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.

• MeSH
• Principal Component Analysis MeSH
• Datasets as Topic MeSH
• Immunoglobulins blood   MeSH
• Bone Marrow metabolism   pathology   MeSH
• Middle Aged MeSH
• Humans MeSH
• Metabolic Networks and Pathways MeSH
• Metabolome * MeSH
• Multiple Myeloma blood   diagnosis   pathology   MeSH
• Neural Networks, Computer * MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization MeSH
• Case-Control Studies MeSH
• Artificial Intelligence * statistics & numerical data   MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Immunoglobulins MeSH