ArticleScientific reports2019
Rapid discrimination of multiple myeloma patients by artificial neural networks coupled with mass spectrometry of peripheral blood plasma.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Exploratory study on application of MALDI‑TOF‑MS to detect serum and urine peptides related to small cell lung carcinoma.Molecular medicine reports · 2020Trial
- Advances in the application of artificial intelligence in mass spectrometry-based analysis of traditional Chinese medicine: compound identification and metabolic pathway elucidation.Analytical and bioanalytical chemistry · 2026Review
- Article
- Detection of early relapse in multiple myeloma patients.Cell division · 2025Article
- Review
- Liquid biopsy of peripheral blood using mass spectrometry detects primary extramedullary disease in multiple myeloma patients.Scientific reports · 2024Article
- Improved Screening of Monoclonal Gammopathy Patients by MALDI-TOF Mass Spectrometry.Journal of the American Society for Mass Spectrometry · 2023Article
- Characteristics of inpatients with newly diagnosed multiple myeloma in hematology, nephrology, and orthopedic departments.Annals of hematology · 2023Article
- Artificial Neural Networks Coupled with MALDI-TOF MS Serum Fingerprinting To Classify and Diagnose Pathological Pain Subtypes in Preclinical Models.ACS chemical neuroscience · 2023Article
- Metabolic Volume Measurements in Multiple Myeloma.Metabolites · 2021Review
- Liquid biopsy: an evolving paradigm for the biological characterisation of plasma cell disorders.Leukemia · 2021Review
- Machine Learning Applications in the Diagnosis of Benign and Malignant Hematological Diseases.Clinical hematology international · 2021Review
- Detection of SARS-CoV-2 Infection in Human Nasopharyngeal Samples by Combining MALDI-TOF MS and Artificial Intelligence.Frontiers in medicine · 2021Article
- Artificial Intelligence in Plasma Cell Myeloma: Neural Networks and Support Vector Machines in the Classification of Plasma Cell Myeloma Data at Diagnosis.Journal of pathology informatics · 2021Article
- Artificial Intelligence in Hematology: Current Challenges and Opportunities.Current hematologic malignancy reports · 2020Review
- New Markers of Renal Failure in Multiple Myeloma and Monoclonal Gammopathies.Journal of clinical medicine · 2020Review
- Recent Updates in the Diagnosis and Management of Kidney Diseases in Multiple Myeloma.Indian journal of nephrologyReview
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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