Brain tissue characterisation by infrared imaging in a rat glioma model

Amharref, Nadia; Beljebbar, A.; Dukic, Sylvain; Ventéo, Lydie; Schneider, Laurence; Pluot, M; Vistelle, Richard; Manfait, Michel

doi:10.1016/j.bbamem.2006.05.003

Cited by 57 publications

(45 citation statements)

References 21 publications

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“…The K-means cluster analysis is often applied to segment IR and Raman spectra directly [26]. But scores of PCA-transformed IR [35] and Raman data sets [36] can also be used as input.…”

Section: Cluster Analysismentioning

confidence: 99%

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Disease recognition by infrared and Raman spectroscopy

Krafft¹,

Steiner²,

Beleites³

et al. 2009

Journal of Biophotonics

258

175

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Infrared (IR) and Raman spectroscopy are emerging biophotonic tools to recognize various diseases. The current review gives an overview of the experimental techniques, data-classification algorithms and applications to assess soft tissues, hard tissues and body fluids. The methodology section presents the principles to combine vibrational spectroscopy with microscopy, lateral information and fiber-optic probes. A crucial step is the classification of spectral data by a variety of algorithms. We discuss unsupervised algorithms such as cluster analysis or principal component analysis and supervised algorithms such as linear discriminant analysis, soft independent modeling of class analogies, artificial neural networks support vector machines, Bayesian classification, partial least-squares regression and ensemble methods. The selected topics include tumors of epithelial tissue, brain tumors, prion diseases, bone diseases, atherosclerosis, kidney stones and gallstones, skin tumors, diabetes and osteoarthritis.

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“…The K-means cluster analysis is often applied to segment IR and Raman spectra directly [26]. But scores of PCA-transformed IR [35] and Raman data sets [36] can also be used as input.…”

Section: Cluster Analysismentioning

confidence: 99%

“…Tissue samples from these animal models were studied by IR spectroscopy [35,64] and Raman spectroscopy [26,36]. Raman images were collected from brain-tissue blocks using a fiberoptic probe and a motorized stage [26].…”

Section: Brain Tumors and Neurodegenerative Diseasesmentioning

confidence: 99%

Disease recognition by infrared and Raman spectroscopy

Krafft¹,

Steiner²,

Beleites³

et al. 2009

Journal of Biophotonics

258

175

View full text Add to dashboard Cite

show abstract

“…Particularly, Fourier transform infrared (FTIR) microspectroscopy has been found to be a technique that is sensitive to molecular composition and molecular conformational changes associated either to administration of drugs or to pathological development, which is not easily detectable by morphological methods. 18 The spatial resolution of this technique offers the possibility of detecting very small amounts of compounds that can be…”

Section: Introductionmentioning

confidence: 99%

FTIR microspectroscopic analysis of the effects of certain drugs on oxidative stress and brain protein structure

et al. 2008

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This study reports the changes in lipids and proteins of different brain areas of nicotine, D(+)‐amphetamine, and nicotine and D(+)‐amphetamine treated rats by monitoring lipid peroxidation and protein β‐sheet formation using infrared microspectroscopy. Compared with the untreated brain samples, the peroxide level is relatively higher in the amphetamine‐treated brain sections, both in the cortex and hippocampus area. However, this peroxide increase is attenuated when administering amphetamine plus nicotine. Analogous drug‐dependent trends for protein β‐sheet content are observed, which suggests a connection between lipid oxidation involved in oxidative stress and β‐sheet protein structure generally present in neurodegenerative diseases. The above property of nicotine is of interest, in the sense that it might reduce the production of β‐amyloid proteins in Alzheimer's disease. © 2008 Wiley Periodicals, Inc. Biopolymers 89: 548–554, 2008.This article was originally published online as an accepted preprint. The (Published Online) date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com

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“…These studies were preceded by earlier work from the authors' laboratory on the detection of micrometastases in lymph nodes, 12-14 cervical adeno-and SqCCs, 15 and from other laboratories on the detection of colon cancer, 11,[16][17][18] prostate and breast cancer, 19,20 brain cancers and brain metastases, [21][22][23] as well as a few other organs. 9,24 In addition, similar efforts using Raman spectroscopy, another form of vibrational spectroscopy, have yielded analogous results.…”

mentioning

confidence: 99%

Infrared spectral histopathology (SHP): a novel diagnostic tool for the accurate classification of lung cancer

Bird

Miljković

Remiszewski

et al. 2012

Laboratory Investigation

119

126

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We report results of a study utilizing a recently developed tissue diagnostic method, based on label-free spectral techniques, for the classification of lung cancer histopathological samples from a tissue microarray. The spectral diagnostic method allows reproducible and objective diagnosis of unstained tissue sections. This is accomplished by acquiring infrared hyperspectral data sets containing thousands of spectra, each collected from tissue pixels about 6 mm on edge; these pixel spectra contain an encoded snapshot of the entire biochemical composition of the pixel area. The hyperspectral data sets are subsequently decoded by methods of multivariate analysis, which reveal changes in the biochemical composition between tissue types, and between various stages and states of disease. In this study, a detailed comparison between classical and spectral histopathology (SHP) is presented, which suggests SHP can achieve levels of diagnostic accuracy that is comparable to that of multi-panel immunohistochemistry. KEYWORDS: artificial neural network analysis; histopathology; immunohistochemistry; lung cancer; spectral histopathology This paper reports a large-scale study of a new technology to classify four common forms of lung cancers, and distinguish them from normal tissues. The new methodology introduced here utilizes optical measurements on unstained tissue 1 for spectral data acquisition, and does not utilize any immunohistochemical or other stains or labels for classification. As the diagnostic procedure is instrument based and utilizes trained computer algorithms for classification, this method offers reproducibility, complete objectivity and improved accuracy over present methodology.Optical methods have been used in histology and pathology ever since these methods were first described. After all, staining tissues or cells by hematoxylin/eosin (H&E), followed by (visual) microscopic examination is a form of spectral analysis: different compartments of the cell respond differently to basophilic and eosinophilic stains and thus, allow a 'spectral analysis' using the eye as a detector. This method can reveal an amazing amount of information but is inherently subjective. More recent optical methods have used image capture at a few selected wavelengths, and computer analysis of the image planes, for tissue analysis. 2 Immunohistochemistry (IHC), to date the most advanced optical method to detect the presence of certain cancer signatures or markers 3,4 uses detection of specific antibodies labeled with easily observable stains.The new approach reported here is based on the observation of inherent spectral signatures (as opposed to any external stains or labels used to treat the sample) of cellular components to aid classical cytopathology and histopathology. 5 The paradigm for the spectral approach is that the transition from normal tissue or cells to diseased states is accompanied by changes in the overall biochemical composition of the tissue, along with well-known changes in cellular morphology and tissue archite...

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Brain tissue characterisation by infrared imaging in a rat glioma model

Cited by 57 publications

References 21 publications

Disease recognition by infrared and Raman spectroscopy

Disease recognition by infrared and Raman spectroscopy

FTIR microspectroscopic analysis of the effects of certain drugs on oxidative stress and brain protein structure

Infrared spectral histopathology (SHP): a novel diagnostic tool for the accurate classification of lung cancer

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