Malignant and benign thyroid nodule differentiation through the analysis of blood plasma with terahertz spectroscopy
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 glucos...
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| Veröffentlicht in: | Biomedical optics express 2021-02, Vol.12 (2), p.1020-1035 |
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| creator | Konnikova, Maria R Cherkasova, Olga P Nazarov, Maxim M Vrazhnov, Denis A Kistenev, Yuri V Titov, Sergei E Kopeikina, Elena V Shevchenko, Sergei P Shkurinov, Alexander P |
| description | 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. |
| doi_str_mv | 10.1364/BOE.412715 |
| format | Article |
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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.</abstract><cop>United States</cop><pub>Optical Society of America</pub><pmid>33680557</pmid><doi>10.1364/BOE.412715</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0001-5760-1462</orcidid><orcidid>https://orcid.org/0000-0002-8035-2422</orcidid><oa>free_for_read</oa></addata></record> |
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| title | Malignant and benign thyroid nodule differentiation through the analysis of blood plasma with terahertz spectroscopy |
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