FT-Raman spectra in combination with machine learning and multivariate analyses as a diagnostic tool in brain tumors
Copyright © 2024. Published by Elsevier Inc..
Brain tumors are one of the most dangerous, because the position of these are in the organ that governs all life processes. Moreover, a lot of brain tumor types were observed, but only one main diagnostic method was used - histopathology, for which preparation of sample was long. Consequently, a new, quicker diagnostic method is needed. In this paper, FT-Raman spectra of brain tissues were analyzed by Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), four different machine learning (ML) algorithms to show possibility of differentiating between glioblastoma G4 and meningiomas, as well as two different types of meningiomas (atypical and angiomatous). Obtained results showed that in meningiomas additional peak around 1503 cm-1 and higher level of amides was noticed in comparison with glioblastoma G4. In the case of meningiomas differentiation, in angiomatous meningiomas tissues lower level of lipids and polysaccharides were visible than in atypical meningiomas. Moreover, PCA analyses showed higher distinction between glioblastoma G4 and meningiomas in the FT-Raman range between 800 cm-1 and 1800 cm-1 and between two types of meningiomas in the range between 2700 cm-1 and 3000 cm-1. Decision trees showed, that the most important peaks to differentiate glioblastoma and meningiomas were at 1151 cm-1 and 2836 cm-1 while for angiomatous and atypical meningiomas - 1514 cm-1 and 2875 cm-1. Furthermore, the accuracy of obtained results for glioblastoma G4 and meningiomas was 88 %, while for meningiomas - 92 %. Consequently, obtained data showed possibility of using FT-Raman spectroscopy in diagnosis of different types of brain tumors.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:57 |
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| Enthalten in: |
Nanomedicine : nanotechnology, biology, and medicine - 57(2024) vom: 01. Apr., Seite 102737 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Tołpa, Bartłomiej [VerfasserIn] |
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| Links: |
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| Themen: |
Differentiation |
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| Anmerkungen: |
Date Completed 02.04.2024 Date Revised 02.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.nano.2024.102737 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Brain tumors are one of the most dangerous, because the position of these are in the organ that governs all life processes. Moreover, a lot of brain tumor types were observed, but only one main diagnostic method was used - histopathology, for which preparation of sample was long. Consequently, a new, quicker diagnostic method is needed. In this paper, FT-Raman spectra of brain tissues were analyzed by Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), four different machine learning (ML) algorithms to show possibility of differentiating between glioblastoma G4 and meningiomas, as well as two different types of meningiomas (atypical and angiomatous). Obtained results showed that in meningiomas additional peak around 1503 cm-1 and higher level of amides was noticed in comparison with glioblastoma G4. In the case of meningiomas differentiation, in angiomatous meningiomas tissues lower level of lipids and polysaccharides were visible than in atypical meningiomas. Moreover, PCA analyses showed higher distinction between glioblastoma G4 and meningiomas in the FT-Raman range between 800 cm-1 and 1800 cm-1 and between two types of meningiomas in the range between 2700 cm-1 and 3000 cm-1. Decision trees showed, that the most important peaks to differentiate glioblastoma and meningiomas were at 1151 cm-1 and 2836 cm-1 while for angiomatous and atypical meningiomas - 1514 cm-1 and 2875 cm-1. Furthermore, the accuracy of obtained results for glioblastoma G4 and meningiomas was 88 %, while for meningiomas - 92 %. Consequently, obtained data showed possibility of using FT-Raman spectroscopy in diagnosis of different types of brain tumors | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Differentiation | |
| 650 | 4 | |a FT-Raman | |
| 650 | 4 | |a Glioblastoma G4 | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Meningiomas | |
| 650 | 4 | |a Multivariate analyses | |
| 700 | 1 | |a Paja, Wiesław |e verfasserin |4 aut | |
| 700 | 1 | |a Trojnar, Elżbieta |e verfasserin |4 aut | |
| 700 | 1 | |a Łach, Kornelia |e verfasserin |4 aut | |
| 700 | 1 | |a Gala-Błądzińska, Agnieszka |e verfasserin |4 aut | |
| 700 | 1 | |a Kowal, Aneta |e verfasserin |4 aut | |
| 700 | 1 | |a Gumbarewicz, Ewelina |e verfasserin |4 aut | |
| 700 | 1 | |a Frączek, Paulina |e verfasserin |4 aut | |
| 700 | 1 | |a Cebulski, Józef |e verfasserin |4 aut | |
| 700 | 1 | |a Depciuch, Joanna |e verfasserin |4 aut | |
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