Introduction to Spectral Imaging, and Applications to Diagnosis of Lymph Nodes

Romeo, Melissa; Dukor, Rina K.; Diem, Max

doi:10.1002/0470027320.s8923

Cited by 9 publications

(9 citation statements)

References 85 publications

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“…Multivariate methods, in contrast, utilize the entire spectral vectors to create images from the hyperspectral datasets. One commonly used multivariate method is unsupervised hierarchical cluster analysis (HCA) [21,56] which calculates the similarity of all spectra in a dataset, and assigns color codes to spectral groups, or clusters, based on their similarity. Figure 5 shows such an HCA-based spectral image and, for comparison, an H & E-stained image of the same tissue section (see Section 3.5).…”

Section: Pre-sorting (Cluster Analysis)mentioning

confidence: 99%

“…Such spatially resolved spectral data can be collected by carrying out either IR or Raman measurements through a microscope [21]. For Raman microscopy, the instrumentation is very similar to confocal fluorescence microscopy, and utilizes a basic visible microscope to which a laser source and a monochromator for the analysis of the scattered light have been added.…”

Section: Introductionmentioning

confidence: 99%

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Molecular pathology via IR and Raman spectral imaging

Diem¹,

Mazur²,

Lenau³

et al. 2013

Journal of Biophotonics

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115

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Journal of BIOPHOTONICSDuring the last 15 years, vibrational spectroscopic methods have been developed that can be viewed as molecular pathology methods that depend on sampling the entire genome, proteome and metabolome of cells and tissues, rather than probing for the presence of selected markers. First, this review introduces the background and fundamentals of the spectroscopies underlying the new methodologies, namely infrared and Raman spectroscopy. Then, results are presented in the context of spectral histopathology of tissues for detection of metastases in lymph nodes, squamous cell carcinoma, adenocarcinomas, brain tumors and brain metastases. Results from spectral cytopathology of cells are discussed for screening of oral and cervical mucosa, and circulating tumor cells. It is concluded that infrared and Raman spectroscopy can complement histopathology and reveal information that is available in classical methods only by costly and time-consuming steps such as immunohistochemistry, polymerase chain reaction or gene arrays. Due to the inherent sensitivity toward changes in the bio-molecular composition of different cell and tissue types, vibrational spectroscopy can even provide information that is in some cases superior to that of any one of the conventional techniques.Schematic of spectral histopathology (SHP) process: diagnostic algorithm is developed by spectral data of well documented specimens and subsequently applied to unknown dataset.

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Section: Pre-sorting (Cluster Analysis)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Molecular pathology via IR and Raman spectral imaging

Diem¹,

Mazur²,

Lenau³

et al. 2013

Journal of Biophotonics

Self Cite

177

115

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“…Thus, for a 1 mm Â 1 mm tissue section, 25 600 individual IR spectra are collected, and stored as a 'hyperspectral data cube', a construct that contains the pixel coordinates and the associated spectrum. 1 This hyperspectral data cube contains the spatial variation of the sample composition, and hereby the sample diagnosis, encoded in the IR spectra.…”

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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

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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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“…Each 1 × 1 mm infrared image therefore consists of 160 × 160 or 25,600 spectra. The spatial resolution of the microscope was calibrated using a special air force resolution target deposited on a low-e slide over a layer of polystyrene, and was found to be 12 μm at 1600 cm -1 [5]. …”

Section: Methodsmentioning

confidence: 99%

Spectral detection of micro‐metastases in lymph node histo‐pathology

Bird

Romeo

Laver

et al. 2009

Journal of Biophotonics

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The first detection of breast cancer micrometastases in lymph nodes by infrared spectral imaging and methods of multivariate analysis is reported. Micrometastases are indicators of early spread of cancer from the organ originally affected by disease, and their detection is of prime importance for the staging and treatment of cancer. Infrared spectral imaging, at a spatial resolution of ca. 10–12 μm, can detect small metastases down to the level of a few cancerous cells. The results presented here add to a rapidly growing database of infrared spectral imaging results for cancer diagnostics. Microscopic image of a stained lymph node section, and a 5-cluster pseudo-color infrared image obtained from same section. The blue areas co-localize with the micro-metastases apparent in left Panel.

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Introduction to Spectral Imaging, and Applications to Diagnosis of Lymph Nodes

Cited by 9 publications

References 85 publications

Molecular pathology via IR and Raman spectral imaging

Molecular pathology via IR and Raman spectral imaging

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

Spectral detection of micro‐metastases in lymph node histo‐pathology

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