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

J 2019

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

DEULOFEU FIGUERAS, Meritxell, Lenka KOLÁŘOVÁ, Victoria SALVADÓ, Eladia María PEÑA-MÉNDEZ, Martina ALMÁŠI et. al.

Basic information

Original name

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

Authors

DEULOFEU FIGUERAS, Meritxell (724 Spain, belonging to the institution), Lenka KOLÁŘOVÁ (203 Czech Republic, belonging to the institution), Victoria SALVADÓ (724 Spain), Eladia María PEÑA-MÉNDEZ (724 Spain), Martina ALMÁŠI (203 Czech Republic), Martin ŠTORK (203 Czech Republic), Luděk POUR (203 Czech Republic), Pere BOADAS-VAELLO (724 Spain), Sabina ŠEVČÍKOVÁ (203 Czech Republic, belonging to the institution), Josef HAVEL (203 Czech Republic, belonging to the institution) and Petr VAŇHARA (203 Czech Republic, guarantor, belonging to the institution)

Edition

Scientific Reports, London, Nature Publishing Group, 2019, 2045-2322

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30205 Hematology

Country of publisher

United Kingdom of Great Britain and Northern Ireland

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 3.998

RIV identification code

RIV/00216224:14110/19:00108502

Organization unit

Faculty of Medicine

DOI

http://dx.doi.org/10.1038/s41598-019-44215-1

UT WoS

000469218200021

Keywords in English

Serum; Biomarkers; Identification; Diagnosis; Criteria; Model

Abstract

V originále

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.

Links

MUNI/A/1298/2017, interní kód MU

Name: Zdroje pro tkáňové inženýrství 8 (Acronym: TissueEng 8)

Investor: Masaryk University, Category A

MUNI/A/1553/2018, interní kód MU

Name: Genetické, environmentální a tkáňové charakteristiky vybraných patologických stavů a nemocí (Acronym: genetika; stres; biomateriály; tkáňové kultury)

Investor: Masaryk University, Category A

NV17-29343A, research and development project

Name: Analýza mikroprostředí kostní dřeně u extramedulárního relapsu mnohočetného myelomu

Citovat

DEULOFEU FIGUERAS, Meritxell, Lenka KOLÁŘOVÁ, Victoria SALVADÓ, Eladia María PEÑA-MÉNDEZ, Martina ALMÁŠI, Martin ŠTORK, Luděk POUR, Pere BOADAS-VAELLO, Sabina ŠEVČÍKOVÁ, Josef HAVEL and Petr VAŇHARA. Rapid discrimination of multiple myeloma patients by artificial neural networks coupled with mass spectrometry of peripheral blood plasma. Scientific Reports. London: Nature Publishing Group, 2019, vol. 9, MAY 28 2019, p. 1-7. ISSN 2045-2322. Available from: https://dx.doi.org/10.1038/s41598-019-44215-1.

@article{1550208,
   author = {Deulofeu Figueras, Meritxell and Kolářová, Lenka and Salvadó, Victoria and PeñaandMéndez, Eladia María and Almáši, Martina and Štork, Martin and Pour, Luděk and BoadasandVaello, Pere and Ševčíková, Sabina and Havel, Josef and Vaňhara, Petr},
   article_location = {London},
   article_number = {MAY 28 2019},
   doi = {http://dx.doi.org/10.1038/s41598-019-44215-1},
   keywords = {Serum; Biomarkers; Identification; Diagnosis; Criteria; Model},
   language = {eng},
   issn = {2045-2322},
   journal = {Scientific Reports},
   title = {Rapid discrimination of multiple myeloma patients by artificial neural networks coupled with mass spectrometry of peripheral blood plasma},
   url = {https://www.nature.com/articles/s41598-019-44215-1},
   volume = {9},
   year = {2019}
}

TY  - JOUR
ID  - 1550208
AU  - Deulofeu Figueras, Meritxell - Kolářová, Lenka - Salvadó, Victoria - Peña-Méndez, Eladia María - Almáši, Martina - Štork, Martin - Pour, Luděk - Boadas-Vaello, Pere - Ševčíková, Sabina - Havel, Josef - Vaňhara, Petr
PY  - 2019
TI  - Rapid discrimination of multiple myeloma patients by artificial neural networks coupled with mass spectrometry of peripheral blood plasma
JF  - Scientific Reports
VL  - 9
IS  - MAY 28 2019
SP  - 1-7
EP  - 1-7
PB  - Nature Publishing Group
SN  - 20452322
KW  - Serum
KW  - Biomarkers
KW  - Identification
KW  - Diagnosis
KW  - Criteria
KW  - Model
UR  - https://www.nature.com/articles/s41598-019-44215-1
L2  - https://www.nature.com/articles/s41598-019-44215-1
N2  - 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.
ER  -

DEULOFEU FIGUERAS, Meritxell, Lenka KOLÁŘOVÁ, Victoria SALVADÓ, Eladia María PEÑA-MÉNDEZ, Martina ALMÁŠI, Martin ŠTORK, Luděk POUR, Pere BOADAS-VAELLO, Sabina ŠEVČÍKOVÁ, Josef HAVEL and Petr VAŇHARA. Rapid discrimination of multiple myeloma patients by artificial neural networks coupled with mass spectrometry of peripheral blood plasma. \textit{Scientific Reports}. London: Nature Publishing Group, 2019, vol.~9, MAY 28 2019, p.~1-7. ISSN~2045-2322. Available from: https://dx.doi.org/10.1038/s41598-019-44215-1.

Detailed Information on Publication Record