using Image Scatter-Plots. Proceedings of the 20th Computer Vision Winter Workshop, p. 113–20, February 9 – 11, 2015. Seggau, Austria; 2015.
 P. Rota, F. Kleber, M. Reiter, S. Groeneveld-Krentz and M. Kampel, The Role of Machine Learning in Medical Data Analysis. A Case Study: Flow Cytometry, in the proceedings of International Conference on Computer Vision Theory and Application VISIGRAPP, 2016, Rome.
 FlowVIEW Functionalities: https://www.youtube.com/watch?v=fu0V76Cppa4 Accessed (March 29th, 2016).
 M. Reiter, F. Kleber, J. Hoffmann, M. Dworzak: “Automation of MRD Measurements in Flow Cytometry to Guide Curative Therapy for ALL in Children”. 4th Munich Biomarker Conference. Munich. 2014.