Reference : Application of Raman Spectroscopy for Detection of Histologically Distinct Areas in F...
Scientific journals : Article
Human health sciences : Multidisciplinary, general & others
http://hdl.handle.net/10993/47587
Application of Raman Spectroscopy for Detection of Histologically Distinct Areas in Formalin-fixed Paraffin-embedded (FFPE) Glioblastoma
English
Klamminger, Gilbert Georg [> >]
Gerardy, Jean-Jacques [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) >]
Jelke, Finn [> >]
Mirizzi, Giulia [> >]
Slimani, Rédouane [> >]
Klein, Karoline [> >]
Husch, Andreas mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Interventional Neuroscience]
Hertel, Frank [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) >]
Mittelbronn, Michel [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) >]
Kleine-Borgmann, Felix B. [> >]
2021
Neuro-Oncology Advances
Yes
International
2632-2498
[en] Raman spectroscopy ; Glioma ; Machine Learning
[en] Background

Although microscopic assessment is still the diagnostic gold standard in pathology, non-light microscopic methods such as new imaging methods and molecular pathology have considerably contributed to more precise diagnostics. As an upcoming method, Raman spectroscopy (RS) offers a "molecular fingerprint" which could be used to differentiate tissue heterogeneity or diagnostic entities. RS has been successfully applied on fresh and frozen tissue, however more aggressively, chemically treated tissue such as formalin-fixed, paraffin-embedded (FFPE) samples are challenging for RS.
Methods

To address this issue, we examined FFPE samples of morphologically highly heterogeneous glioblastoma (GBM) using RS in order to classify histologically defined GBM areas according to RS spectral properties. We have set up a SVM (support vector machine)-based classifier in a training cohort and corroborated our findings in a validation cohort.
Results

Our trained classifier identified distinct histological areas such as tumor core and necroses in GBM with an overall accuracy of 70.5% based on spectral properties of RS. With an absolute misclassification of 21 out of 471 Raman measurements, our classifier has the property of precisely distinguishing between normal appearing brain tissue and necrosis. When verifying the suitability of our classifier system in a second independent dataset, very little overlap between necrosis and normal appearing brain tissue can be detected.
Conclusion

These findings show that histologically highly variable samples such as GBM can be reliably recognized by their spectral properties using RS. As a conclusion, we propose that RS may serve useful as a future method in the pathological toolbox.
Fondation Cancer
INSITU
Researchers ; Professionals
http://hdl.handle.net/10993/47587
10.1093/noajnl/vdab077
https://doi.org/10.1093/noajnl/vdab077

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
vdab077.pdfAuthor preprint873.71 kBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.