TY - JOUR
T1 - Application of Raman spectroscopy for detection of histologically distinct areas in formalin-fixed paraffin-embedded glioblastoma
AU - Klamminger, Gilbert Georg
AU - Gérardy, Jean Jacques
AU - Jelke, Finn
AU - Mirizzi, Giulia
AU - Slimani, Rédouane
AU - Klein, Karoline
AU - Husch, Andreas
AU - Hertel, Frank
AU - Mittelbronn, Michel
AU - Kleine-Borgmann, Felix B.
N1 - Funding Information:
This work was supported by Fondation Cancer Luxemburg (grant to F.H., M.M., A.H., and F.K.) and Luxembourg National Research Fund, FNR (FNR PEARL P16/BM/11192868 grant to M.M.) Acknowledgment
Publisher Copyright:
© 2021 Neuro-Oncology Advances. All right reserved.
PY - 2021/6/18
Y1 - 2021/6/18
N2 - 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" that 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 an 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 the 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 conclusion, we propose that RS may serve useful as a future method in the pathological toolbox.
AB - 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" that 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 an 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 the 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 conclusion, we propose that RS may serve useful as a future method in the pathological toolbox.
KW - FFPE
KW - glioblastoma
KW - machine learning
KW - pathology
KW - Raman spectroscopy
UR - http://www.scopus.com/inward/record.url?scp=85126574198&partnerID=8YFLogxK
U2 - 10.1093/noajnl/vdab077
DO - 10.1093/noajnl/vdab077
M3 - Article
C2 - 34355170
SN - 2632-2498
VL - 3
SP - vdab077
JO - Neuro-Oncology Advances
JF - Neuro-Oncology Advances
IS - 1
M1 - vdab077
ER -