CC BY
16B-I-3
BIOMEDICAL PHOTONICS
Terahertz and Infrared Spectroscopy of blood plasma for glioblastoma diagnosis
O. Cherkasova1,2, M. Konnikova2,3, A. Mankova3, D. Vrazhnov4, Yu. Kistenev5, Y.Peng6, A. Shkurinov2,3
1-Institute of Laser Physics of SB RAS, pr. Lavrentyeva, 15B, 630090 Novosibirsk, Russia 2- Institute on Laser and Information Technologies - Branch of the Federal Scientific Research Centre "Crystallography and Photonics" of RAS, 1 Svyatoozerskaya st., 140700 Shatura, Moscow Region, Russia 3 - Lomonosov Moscow State University, 1 Leninskiye Gory, 119991 Moscow, Russia, 4 - Institute of Atmospheric Optics, Siberian Branch of the RAS, pr. Akademicheskii 1, 634055 Tomsk, Russia 5 - Tomsk State University, 36 Lenin Ave., 634050 Tomsk, Russia 6 - University of Shanghai for Science and Technology, 516 Jungong Rd., 200093 Shanghai, R. P. China
e-mail: o.p.cherkasova@gmail.com
Glioblastoma belongs to deadliest neoplasms. A reason for the glioblastoma poor outcome is a late-stage diagnosis [1]. In this work, we use Terahertz (THz) and Infrared (IR) spectroscopy to study mouse blood plasma in the dynamics of glioblastoma development. IR spectroscopy allows determining concentrations of different compounds present in a biological sample by characteristic frequencies of individual chemical groups [2]. On the other hand, THz spectroscopy is most sensitive to the state of the water, which can be in free and bound states [3]. We compare the capabilities of each method for the early diagnosis of glioma. U87 human glioblastoma in mice of the SCID line was created by the method detailed in [4]. Animals were removed from the experiment on days 7, 14, 21 and 28 after tumor cells inoculation. The THZ and IR spectra analysis was conducted by the Principal Component Analysis, and multidimensional scaling. The prognostic models were developed by the linear kernel Support vector machine, Random forests, and Gradient boosting methods [5, 6].
We have shown a decrease in THz absorption of blood plasma in the dynamics of glioblastoma development. Analysis of the Attenuated Total Reflectance (ATR)-FTIR blood plasma spectra showed an increase in the intensity of the characteristic bands with the glioblastoma development. It was demonstrated that it is possible to use THz and IR spectral data to differentiate glioma stages and to build a predictive model to distinguish between experimental and healthy groups. A machine learning pipeline was proposed to extract the informative features, with good results (sensitivity, specificity, accuracy over 90%). The use of automatic methods to remove outliers in the data improves the robustness of predictive models.
This work was supported by the Russian Foundation for Basic Research (grant # 19-52-55004), the Ministry of Science and Higher Education of the Russian Federation within the State assignment FSRC "Crystallography and Photonics" RAS, by the Interdisciplinary Scientific and Educational School of Moscow University "Photonic and Quantum Technologies. Digital Medicine". The research was carried out with the support of a grant under the Decree of the Government of the Russian Federation No. 220 of 09 April 2010 (Agreement No. 075-15-2021-615 of 04 June 2021).
[1] O. Cherkasova, Y. Peng, M. Konnikova, et al., "Diagnosis of Glioma Molecular Markers by Terahertz Technologies", Photonics, vol. 8(1), pp. 22, (2021).
[2] J. R. Hands, K. M. Dorling, P. Abel, et al., "Attenuated total reflection fourier transform infrared (ATR-FTIR) spectral discrimination of brain tumour severity from serum samples", JBiophotonics, vol. 7(3-4), pp. 189-199 (2014).
[3] M. M. Nazarov, O. P. Cherkasova, E. N. Lazareva, et al., "A Complex Study of the Peculiarities of Blood Serum Absorption of Rats with Experimental Liver Cancer", Opt. Spectrosc, vol. 126, pp. 721-729, (2019).
[4] E. L. Zavjalov, I. A. Razumov, L. A. Gerlinskaya, A. V. Romashchenko, "In vivo MRI Visualization of U87 Glioblastoma Development Dynamics in the Model of Orthotopic Xenotransplantation to the SCID Mouse", Russ. J. Genet. Appl. Res., vol. 6 (4), pp. 448-453, (2016).
[5]Kistenev Y.V., Borisov A.V., Kuzmin D.A. et al. J. Biomed. Opt. 22(1), 017002 (2017).
[6] N. Mazumder, G. Gangadharan, Yu. V. Kistenev, Advances in Brain Imaging Techniques (Springer Singapore), pp. 203-230, (2022).
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