Fourier transform infrared spectroscopic marker of glioblastoma obtained from machine learning and changes in the spectra

Bartłomiej Tołpa; Joanna Depciuch; Paweł Jakubczyk; Wiesław Paja; Krzysztof Pancerz; Agnieszka Wosiak; Ewa Kaznowska; Agnieszka Gala-Błądzińska; Józef Cebulski

doi:10.1016/j.pdpdt.2023.103550

Fourier transform infrared spectroscopic marker of glioblastoma obtained from machine learning and changes in the spectra

Photodiagnosis Photodyn Ther. 2023 Jun:42:103550. doi: 10.1016/j.pdpdt.2023.103550. Epub 2023 Apr 5.

Authors

Bartłomiej Tołpa¹, Joanna Depciuch², Paweł Jakubczyk³, Wiesław Paja⁴, Krzysztof Pancerz⁵, Agnieszka Wosiak⁶, Ewa Kaznowska⁷, Agnieszka Gala-Błądzińska⁷, Józef Cebulski³

Affiliations

¹ Department of Neurosurgery, Clinical Hospital Nr 2 in Rzeszow, Lwowska 60, 35-309, Poland.
² Institute of Nuclear Physics, Polish Academy of Sciences, 31-342 Krakow, Poland; Department of Biochemistry and Molecular Biology, Medical University of Lublin, 20-093 Lublin, Poland. Electronic address: joanna.depciuch@ifj.edu.pl.
³ Institute of Physics, College of Natural Sciences, University of Rzeszow, PL-35959 Rzeszow Poland.
⁴ Institute of Computer Science, College of Natural Sciences, University of Rzeszow, Poland.
⁵ Institute of Philosophy, John Paul II Catholic University of Lublin, Poland.
⁶ Institute of Information Technology, Lodz University of Technology, Poland.
⁷ Institute of Medical Sciences, Medical College of Rzeszów University, Rzeszów, Poland.

PMID: 37024000
DOI: 10.1016/j.pdpdt.2023.103550

Abstract

Background: Glioblastoma is among the most malignant brain cancer with an average survival rate measured in months. In neurosurgical practice, it is considered impossible to completely remove a glioblastoma because of difficulties in the intraoperative assessment of the boundaries between healthy brain tissue and glioblastoma cells. Therefore, it is important to find a new, quick, cost-effective and useful neurosurgical practice method for the intraoperative differentiation of glioblastoma from healthy brain tissue.

Methods: Herein, the features of absorbance at specific wavenumbers considered characteristic of glioblastoma tissues could be markers of this cancer. We used Fourier transform infrared spectroscopy to measure the spectra of tissues collected from control and patients suffering from glioblastoma.

Results: The spectrum obtained from glioblastoma tissues demonstrated an additional peak at 1612 cm^-1 and a shift of peaks at 1675 cm^-1 and 1637 cm^-1. Deconvolution of amide I vibrations showed that in the glioblastoma tissue, the percentage amount of β-sheet is around 20% higher than that in the control. Moreover, the principal component analysis showed that using fingerprint and amide I regions it is possible to distinguish cancer and non-cancer samples. Machine learning methods presented that the accuracy of the results is around 100%. Finally, analysis of the differences in the rate of change of Fourier transform infrared spectroscopy spectra showed that absorbance features between 1053 cm^-1 and 1056 cm^-1 as well as between 1564 cm^-1 and 1588 cm^-1 are characteristic of glioblastoma.

Conclusion: Calculated features of absorbance at specific wavenumbers could be used as a spectroscopic marker of glioblastoma which may be useful in the future for neuronavigation.

Keywords: Fourier transform infrared spectroscopy; Glioblastoma; Machine learning; Principal component analysis; Spectroscopic marker.

MeSH terms

Fourier Analysis
Glioblastoma* / diagnosis
Humans
Machine Learning
Photochemotherapy* / methods
Photosensitizing Agents
Spectroscopy, Fourier Transform Infrared / methods

Substances

Photosensitizing Agents