期刊
JOURNAL OF BIOMEDICAL OPTICS
卷 21, 期 9, 页码 -出版社
SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
DOI: 10.1117/1.JBO.21.9.094002
关键词
Raman spectroscopy; brain cancer; neural networks; neurosurgery; machine learning
资金
- Fonds de Recherche du Quebec Nature et Technologies
- Natural Sciences and Engineering Research Council of Canada
- Canadian Institute of Health Research
- Groupe de Recherche en Sciences et Technologies Biomedicales
- Banque Nationale
Invasive brain cancer cells cannot be visualized during surgery and so they are often not removed. These residual cancer cells give rise to recurrences. In vivo 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 non-linear 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. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
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