TY - JOUR

T1 - Malignant and benign thyroid nodule differentiation through the analysis of blood plasma with terahertz spectroscopy

AU - Konnikova, Maria R.

AU - Cherkasova, Olga P.

AU - Nazarov, Maxim M.

AU - Vrazhnov, Denis A.

AU - Kistenev, Yuri V.

AU - Titov, Sergei E.

AU - Kopeikina, Elena V.

AU - Shevchenko, Sergei P.

AU - Shkurinov, Alexander P.

N1 - Publisher Copyright: © 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/2/1

Y1 - 2021/2/1

N2 - The liquid and lyophilized blood plasma of patients with benign or malignant thyroid nodules and healthy individuals were studied by terahertz (THz) time-domain spectroscopy and machine learning. The blood plasma samples from malignant nodule patients were shown to have higher absorption. The glucose concentration and miRNA-146b level were correlated with the sample's absorption at 1 THz. A two-stage ensemble algorithm was proposed for the THz spectra analysis. The first stage was based on the Support Vector Machine with a linear kernel to separate healthy and thyroid nodule participants. The second stage included additional data preprocessing by Ornstein-Uhlenbeck kernel Principal Component Analysis to separate benign and malignant thyroid nodule participants. Thus, the distinction of malignant and benign thyroid nodule patients through their lyophilized blood plasma analysis by terahertz time-domain spectroscopy and machine learning was demonstrated.

AB - The liquid and lyophilized blood plasma of patients with benign or malignant thyroid nodules and healthy individuals were studied by terahertz (THz) time-domain spectroscopy and machine learning. The blood plasma samples from malignant nodule patients were shown to have higher absorption. The glucose concentration and miRNA-146b level were correlated with the sample's absorption at 1 THz. A two-stage ensemble algorithm was proposed for the THz spectra analysis. The first stage was based on the Support Vector Machine with a linear kernel to separate healthy and thyroid nodule participants. The second stage included additional data preprocessing by Ornstein-Uhlenbeck kernel Principal Component Analysis to separate benign and malignant thyroid nodule participants. Thus, the distinction of malignant and benign thyroid nodule patients through their lyophilized blood plasma analysis by terahertz time-domain spectroscopy and machine learning was demonstrated.

UR - http://www.scopus.com/inward/record.url?scp=85102051700&partnerID=8YFLogxK

U2 - 10.1364/BOE.412715

DO - 10.1364/BOE.412715

M3 - Article

C2 - 33680557

AN - SCOPUS:85102051700

VL - 12

SP - 1020

EP - 1035

JO - Biomedical Optics Express

JF - Biomedical Optics Express

SN - 2156-7085

IS - 2

ER -