TABLE 1. Summary of Raman spectroscopy techniques since 2002.

Authors & Year	Title	Summary
Raman spectroscopy
Desroches et al., 2015	Characterization of a Raman spectroscopy probe  system for intraoperative brain tissue classifica  tion	A handheld Raman probe is used to differentiate necrosis from vital  tissue (including tumor and normal brain tissue) with an accuracy of  87%.
Jermyn et al., 2015	Intraoperative brain cancer detection with Raman  spectroscopy in humans	An intraoperative Raman spectroscopy probe is used to differentiate  normal brain from dense tumor with 93% sensitivity and 91% specific  ity.
Kalkanis et al., 2014	Raman spectroscopy to distinguish grey matter,  necrosis, and glioblastoma multiforme in frozen  tissue sections	Raman spectroscopy was used to differentiate gray matter, viable GBM,  and necrosis in frozen specimens with 97.8% accuracy in samples  without freeze artifacts, and 77.5% of samples with freeze artifacts.
Tanahashi et al., 2014	Assessment of tumor cells in a mouse model of dif-  fuse infiltrative glioma by Raman spectroscopy	Principal component analysis was used to elucidate differences in the  spectra of infiltrative glioma and normal brain with 98.3% sensitivity  and 75% specificity.
Aguiar et al., 2013	Discriminating neoplastic and normal brain tissues  in vitro through Raman spectroscopy: a principal  components analysis classification model	Principal component analysis was able to discriminate normal tissue  from tumor, and glioblastoma from other CNS neoplasms, with a  sensitivity and specificity of 97.4% and 100%, respectively, in vitro.
Auner et al., 2013	Conclusions and data analysis: a 6-year study of  Raman spectroscopy of solid tumors at a major  pediatric institute	A database of Raman spectra from normal brain, kidney, and adrenal  gland, and their malignancies, was compiled. Leave-one-out analysis  predicted the presence of tumor with 85.5% accuracy in a test set not  assuming tissue origin.
Gajjar et al., 2012	Diagnostic segregation of human brain tumors  using Fourier-transform infrared and/or Raman  spectroscopy coupled with discriminant analysis	Raman spectroscopy was capable of identifying tumor-specific changes  in biochemical composition in formalin-fixed tumor samples.
Leslie et al., 2012	Identification of pediatric brain neoplasms using  Raman spectroscopy	A support vector machine analysis was used to identify Raman spectra  collected from various tumor subtypes and normal brain with ex  tremely high accuracy (91%-100%).
Zhou et al., 2012	Human brain cancer studied by resonance Raman  spectroscopy	Several specific molecular signatures were identified that distinguished  the spectra of normal meningeal tissues from several primary and  secondary brain neoplasms, with a sensitivity of 90.9% and specific  ity of 100% when principal component analysis was employed.
Beljebbar et al., 2010	Ex vivo and in vivo diagnosis of C6 glioblastoma  development by Raman spectroscopy coupled  to a microprobe	Employed Raman spectra collected from ex vivo mouse tissue to  differentiate normal tissue from tumor with 100% accuracy, and to  delineate early from mature tumor tissue.
Kirsch et al., 2010	Raman spectroscopic imaging for in vivo detection  of cerebral brain metastases	Demonstrates the first use of in vivo Raman spectral mapping of the  brain surface to aid tumor resection in a mouse model.
Köhler et al., 2009	Characterization of lipid extracts from brain tissue  and tumors using Raman spectroscopy and  mass spectrometry	Demonstrated increased water and decreased lipid content in glioma  versus healthy brain tissues in porcine and human samples, con  firmed with mass spectroscopy.
Krafft et al., 2009	Disease recognition by infrared and Raman  spectroscopy	Reviewed Raman spectroscopy applications for assessment of numer  ous tissues and body fluids, as well as classification and supervised  learning algorithms commonly used in analysis of Raman spectra.
Koljenović et al., 2005	Tissue characterization using high wave number  Raman spectroscopy	Established that comparatively diagnostic information can be gleaned  from high wave number and low wave number portions of the Raman  spectrum from brain and bladder cancer samples in vitro.
Krafft et al., 2005	Near infrared Raman spectra of human brain lipids	Demonstrated Raman spectral characteristics of 12 major brain lipids.
Hyperspectral Raman microscopy
Kast et al., 2015	Identification of regions of normal grey matter  and white matter from pathologic glioblastoma  and necrosis in frozen sections using Raman  imaging	Raman spectra acquired grid-wise across a frozen section of brain  tumor differentiated gray matter, white matter, tumor, and necrosis  through molecular features.
Kast et al., 2014	Raman molecular imaging of brain frozen tissue  sections	Frozen sections of brain tissue were mapped using grid-wise acquisition  of Raman spectra, identifying boundaries of gray and white matter,  necrosis, GBM, and infiltrating tumor.
Bergner et al., 2013	Hyperspectral unmixing of Raman micro-images  for assessment of morphological and chemical  parameters in non-dried brain tumor specimens	Both nuclear morphological characteristics and chemical composition  as defined by hyperspectral Raman imaging may offer new ways to  classify brain tumors.
Bergner et al., 2012	Unsupervised unmixing of Raman microspectro-  scopic images for morphochemical analysis of  non-dried brain tumor specimens	The hyperspectral unmixing algorithms N-FINDR and VCA were used to  map abundances of cholesterol, cholesterol ester, nucleic acids, caro  tene, proteins, and lipids in normal brain and several tumor subtypes  based on hyperspectral Raman micrographs.
Krafft et al., 2012	Advances in optical biopsy—correlation of malig  nancy and cell density of primary brain tumors  using Raman microspectroscopic imaging	Demonstrated increased nucleic acid bends in high-grade glioma  spectra, among other molecular differences correlating with structural  features on H & E microscopy.
Amharref et al., 2007	Discriminating healthy from tumor and necrosis  tissue in rat brain tissue samples by Raman  spectral imaging	Demonstrated that Raman microspectroscopy can discriminate between  healthy and tumoral brain tissue and yield spectroscopic markers as  sociated with the proliferative and invasive properties of glioblastoma  ex vivo.
Krafft et al., 2005	Near infrared Raman spectroscopic mapping of  native brain tissue and intracranial tumors	Initial exploration of Raman spectroscopic mapping of frozen samples of  brain tissue, meninges, and brain tumor, demonstrating measurable  spectroscopic and structural differences.
Koljenović et al., 2002	Discriminating vital tumor from necrotic tissue in  human glioblastoma tissue samples by Raman  spectroscopy	Utilized Raman spectral maps of frozen tumor sections to differentiate  viable from necrotic tumor via cluster analysis.

CNS = central nervous system; GBM = glioblastoma multiforme; VCA = vertex component analysis.