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Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 30222

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Development of a Telemedical Network Supporting an Automated Flow Cytometric Analysis for the Clinical Follow-up of Leukaemia
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.
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[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] A. Bashashati, R. Brinkman (2009). A Survey of FCM Cytometry Data Analysis Methods. Advances in Bioinformatics. Volume 2009, Article ID 584603.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
[18] FlowVIEW Functionalities: Accessed (March 29th, 2016).
[19] 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.
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