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.

Základní údaje

Originální název

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

Autoři

Vydání

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

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

30205 Hematology

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 3.998

Kód RIV

RIV/00216224:14110/19:00108502

Organizační jednotka

Lékařská fakulta

DOI

https://doi.org/10.1038/s41598-019-44215-1

UT WoS

000469218200021

EID Scopus

2-s2.0-85067291129

Klíčová slova anglicky

Serum; Biomarkers; Identification; Diagnosis; Criteria; Model

Štítky

14110517, 14110518, podil, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 14. 4. 2020 14:35, Mgr. Tereza Miškechová

Anotace

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.

Návaznosti

MUNI/A/1298/2017, interní kód MU

Název: Zdroje pro tkáňové inženýrství 8 (Akronym: TissueEng 8)

Investor: Masarykova univerzita, Zdroje pro tkáňové inženýrství 8, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

MUNI/A/1553/2018, interní kód MU

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

Investor: Masarykova univerzita, Genetické, environmentální a tkáňové charakteristiky vybraných patologických stavů a nemocí, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

NV17-29343A, projekt VaV

Název: 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 a 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, roč. 9, MAY 28 2019, s. 1-7. ISSN 2045-2322. Dostupné z: https://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 = {https://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 a 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, roč.~9, MAY 28 2019, s.~1-7. ISSN~2045-2322. Dostupné z: https://doi.org/10.1038/s41598-019-44215-1.

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