Application of Raman spectroscopy for detection of histologically distinct areas in formalin-fixed paraffin-embedded glioblastoma
| Autor: | Frank Hertel, Giulia Mirizzi, Finn Jelke, Redouane Slimani, Gilbert Georg Klamminger, Michel Mittelbronn, Felix B Kleine-Borgmann, Andreas Husch, Jean-Jacques Gérardy, Karoline Klein |
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| Přispěvatelé: | Fondation Cancer [sponsor] |
| Rok vydání: | 2021 |
| Předmět: |
Pathology
medicine.medical_specialty Multidisciplinaire généralités & autres [D99] [Sciences de la santé humaine] Formalin fixed paraffin embedded Molecular pathology glioblastoma Glioma Gold standard (test) Biology FFPE Molecular Fingerprint medicine.disease Machine Learning Support vector machine machine learning Basic and Translational Investigations Raman spectroscopy medicine AcademicSubjects/MED00300 AcademicSubjects/MED00310 pathology Classifier (UML) Multidisciplinary general & others [D99] [Human health sciences] Glioblastoma |
| Zdroj: | Neuro-oncology Advances |
| ISSN: | 2632-2498 |
| DOI: | 10.1093/noajnl/vdab077 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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