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Rapid discrimination of multiple myeloma patients by artificial neural networks coupled with mass spectrometry of peripheral blood plasma

M. Deulofeu, L. Kolářová, V. Salvadó, E. María Peña-Méndez, M. Almáši, M. Štork, L. Pour, P. Boadas-Vaello, S. Ševčíková, J. Havel, P. Vaňhara,

. 2019 ; 9 (1) : 7975. [pub] 20190528

Language English Country Great Britain

Document type Journal Article, Research Support, Non-U.S. Gov't

Persistent link   https://www.medvik.cz/link/bmc20028902

Multiple myeloma (MM) is a highly heterogeneous disease of malignant plasma cells. Diagnosis and monitoring of MM patients is based on bone marrow biopsies and detection of abnormal immunoglobulin in serum and/or urine. However, biopsies have a single-site bias; thus, new diagnostic tests and early detection strategies are needed. Matrix-Assisted Laser Desorption/Ionization Time-of Flight Mass Spectrometry (MALDI-TOF MS) is a powerful method that found its applications in clinical diagnostics. Artificial intelligence approaches, such as Artificial Neural Networks (ANNs), can handle non-linear data and provide prediction and classification of variables in multidimensional datasets. In this study, we used MALDI-TOF MS to acquire low mass profiles of peripheral blood plasma obtained from MM patients and healthy donors. Informative patterns in mass spectra served as inputs for ANN that specifically predicted MM samples with high sensitivity (100%), specificity (95%) and accuracy (98%). Thus, mass spectrometry coupled with ANN can provide a minimally invasive approach for MM diagnostics.

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$a Deulofeu, Meritxell $u Department of Histology and Embryology, Faculty of Medicine, Masaryk University, Brno, Czech Republic. Research Group of Clinical Anatomy, Embryology and Neuroscience (NEOMA), Department of Medical Sciences, University of Girona, Girona, Spain. Experimental Neurophysiology and Clinical Anatomy (NE∾ 2017 SGR 01279), Department of Medical Sciences, University of Girona, Girona, Spain.
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$a Multiple myeloma (MM) is a highly heterogeneous disease of malignant plasma cells. Diagnosis and monitoring of MM patients is based on bone marrow biopsies and detection of abnormal immunoglobulin in serum and/or urine. However, biopsies have a single-site bias; thus, new diagnostic tests and early detection strategies are needed. Matrix-Assisted Laser Desorption/Ionization Time-of Flight Mass Spectrometry (MALDI-TOF MS) is a powerful method that found its applications in clinical diagnostics. Artificial intelligence approaches, such as Artificial Neural Networks (ANNs), can handle non-linear data and provide prediction and classification of variables in multidimensional datasets. In this study, we used MALDI-TOF MS to acquire low mass profiles of peripheral blood plasma obtained from MM patients and healthy donors. Informative patterns in mass spectra served as inputs for ANN that specifically predicted MM samples with high sensitivity (100%), specificity (95%) and accuracy (98%). Thus, mass spectrometry coupled with ANN can provide a minimally invasive approach for MM diagnostics.
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$a Kolářová, Lenka $u Department of Chemistry, Faculty of Science, Masaryk University, Brno, Czech Republic.
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$a Salvadó, Victoria $u Department of Chemistry, Faculty of Science, University of Girona, Girona, Spain.
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$a María Peña-Méndez, Eladia $u Department of Chemistry, Analytical Chemistry Division, Faculty of Science, University of La Laguna, La Laguna, Spain.
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$a Almáši, Martina $u Department of Clinical Hematology, University Hospital Brno, Brno, Czech Republic.
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$a Štork, Martin $u Department of Internal Medicine, Hematology and Oncology, University Hospital Brno, Brno, Czech Republic.
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$a Pour, Luděk $u Department of Internal Medicine, Hematology and Oncology, University Hospital Brno, Brno, Czech Republic.
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$a Boadas-Vaello, Pere $u Research Group of Clinical Anatomy, Embryology and Neuroscience (NEOMA), Department of Medical Sciences, University of Girona, Girona, Spain. Experimental Neurophysiology and Clinical Anatomy (NE∾ 2017 SGR 01279), Department of Medical Sciences, University of Girona, Girona, Spain.
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$a Ševčíková, Sabina $u Babak Myeloma Group, Department of Pathological Physiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic.
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$a Havel, Josef $u Department of Chemistry, Faculty of Science, Masaryk University, Brno, Czech Republic. International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic.
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$a Vaňhara, Petr $u Department of Histology and Embryology, Faculty of Medicine, Masaryk University, Brno, Czech Republic. pvanhara@med.muni.cz. International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic. pvanhara@med.muni.cz.
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