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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

Jazyk angličtina Země Velká Británie

Typ dokumentu časopisecké články, práce podpořená grantem

Perzistentní odkaz   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.

Citace poskytuje Crossref.org

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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 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 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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