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Journal of Biomedical Optics

Neural networks improve brain cancer detection with Raman spectroscopy in the presence of operating room light artifacts
Author(s): Michael Jermyn; Joannie Desroches; Jeanne Mercier; Marie-Andrée Tremblay; Karl St-Arnaud; Marie-Christine Guiot; Kevin Petrecca; Frédéric Leblond
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Paper Abstract

Invasive brain cancer cells cannot be visualized during surgery and so they are often not removed. These residual cancer cells give rise to recurrences. <italic<In vivo</italic< Raman spectroscopy can detect these invasive cancer cells in patients with grade 2 to 4 gliomas. The robustness of this Raman signal can be dampened by spectral artifacts generated by lights in the operating room. We found that artificial neural networks (ANNs) can overcome these spectral artifacts using nonparametric and adaptive models to detect complex nonlinear spectral characteristics. Coupling ANN with Raman spectroscopy simplifies the intraoperative use of Raman spectroscopy by limiting changes required to the standard neurosurgical workflow. The ability to detect invasive brain cancer under these conditions may reduce residual cancer remaining after surgery and improve patient survival.

Paper Details

Date Published: 8 September 2016
PDF: 6 pages
J. Biomed. Opt. 21(9) 094002 doi: 10.1117/1.JBO.21.9.094002
Published in: Journal of Biomedical Optics Volume 21, Issue 9
Show Author Affiliations
Michael Jermyn, McGill Univ. (Canada)
Ecole Polytechnique de Montréal (Canada)
Joannie Desroches, Ecole Polytechnique de Montréal (Canada)
Jeanne Mercier, Ecole Polytechnique de Montréal (Canada)
Marie-Andrée Tremblay, Ecole Polytechnique de Montréal (Canada)
Karl St-Arnaud, Ecole Polytechnique de Montréal (Canada)
Marie-Christine Guiot, Montreal Neurological Hospital and Institute (Canada)
McGill Univ. (Canada)
Kevin Petrecca, McGill Univ. (Canada)
Frédéric Leblond, Ecole Polytechnique de Montréal (Canada)
Ctr. Hospitalier de l'Univ. de Montréal (Canada)

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