Raman spectroscopy in lung cancer diagnostics: Can an in vivo setup compete with ex vivo applications?

Autor: Fousková M; Department of Analytical Chemistry, University of Chemistry and Technology, Prague, Technická 5, 166 28, Prague 6, Czech Republic., Habartová L; Department of Analytical Chemistry, University of Chemistry and Technology, Prague, Technická 5, 166 28, Prague 6, Czech Republic., Vališ J; Department of Analytical Chemistry, University of Chemistry and Technology, Prague, Technická 5, 166 28, Prague 6, Czech Republic., Nahodilová M; Department of Analytical Chemistry, University of Chemistry and Technology, Prague, Technická 5, 166 28, Prague 6, Czech Republic., Vaňková A; Department of Analytical Chemistry, University of Chemistry and Technology, Prague, Technická 5, 166 28, Prague 6, Czech Republic., Synytsya A; Department of Analytical Chemistry, University of Chemistry and Technology, Prague, Technická 5, 166 28, Prague 6, Czech Republic., Šestáková Z; 1st Clinic of Tuberculosis and Respiratory Diseases, 1st Faculty of Medicine, Charles University Prague and General University Hospital in Prague, U Nemocnice 2, 128 08, Prague 2, Czech Republic., Votruba J; 1st Clinic of Tuberculosis and Respiratory Diseases, 1st Faculty of Medicine, Charles University Prague and General University Hospital in Prague, U Nemocnice 2, 128 08, Prague 2, Czech Republic., Setnička V; Department of Analytical Chemistry, University of Chemistry and Technology, Prague, Technická 5, 166 28, Prague 6, Czech Republic. Electronic address: Vladimir.Setnicka@vscht.cz.
Jazyk: angličtina
Zdroj: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy [Spectrochim Acta A Mol Biomol Spectrosc] 2024 Dec 05; Vol. 322, pp. 124770. Date of Electronic Publication: 2024 Jul 03.
DOI: 10.1016/j.saa.2024.124770
Abstrakt: Lung carcinoma remains the leading cause of cancer death worldwide. The tactic to change this unfortunate rate may be a timely and rapid diagnostic, which may in many cases improve patient prognosis. In our study, we focus on the comparison of two novel methods of rapid lung carcinoma diagnostics, label-free in vivo and ex vivo Raman spectroscopy of the epithelial tissue, and assess their feasibility in clinical practice. As these techniques are sensitive not only to the basic molecular composition of the analyzed sample but also to the secondary structure of large biomolecules, such as tissue proteins, they represent suitable candidate methods for epithelial cancer diagnostics. During routine bronchoscopy, we collected 78 in vivo Raman spectra of normal and cancerous lung tissue and 37 samples of endobronchial pathologies, which were subsequently analyzed ex vivo. Using machine learning techniques, namely principal component analysis (PCA) and support vector machines (SVM), we were able to reach 87.2% (95% CI, 79.8-94.6%) and 100.0% (95% CI, 92.1-100.0%) of diagnostic accuracy for in vivo and ex vivo setup, respectively. Although the ex vivo approach provided superior results, the rapidity of in vivo Raman spectroscopy might become unmatchable in the acceleration of the diagnostic process.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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