gms | German Medical Science

56. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie e. V. (DGNC)
3èmes journées françaises de Neurochirurgie (SFNC)

Deutsche Gesellschaft für Neurochirurgie e. V.
Société Française de Neurochirurgie

07. bis 11.05.2005, Strasbourg

Classification of human glioma by infrared spectroscopy

Klassifikation humaner Gliome durch Infrarot-Spektroskopie

Meeting Abstract

  • corresponding author S. B. Sobottka - Klinik für Neurochirurgie, Universitätsklinikum Carl Gustav Carus, Technische Universität Dresden
  • C. Krafft - Institut für Analytische Chemie, Technische Universität Dresden
  • G. Steiner - Institut für Analytische Chemie, Technische Universität Dresden
  • R. Salzer - Institut für Analytische Chemie, Technische Universität Dresden
  • G. Schackert - Klinik für Neurochirurgie, Universitätsklinikum Carl Gustav Carus, Technische Universität Dresden

Deutsche Gesellschaft für Neurochirurgie. Société Française de Neurochirurgie. 56. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie e.V. (DGNC), 3èmes journées françaises de Neurochirurgie (SFNC). Strasbourg, 07.-11.05.2005. Düsseldorf, Köln: German Medical Science; 2005. Doc11.05.-06.06

The electronic version of this article is the complete one and can be found online at: http://www.egms.de/en/meetings/dgnc2005/05dgnc0197.shtml

Published: May 4, 2005

© 2005 Sobottka et al.
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Outline

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Objective

Optical spectroscopic imaging techniques have the potential to expand the ability to diagnose tissue beyond that of standard pathological methods because they characterise tissue at a microstructural molecular level with a spatial resolution in the single cell range. With the aim to achieve an intraoperative glioma grading, infrared spectroscopy was used to characterise and classify specimens of human glioma patients according to the WHO grading system.

Methods

Spectroscopic maps of native specimens of patients with human gliomas were collected ex vivo by infrared (IR) spectroscopy. Variations of tissue composition within each spectroscopic map were demonstrated by cluster analysis and were compared to histopathological findings. In certain glioma patients with inhomogeneous tumours showing regions of different tumour grades, multiple specimens were collected using neuronavigation. Spectroscopic maps were compared to histopathological and radiological findings.

Results

Accurate classification was achieved by a supervised multivariate algorithm called soft independent modelling of class analogies (SIMCA) and a genetic optimal region selection routine and linear discriminant analysis. A classification success rate of up to 96% was obtained for the glioma grading depending on the complexity of data analysis. Normal tissue and low-grade or high-grade regions of glioma could be reliably distinguished by IR spectroscopy.

Conclusions

Infrared spectroscopy has a great potential for identification and grading of cerebral glioma.