In vivo Raman spectral pathology of human atherosclerosis and vulnerable plaque

Motz, Jason T.; Fitzmaurice, Maryann; Miller, Arnold; Gandhi, Saumil; Haka, Abigail S.; Galindo, Luis H.; Dasari, Ramachandra R.; Kramer, John; Feld, Michael S.

doi:10.1117/1.2190967

Cited by 128 publications

(126 citation statements)

References 27 publications

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“…Here, we attribute the inaccuracies in classification (i.e., the ten false negatives from two cancerous sites) to spectroscopy-histopathology registration errors [29,30]. In other words, the spectroscopic measurements were potentially performed on a grossly normal site of the biopsied tissue whereas the histopathological results were obtained for a marginally different position on the tissue where cancerous cells could be observed.…”

Section: Resultsmentioning

confidence: 99%

Selective sampling using confocal Raman spectroscopy provides enhanced specificity for urinary bladder cancer diagnosis

et al. 2012

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In recent years, Raman spectroscopy has shown substantive promise in diagnosing bladder cancer, especially due to its exquisite molecular specificity. The ability to reduce false detection rates in comparison to existing diagnostic tools such as photodynamic diagnosis makes Raman spectroscopy particularly attractive as a complementary diagnostic tool for real-time guidance of transurethral resection of bladder tumor (TURBT). Nevertheless, the state-ofthe-art high-volume Raman spectroscopic probes have not reached the expected levels of specificity thereby impeding their clinical translation. To address this issue, we propose the use of a confocal Raman probe for bladder cancer diagnosis that can boost the specificity of the diagnostic algorithm based on its suppression of the out-of-focus nonanalyte-specific signals emanating from the neighboring normal tissue. In this article, we engineer and apply such a probe, having depth of field of approximately 280 μm, for Raman spectral acquisition from ex vivo normal and cancerous TURBT samples. Using this clinical dataset, a diagnostic algorithm based on principal component analysis and logistic regression is developed. We demonstrate that this approach results in comparable sensitivity but significantly higher specificity in relation to high-volume Raman spectral data. The application of only two principal components is sufficient for the discrimination of the samples underlining the robustness of the algorithm. Further, no discordance between replicate spectra is observed emphasizing the reproducible nature of the current diagnostic assessment. The high levels of sensitivity and specificity achieved in this proof-of-concept study opens substantive avenues for application of a confocal Raman probe during endoscopic procedures related to diagnosis and treatment of bladder cancer.

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Section: Resultsmentioning

confidence: 99%

Selective sampling using confocal Raman spectroscopy provides enhanced specificity for urinary bladder cancer diagnosis

et al. 2012

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“…This approach has been widely applied to determine the composition of biomolecules in cells and tissue by spontaneous Raman spectroscopy. 25,[42][43][44] The composition of the sample is, however, not always entirely known, and at times, it might be cumbersome or impossible to perform an a priori chemical analysis to determine the exact composition of the sample. Several methods have been developed that can extract the ''pure'' components of a sample by spectral unmixing, also widely known as BSS.…”

Section: Fig 2 (A)mentioning

confidence: 99%

Methods and Applications of Raman Microspectroscopy to Single-Cell Analysis

2013

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Raman spectroscopy is a powerful biochemical analysis technique that allows for the dynamic characterization and imaging of living biological cells in the absence of fluorescent stains. In this review, we summarize some of the most recent developments in the noninvasive biochemical characterization of single cells by spontaneous Raman scattering. Different instrumentation strategies utilizing confocal detection optics, multispot, and line illumination have been developed to improve the speed and sensitivity of the analysis of single cells by Raman spectroscopy. To analyze and visualize the large data sets obtained during such experiments, sophisticated multivariate statistical analysis tools are necessary to reduce the data and extract components of interest. We highlight the most recent applications of single cell analysis by Raman spectroscopy and their biomedical implications that have enabled the noninvasive characterization of specific metabolic states of eukaryotic cells, the identification and characterization of stem cells, and the rapid identification of bacterial cells. We conclude the article with a brief look into the future of this rapidly evolving research area.

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“…Feld and Motz have published one design that has been particularly successful ( Figure 2); it allows the researchers to characterize arterial plaques and breast cancer tumor margins, both during real surgeries (5,6). Their probe, which is 2 mm in diameter, consists of a delivery fiber surrounded by a ring of collection fibers (7 ).…”

Section: The Probe Problemmentioning

confidence: 99%

Raman Spectroscopy For Medical Diagnosis

Griffiths¹

2007

Anal. Chem.

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SPECTROSCOPY FOR MEDICAL DIAGNOSIS 3 9 7 6A N A LY T I C A L C H E M I S T R Y / J U N E 1 , 2 0 0 7 will be different. How different they are depends on which tissue types are being compared. Often the spectra of the two populations look very similar to the naked eye, and many research hours have been poured into mathematically deconvoluting and giving clinical meaning to those spectra.To set up a model for a tissue comparison, researchers might start with a mixture of tissue samples that have been classified (e.g., cancerous vs noncancerous) by the gold standard of histopathology. They would take spectra of all those samples and apply one of several statistical methods to extract the subtle spectroscopic differences between the two classes of tissue. These differences are written into an algorithm that can then be used to classify an unknown tissue sample.One of the hottest areas of research in tissue Raman is identifying cancerous versus noncancerous tissue from the brain, cervix, bladder, and other organs ( Figure 1). Most of the work to date has been ex vivo, but Raman researchers say that their ultimate goal is to move into the body. "The direct advantage of Raman is that you don't need to do anything with your tissue," says Gerwin Puppels of Erasmus University Medical Center (The Netherlands). "The only thing you do is shine light on the tissue and collect the light that comes back," he adds; this makes Raman nondestructive and safe to use on real patients.One application could be the use of Raman during surgery to identify the margins of a tumor. Today, surgeons often walk a fine line in deciding how much tissue to remove. Remove too much and they may damage the organ; remove too little and the tumor may recur, necessitating more surgery. A biopsy can take hours or days, whereas Raman could give an answer in seconds.It's not only soft tissue that can be classified by Raman spectroscopy. Bone and tooth enamel have relatively strong Raman signals, and both are receiving attention from scientists in the tissue Raman community. Currently, the risk of osteoporotic bone fracture is assessed by a technique called dual-energy X-ray absorptiometry (DXA), which measures absorption of X-rays by calcium. According to Michael Morris of the University of Michigan, only 50 -70% of the fracture risk is predicted by DXA. What DXA doesn't measure is "bone quality", a general term that includes the chemical makeup of bone.Morris and colleagues use Raman to detect the minerals and collagen fibrils that make up bone. "What we bring to the table are very clear and very unambiguous measurements of bone chemistry that include cross-linking [of collagen] and measures of mineral maturity," Morris says. They have found a clear correlation between bone chemistry and osteoporotic fracture risk ex vivo (2), and they say that they will be moving their studies in vivo shortly.Meanwhile, other researchers are interested in the oral health applications of Raman spectroscopy. "Dental is one area that has been overlooked quite a lot," says...

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In vivo Raman spectral pathology of human atherosclerosis and vulnerable plaque

Cited by 128 publications

References 27 publications

Selective sampling using confocal Raman spectroscopy provides enhanced specificity for urinary bladder cancer diagnosis

Selective sampling using confocal Raman spectroscopy provides enhanced specificity for urinary bladder cancer diagnosis

Methods and Applications of Raman Microspectroscopy to Single-Cell Analysis

Raman Spectroscopy For Medical Diagnosis

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