Rapid discrimination of multiple myeloma patients by artificial neural networks coupled with mass spectrometry of peripheral blood plasma
| Authors | |
|---|---|
| Year of publication | 2019 |
| Type | Article in Periodical |
| Magazine / Source | Scientific Reports |
| MU Faculty or unit | |
| Citation | |
| web | https://www.nature.com/articles/s41598-019-44215-1 |
| Doi | http://dx.doi.org/10.1038/s41598-019-44215-1 |
| Keywords | Serum; Biomarkers; Identification; Diagnosis; Criteria; Model |
| Description | 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. Artifcial intelligence approaches, such as Artifcial Neural Networks (ANNs), can handle non-linear data and provide prediction and classifcation of variables in multidimensional datasets. In this study, we used MALDI-TOF MS to acquire low mass profles of peripheral blood plasma obtained from MM patients and healthy donors. Informative patterns in mass spectra served as inputs for ANN that specifcally predicted MM samples with high sensitivity (100%), specifcity (95%) and accuracy (98%). Thus, mass spectrometry coupled with ANN can provide a minimally invasive approach for MM diagnostics. |
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