Fourier transform infrared for noninvasive optical diagnosis of oral, oropharyngeal, and laryngeal cancer

Menzies, Georgina E.; Fox, Hannah; Marnane, Conor; Pope, Laysan; Prabhu, Vinod; Winter, Stuart C; Derrick, Anna V.; Lewis, Paul D.

doi:10.1016/j.trsl.2013.09.006

Cited by 36 publications

(31 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…16, [18][19][20][21] For initial analysis of the data, the Mahalanobis distance 15 was estimated and the data falling far away from this distance were considered as outliers. Approximately 10% of the whole dataset (from the original 270 Raman spectra and 270 DLIPS spectra) were dropped based on the outlier test, which is attributed to poor Raman SNR due to thin film regions, instabilities in laser power, film anomalies or impurities, or for the case of DLIPS, slight sample movement/ misalignment between the preperturbation and postperturbation Raman spectra, noting the rather high magnification (i.e., 50×).…”

Section: Data Processingmentioning

confidence: 99%

“…In addition, a PLS regression model was developed to further quantify the classification ability of the DLIPS and traditional Raman data sets. 17,18,29 For PLS analysis, 10 factors were used to build a model which together accounted for ∼99% of the total variance. Since the purpose was to directly compare the two datasets on PLS model quality, rather than dividing the data set into two halves for model development and validation, respectively, as is commonly done, 15 the entire datasets were used to evaluate the PLS model performance.…”

Section: Raman and Dlips Performance In Classificationmentioning

confidence: 99%

“…14,15 Still, the applicability outside of the training data and closely related samples is often quite limited and clinically unacceptable, as noted above. Other models based on multivariate statistics including principal component analysis (PCA), hierarchical cluster analysis (HCA), partial least squares (PLS), [15][16][17][18][19] and support vector machines, 14,20,21 which aid in the interpretation of spectral data, have been used for further discrimination of different sample types. These methods are specifically used for data processing and their success is highly dependent on the quality of the acquisition schemes and may not be effective on all types of spectral noise or with poor signal quality (i.e., low SNR).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Differential laser-induced perturbation Raman spectroscopy: a comparison with Raman spectroscopy for analysis and classification of amino acids and dipeptides

2015

View full text Add to dashboard Cite

Abstract. Differential-laser induced perturbation spectroscopy (DLIPS) is a new spectral analysis technique for classification and identification, with key potential applications for analysis of complex biomolecular systems. DLIPS takes advantage of the complex ultraviolet (UV) laser-material interactions based on difference spectroscopy by coupling low intensity UV laser perturbation with a traditional spectroscopy probe. Here, we quantify the DLIPS performance using a Raman scattering probe in classification of basic constituents of collagenous tissues, namely, the amino acids glycine, L-proline, and L-alanine, and the dipeptides glycine-glycine, glycinealanine and glycine-proline and compare the performance to a traditional Raman spectroscopy probe via several multivariate analyses. We find that the DLIPS approach yields an ∼40% improvement in discrimination among these tissue building blocks. The effects of the 193-nm perturbation laser are further examined by assessing the photodestruction of targeted material molecular bonds. The DLIPS method with a Raman probe holds promise for future tissue diagnosis, either as a stand-alone technique or as part of an orthogonal biosensing scheme. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

show abstract

Section: Data Processingmentioning

confidence: 99%

Section: Raman and Dlips Performance In Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Differential laser-induced perturbation Raman spectroscopy: a comparison with Raman spectroscopy for analysis and classification of amino acids and dipeptides

2015

View full text Add to dashboard Cite

show abstract

“…At the preprocessing step, all spectra were smoothed by second-order Savitzky-Golay polynomial filter using 15 points. This specific preprocessing method is commonly used at the onset of the multivariate analyses for spectroscopic techniques in biochemistry and in analytical chemistry field (42,(50)(51)(52)(53)(54). This preprocessing method does not have any effect on the signal contours, only smooths the noisy data often characteristic, for example, of intensified CCD systems.…”

Section: Performance Of Dlips Compared To Traditional Fluorescence Onmentioning

confidence: 99%

Differential Laser‐Induced Perturbation Spectroscopy for Analysis of Mixtures of the Fluorophores l‐Phenylalanine, l‐Tyrosine and l‐Tryptophan Using a Fluorescence Probe

Oztekin

Hahn

2016

Photochem & Photobiology

View full text Add to dashboard Cite

Quantitative detection of common endogenous fluorophores is accomplished using differential laser-induced perturbation spectroscopy (DLIPS) with a 193-nm UV fluorescence probe and various UV perturbation wavelengths. In this study, DLIPS is explored as an alternative to traditional fluorescence spectroscopy alone, with a goal of exploring natural fluorophores pursuant to biological samples and tissue analysis. To this end, aromatic amino acids, namely, l-phenylalanine, l-tyrosine and l-tryptophan are mixed with differing mass ratios and then classified with various DLIPS schemes. Classification with a traditional fluorescence probe is used as a benchmark. The results show a 20% improvement in classification performance of the DLIPS method over the traditional fluorescence method using partial least squares (PLS) analysis. Additional multivariate analyses are explored, and the relevant photochemistry is elucidated in the context of perturbation wavelengths. We conclude that DLIPS is a promising biosensing approach with potential for in vivo analysis given the current findings with fluorophores relevant to biological tissues.

show abstract

“…Yuvaraj et al characterized different salivary metabolites associated with oral cancers by fluorescence spectroscopy. Fourier‐transform infrared spectroscopy has been applied to study sputum in order to diagnose oral cancers, and discrimination between normal and cancerous samples was achieved on the basis of changes in the protein and glycoprotein structure within cells . Surface‐enhanced Raman spectroscopy methods have been used to differentiate between healthy subjects and patients with oral cancer using spectra acquired from saliva .…”

Section: Introductionmentioning

confidence: 99%

Recent advances in optical diagnosis of oral cancers: Review and future perspectives

Singh

Ibrahim

Byrne³

et al. 2015

Head & Neck

View full text Add to dashboard Cite

Optical diagnosis techniques offer several advantages over traditional approaches, including objectivity, speed, and cost, and these label-free, noninvasive methods have the potential to change the future workflow of cancer management. The oral cavity is particularly accessible and, thus, such methods may serve as alternate/adjunct tools to traditional methods. Recently, in vivo human clinical studies have been initiated with a view to clinical translation of such technologies. A comprehensive review of optical methods in oral cancer diagnosis is presented. After an introduction to the epidemiology and etiological factors associated with oral cancers currently used, diagnostic methods and their limitations are presented. A thorough review of fluorescence, infrared absorption, and Raman spectroscopic methods in oral cancer diagnosis is presented. The applicability of minimally invasive methods based on serum/saliva is also discussed. The review concludes with a discussion on future demands and scope of developments from a clinical point of view. © 2015 Wiley Periodicals, Inc. Head Neck 38: E2403-E2411, 2016.

show abstract

Fourier transform infrared for noninvasive optical diagnosis of oral, oropharyngeal, and laryngeal cancer

Cited by 36 publications

References 23 publications

Differential laser-induced perturbation Raman spectroscopy: a comparison with Raman spectroscopy for analysis and classification of amino acids and dipeptides

Differential laser-induced perturbation Raman spectroscopy: a comparison with Raman spectroscopy for analysis and classification of amino acids and dipeptides

Differential Laser‐Induced Perturbation Spectroscopy for Analysis of Mixtures of the Fluorophores l‐Phenylalanine, l‐Tyrosine and l‐Tryptophan Using a Fluorescence Probe

Recent advances in optical diagnosis of oral cancers: Review and future perspectives

Contact Info

Product

Resources

About