Raman spectroscopy to distinguish grey matter, necrosis, and glioblastoma multiforme in frozen tissue sections

Steven N. Kalkanis, Rachel E. Kast, Mark L. Rosenblum, Tom Mikkelsen, Sally M. Yurgelevic, Katrina M. Nelson, Aditya Raghunathan, Laila M. Poisson, Gregory W. Auner

Research output: Contribution to journalArticle

48 Citations (Scopus)

Abstract

The need exists for a highly accurate, efficient and inexpensive tool to distinguish normal brain tissue from glioblastoma multiforme (GBM) and necrosis boundaries rapidly, in real-time, in the operating room. Raman spectroscopy provides a unique biochemical signature of a tissue type, with the potential to provide intraoperative identification of tumor and necrosis boundaries. We aimed to develop a database of Raman spectra from normal brain, GBM, and necrosis, and a methodology for distinguishing these pathologies. Raman spectroscopy was used to measure 95 regions from 40 frozen tissue sections using 785 nm excitation wavelength. Review of adjacent hematoxylin and eosin sections confirmed histology of each region. Three regions each of normal grey matter, necrosis, and GBM were selected as a training set. Ten regions were selected as a validation set, with a secondary validation set of tissue regions containing freeze artifact. Grey matter contained higher lipid (1061, 1081 cm-1) content, whereas necrosis revealed increased protein and nucleic acid content (1003, 1206, 1239, 1255-1266, 1552 cm-1). GBM fell between these two extremes. Discriminant function analysis showed 99.6, 97.8, and 77.5 % accuracy in distinguishing tissue types in the training, validation, and validation with freeze artifact datasets, respectively. Decreased classification in the freeze artifact group was due to tissue preparation damage. This study shows the potential of Raman spectroscopy to accurately identify normal brain, necrosis, and GBM as a tool to augment pathologic diagnosis. Future work will develop mapped images of diffuse glioma and neoplastic margins toward development of an intraoperative surgical tool.

Original languageEnglish (US)
Pages (from-to)477-485
Number of pages9
JournalJournal of Neuro-Oncology
Volume116
Issue number3
DOIs
StatePublished - Feb 1 2014
Externally publishedYes

Fingerprint

Raman Spectrum Analysis
Frozen Sections
Glioblastoma
Necrosis
Artifacts
Brain
Discriminant Analysis
Operating Rooms
Hematoxylin
Eosine Yellowish-(YS)
Glioma
Nucleic Acids
Gray Matter
Histology
Databases
Pathology
Lipids
Neoplasms
Proteins

Keywords

  • Glioblastoma
  • In vivo
  • Necrosis
  • Raman spectroscopy

ASJC Scopus subject areas

  • Oncology
  • Neurology
  • Clinical Neurology
  • Cancer Research

Cite this

Kalkanis, S. N., Kast, R. E., Rosenblum, M. L., Mikkelsen, T., Yurgelevic, S. M., Nelson, K. M., ... Auner, G. W. (2014). Raman spectroscopy to distinguish grey matter, necrosis, and glioblastoma multiforme in frozen tissue sections. Journal of Neuro-Oncology, 116(3), 477-485. https://doi.org/10.1007/s11060-013-1326-9

Raman spectroscopy to distinguish grey matter, necrosis, and glioblastoma multiforme in frozen tissue sections. / Kalkanis, Steven N.; Kast, Rachel E.; Rosenblum, Mark L.; Mikkelsen, Tom; Yurgelevic, Sally M.; Nelson, Katrina M.; Raghunathan, Aditya; Poisson, Laila M.; Auner, Gregory W.

In: Journal of Neuro-Oncology, Vol. 116, No. 3, 01.02.2014, p. 477-485.

Research output: Contribution to journalArticle

Kalkanis, SN, Kast, RE, Rosenblum, ML, Mikkelsen, T, Yurgelevic, SM, Nelson, KM, Raghunathan, A, Poisson, LM & Auner, GW 2014, 'Raman spectroscopy to distinguish grey matter, necrosis, and glioblastoma multiforme in frozen tissue sections', Journal of Neuro-Oncology, vol. 116, no. 3, pp. 477-485. https://doi.org/10.1007/s11060-013-1326-9
Kalkanis, Steven N. ; Kast, Rachel E. ; Rosenblum, Mark L. ; Mikkelsen, Tom ; Yurgelevic, Sally M. ; Nelson, Katrina M. ; Raghunathan, Aditya ; Poisson, Laila M. ; Auner, Gregory W. / Raman spectroscopy to distinguish grey matter, necrosis, and glioblastoma multiforme in frozen tissue sections. In: Journal of Neuro-Oncology. 2014 ; Vol. 116, No. 3. pp. 477-485.
@article{274915699a6241e882d515a8f960d4d8,
title = "Raman spectroscopy to distinguish grey matter, necrosis, and glioblastoma multiforme in frozen tissue sections",
abstract = "The need exists for a highly accurate, efficient and inexpensive tool to distinguish normal brain tissue from glioblastoma multiforme (GBM) and necrosis boundaries rapidly, in real-time, in the operating room. Raman spectroscopy provides a unique biochemical signature of a tissue type, with the potential to provide intraoperative identification of tumor and necrosis boundaries. We aimed to develop a database of Raman spectra from normal brain, GBM, and necrosis, and a methodology for distinguishing these pathologies. Raman spectroscopy was used to measure 95 regions from 40 frozen tissue sections using 785 nm excitation wavelength. Review of adjacent hematoxylin and eosin sections confirmed histology of each region. Three regions each of normal grey matter, necrosis, and GBM were selected as a training set. Ten regions were selected as a validation set, with a secondary validation set of tissue regions containing freeze artifact. Grey matter contained higher lipid (1061, 1081 cm-1) content, whereas necrosis revealed increased protein and nucleic acid content (1003, 1206, 1239, 1255-1266, 1552 cm-1). GBM fell between these two extremes. Discriminant function analysis showed 99.6, 97.8, and 77.5 {\%} accuracy in distinguishing tissue types in the training, validation, and validation with freeze artifact datasets, respectively. Decreased classification in the freeze artifact group was due to tissue preparation damage. This study shows the potential of Raman spectroscopy to accurately identify normal brain, necrosis, and GBM as a tool to augment pathologic diagnosis. Future work will develop mapped images of diffuse glioma and neoplastic margins toward development of an intraoperative surgical tool.",
keywords = "Glioblastoma, In vivo, Necrosis, Raman spectroscopy",
author = "Kalkanis, {Steven N.} and Kast, {Rachel E.} and Rosenblum, {Mark L.} and Tom Mikkelsen and Yurgelevic, {Sally M.} and Nelson, {Katrina M.} and Aditya Raghunathan and Poisson, {Laila M.} and Auner, {Gregory W.}",
year = "2014",
month = "2",
day = "1",
doi = "10.1007/s11060-013-1326-9",
language = "English (US)",
volume = "116",
pages = "477--485",
journal = "Journal of Neuro-Oncology",
issn = "0167-594X",
publisher = "Kluwer Academic Publishers",
number = "3",

}

TY - JOUR

T1 - Raman spectroscopy to distinguish grey matter, necrosis, and glioblastoma multiforme in frozen tissue sections

AU - Kalkanis, Steven N.

AU - Kast, Rachel E.

AU - Rosenblum, Mark L.

AU - Mikkelsen, Tom

AU - Yurgelevic, Sally M.

AU - Nelson, Katrina M.

AU - Raghunathan, Aditya

AU - Poisson, Laila M.

AU - Auner, Gregory W.

PY - 2014/2/1

Y1 - 2014/2/1

N2 - The need exists for a highly accurate, efficient and inexpensive tool to distinguish normal brain tissue from glioblastoma multiforme (GBM) and necrosis boundaries rapidly, in real-time, in the operating room. Raman spectroscopy provides a unique biochemical signature of a tissue type, with the potential to provide intraoperative identification of tumor and necrosis boundaries. We aimed to develop a database of Raman spectra from normal brain, GBM, and necrosis, and a methodology for distinguishing these pathologies. Raman spectroscopy was used to measure 95 regions from 40 frozen tissue sections using 785 nm excitation wavelength. Review of adjacent hematoxylin and eosin sections confirmed histology of each region. Three regions each of normal grey matter, necrosis, and GBM were selected as a training set. Ten regions were selected as a validation set, with a secondary validation set of tissue regions containing freeze artifact. Grey matter contained higher lipid (1061, 1081 cm-1) content, whereas necrosis revealed increased protein and nucleic acid content (1003, 1206, 1239, 1255-1266, 1552 cm-1). GBM fell between these two extremes. Discriminant function analysis showed 99.6, 97.8, and 77.5 % accuracy in distinguishing tissue types in the training, validation, and validation with freeze artifact datasets, respectively. Decreased classification in the freeze artifact group was due to tissue preparation damage. This study shows the potential of Raman spectroscopy to accurately identify normal brain, necrosis, and GBM as a tool to augment pathologic diagnosis. Future work will develop mapped images of diffuse glioma and neoplastic margins toward development of an intraoperative surgical tool.

AB - The need exists for a highly accurate, efficient and inexpensive tool to distinguish normal brain tissue from glioblastoma multiforme (GBM) and necrosis boundaries rapidly, in real-time, in the operating room. Raman spectroscopy provides a unique biochemical signature of a tissue type, with the potential to provide intraoperative identification of tumor and necrosis boundaries. We aimed to develop a database of Raman spectra from normal brain, GBM, and necrosis, and a methodology for distinguishing these pathologies. Raman spectroscopy was used to measure 95 regions from 40 frozen tissue sections using 785 nm excitation wavelength. Review of adjacent hematoxylin and eosin sections confirmed histology of each region. Three regions each of normal grey matter, necrosis, and GBM were selected as a training set. Ten regions were selected as a validation set, with a secondary validation set of tissue regions containing freeze artifact. Grey matter contained higher lipid (1061, 1081 cm-1) content, whereas necrosis revealed increased protein and nucleic acid content (1003, 1206, 1239, 1255-1266, 1552 cm-1). GBM fell between these two extremes. Discriminant function analysis showed 99.6, 97.8, and 77.5 % accuracy in distinguishing tissue types in the training, validation, and validation with freeze artifact datasets, respectively. Decreased classification in the freeze artifact group was due to tissue preparation damage. This study shows the potential of Raman spectroscopy to accurately identify normal brain, necrosis, and GBM as a tool to augment pathologic diagnosis. Future work will develop mapped images of diffuse glioma and neoplastic margins toward development of an intraoperative surgical tool.

KW - Glioblastoma

KW - In vivo

KW - Necrosis

KW - Raman spectroscopy

UR - http://www.scopus.com/inward/record.url?scp=84894050372&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84894050372&partnerID=8YFLogxK

U2 - 10.1007/s11060-013-1326-9

DO - 10.1007/s11060-013-1326-9

M3 - Article

VL - 116

SP - 477

EP - 485

JO - Journal of Neuro-Oncology

JF - Journal of Neuro-Oncology

SN - 0167-594X

IS - 3

ER -