The potential for histological screening using a combination of rapid Raman mapping and principal component analysis

Hutchings, Joanne; Kendall, Catherine; Smith, Brian J.; Shepherd, Neil A.; Barr, Hugh; Stone, Nick

doi:10.1002/jbio.200810070

Cited by 54 publications

(55 citation statements)

References 31 publications

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“…a Raman map. Recent studies have demonstrated mapping of fresh tissue sections using unsupervised multivariate analysis methods such as K-means (KM) clustering analysis [5][6][7][8][9], hierarchical cluster analysis (HCA) [6] and principal component analysis (PCA) [10]. However there are no published studies to date that apply Raman mapping approaches to de-paraffinized tissue.…”

Section: Biophotonicsmentioning

confidence: 98%

Discrimination of normal from pre‐malignant cervical tissue by Raman mapping of de‐paraffinized histological tissue sections

Tan¹,

Herrington

Brown

2010

Journal of Biophotonics

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The authors present Raman cluster mapping of de-paraffinized normal cervical tissue and demonstrate the ability of this approach to differentiate between normal squamous epithelium and cervical intraepithelial neoplasia (CIN). Multivariate analysis was performed by hierarchical cluster analysis (HCA) of the Raman spectra associated with the different tissue types and Raman maps were generated using the resultant clusters. Using normal cervical tissue, squamous epithelium and the epithelial-stromal interface, a muscular artery and endocervical glands were successfully mapped. Analysis of a tissue section containing a cervical intraepithelial neoplasia (CIN) grade 2 lesion adjacent to normal squamous epithelium demonstrated that the CIN lesion clustered predominantly with the basal epithelial cells of normal epithelium and allowed visual discrimination of these areas using the Raman map. These findings suggest that Raman mapping has the potential to provide images that are useful for disease diagnosis. In particular, the discrimination between normal cervical squamous epithelium and CIN is of relevance to cervical screening pathology.

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Section: Biophotonicsmentioning

confidence: 98%

Discrimination of normal from pre‐malignant cervical tissue by Raman mapping of de‐paraffinized histological tissue sections

Tan¹,

Herrington

Brown

2010

Journal of Biophotonics

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“…Additionally, curve fitting was used in these studies to detect the changes in the main peaks' intensity in relation to a reference peak. Recently, principal component analysis was shown to improve dramatically the signal-to-noise ratio of noisy spectra from large data sets [Hutchings et al, 2009], but becomes computationally impractical if the data set is too large. However, due to the direct relationship between the solution of k-mean clustering and principal component analysis [Ding and He, 2004], k-mean clustering offered the same level of analytical power of a large data set but with vastly superior computational speed.…”

Section: Discussionmentioning

confidence: 99%

Microbiochemical Analysis of Carious Dentine Using Raman and Fluorescence Spectroscopy

et al. 2012

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The aim of this study was to evaluate and correlate objectively the microspectroscopically derived biochemical components of sound, infected and affected carious dentine with their microhardness and autofluorescence (AF) characteristics. Over 3 million high-resolution Raman spectra from 8 extracted human carious teeth were recorded using Raman spectrometer with parallel spectrum acquisition. Green AF signals across each carious lesion from all samples were acquired with a similar spatial resolution using confocal fluorescence microscopy. The Knoop microhardness (KHN) from a total of 233 co-localized areas was recorded from the same samples and allocated subjectively into the three zones. Cluster analysis of the Raman data, performed using in-house software, produced five independent spectral components representing mineral content, protein content, porphyrin fluorescence (PF), putative infected dentine signal (IDS) and affected dentine signal (ADS). The distributions of the 5 Raman components and the AF signal were matched across all samples and their average values were calculated for each corresponding KHN area. The infected dentine was defined significantly by the KHN, AF and by the relative contribution of the mineral, PF and IDS clusters. Protein cluster was not statistically related to the KHN or AF. A delineation between affected and sound dentine was observed using the KHN, AF, PF and ADS parameters. This study concludes that micro-Raman spectroscopy can provide a non-invasive and objective evaluation of different carious dentine zones. Being able to detect and assess clinically the caries-affected dentine during minimally invasive operative caries management is important to control the risk of unnecessary tissue removal.

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“…9,24 In addition, similar efforts using Raman spectroscopy, another form of vibrational spectroscopy, have yielded analogous results. [25][26][27][28] These studies mostly were aimed at demonstrating that vibrational (IR and Raman) spectroscopy can detect spectral differences between normal tissue types, and between normal and diseased tissue. However, the data sets were generally restricted in size such that rigorous statistical validation was impossible, or used data analysis procedures that were not completely objective.…”

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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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The potential for histological screening using a combination of rapid Raman mapping and principal component analysis

Cited by 54 publications

References 31 publications

Discrimination of normal from pre‐malignant cervical tissue by Raman mapping of de‐paraffinized histological tissue sections

Discrimination of normal from pre‐malignant cervical tissue by Raman mapping of de‐paraffinized histological tissue sections

Microbiochemical Analysis of Carious Dentine Using Raman and Fluorescence Spectroscopy

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

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