Computational Assessment of Spectral Heterogeneity within Fresh Glioblastoma Tissue Using Raman Spectroscopy and Machine Learning Algorithms

Karoline Klein, Gilbert Georg Klamminger*, Laurent Mombaerts, Finn Jelke, Isabel Fernandes Arroteia, Rédouane Slimani, Giulia Mirizzi, Andreas Husch, Katrin B.M. Frauenknecht, Michel Mittelbronn, Frank Hertel, Felix B. Kleine Borgmann*

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

Abstract

Understanding and classifying inherent tumor heterogeneity is a multimodal approach, which can be undertaken at the genetic, biochemical, or morphological level, among others. Optical spectral methods such as Raman spectroscopy aim at rapid and non-destructive tissue analysis, where each spectrum generated reflects the individual molecular composition of an examined spot within a (heterogenous) tissue sample. Using a combination of supervised and unsupervised machine learning methods as well as a solid database of Raman spectra of native glioblastoma samples, we succeed not only in distinguishing explicit tumor areas—vital tumor tissue and necrotic tumor tissue can correctly be predicted with an accuracy of 76%—but also in determining and classifying different spectral entities within the histomorphologically distinct class of vital tumor tissue. Measurements of non-pathological, autoptic brain tissue hereby serve as a healthy control since their respective spectroscopic properties form an individual and reproducible cluster within the spectral heterogeneity of a vital tumor sample. The demonstrated decipherment of a spectral glioblastoma heterogeneity will be valuable, especially in the field of spectroscopically guided surgery to delineate tumor margins and to assist resection control.

Original languageEnglish
Article number979
JournalMolecules
Volume29
Issue number5
DOIs
Publication statusPublished - 23 Feb 2024

Keywords

  • brain tumor
  • glioblastoma
  • heterogeneity
  • machine learning
  • Raman spectroscopy
  • unsupervised learning
  • vibrational spectroscopy

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