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FTIR-based spectroscopic analysis in the identification of clinically aggressive prostate cancer.

Baker MJ, Gazi E, Brown MD, Shanks JH, Gardner P, Clarke NW - Br. J. Cancer (2008)

Bottom Line: Principal component-discriminant function analysis improved the sensitivity and specificity of a three-band Gleason score criterion diagnosis previously reported by attaining an overall sensitivity of 92.3% and specificity of 99.4%.For the first time, we present the use of a two-band criterion showing an association of FTIR-based spectral characteristics with clinically aggressive behaviour in CaP manifest as local and/or distal spread.This paper shows the potential for the use of spectroscopic analysis for the evaluation of the biopotential of CaP in an accurate and reproducible manner.

View Article: PubMed Central - PubMed

Affiliation: Manchester Interdisciplinary Biocentre, Centre for Instrumentation and Analytical Science, School of Chemical Engineering and Analytical Science, The University of Manchester, Manchester, M1 7DN, UK. M.J.Baker@manchester.ac.uk

ABSTRACT
Fourier transform infrared (FTIR) spectroscopy is a vibrational spectroscopic technique that uses infrared radiation to vibrate molecular bonds within the sample that absorbs it. As different samples contain different molecular bonds or different configurations of molecular bonds, FTIR allows us to obtain chemical information on molecules within the sample. Fourier transform infrared microspectroscopy in conjunction with a principal component-discriminant function analysis (PC-DFA) algorithm was applied to the grading of prostate cancer (CaP) tissue specimens. The PC-DFA algorithm is used alongside the established diagnostic measures of Gleason grading and the tumour/node/metastasis system. Principal component-discriminant function analysis improved the sensitivity and specificity of a three-band Gleason score criterion diagnosis previously reported by attaining an overall sensitivity of 92.3% and specificity of 99.4%. For the first time, we present the use of a two-band criterion showing an association of FTIR-based spectral characteristics with clinically aggressive behaviour in CaP manifest as local and/or distal spread. This paper shows the potential for the use of spectroscopic analysis for the evaluation of the biopotential of CaP in an accurate and reproducible manner.

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Raw spectral data (A) and the effect of three pre-processing steps above on an example subset of the FTIR data with the variable CO2 region removed. (B) Vector normalisation (model A), (C) vector normalisation combined with first derivatisation (model B) and (D) vector normalisation combined with second derivatisation (model C).
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fig1: Raw spectral data (A) and the effect of three pre-processing steps above on an example subset of the FTIR data with the variable CO2 region removed. (B) Vector normalisation (model A), (C) vector normalisation combined with first derivatisation (model B) and (D) vector normalisation combined with second derivatisation (model C).

Mentions: Data processing was carried out immediately after spectral acquisition using OMNIC v.5.1a software. OMNIC software was used to convert the FTIR absorbance spectra into first derivatives (Savitzky–Golay algorithm, nine smoothing points) and second derivatives. The spectral region used was 750.1859–3999.706 cm−1 with the variable CO2 region from 1847.501 to 2809.22 cm−1 removed. This resulted in 1188 spectral data points for analysis. The spectra were then vector normalised in Matlab™, to correct for baseline shifts (scattering). Three different pre-processing procedures were used on raw spectra and assessed for optimum discrimination; these steps were (1) vector normalisation (model A), (2) vector normalisation combined with first derivatisation (model B) and (3) vector normalisation combined with second derivatisation (model C). Figure 1 shows raw spectral data and the effect of three pre-processing steps above on an example subset of the FTIR data with the variable CO2 region removed. Matlab coupled with an in-house written software was used to perform PC-DFA. Principal component-discriminant function analysis uses principal component analysis to reduce the dimensionality of the data before DFA. Discriminant function analysis then discriminates between groups on the basis of the resulting PCs and the a priori knowledge of the group memberships that are fed into the DFA algorithm (Baker et al, 2008). Maximising the inter-group variance and minimising the intra-group variance achieve this. The maximum number of discriminant functions available is the number of groups minus one. The optimum number of PCs was determined through an iterative process of chemometric model generation and validation. Discriminant function analysis is a supervised technique and the model is supplied with information about group membership, so any result produced by the model needs to be tested. This testing was carried out by retaining one-fifth of the total filtered pre-processed spectra, randomly selected as an independent test set, and then supplying the spectra to the model as a test set and observing where the model places the spectra on a graphical output. Error ellipses with 95 and 90% confidence are added to the discriminant function plots. This was achieved using error_ellipse.m written by AJ Johnson and obtained from Matlab central file exchange. Covariance matrices were calculated from the discriminant function analysis score matrix for each grouping, where the centroid was defined as the mean of the discriminant function analysis score matrix for each grouping.


FTIR-based spectroscopic analysis in the identification of clinically aggressive prostate cancer.

Baker MJ, Gazi E, Brown MD, Shanks JH, Gardner P, Clarke NW - Br. J. Cancer (2008)

Raw spectral data (A) and the effect of three pre-processing steps above on an example subset of the FTIR data with the variable CO2 region removed. (B) Vector normalisation (model A), (C) vector normalisation combined with first derivatisation (model B) and (D) vector normalisation combined with second derivatisation (model C).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2600682&req=5

fig1: Raw spectral data (A) and the effect of three pre-processing steps above on an example subset of the FTIR data with the variable CO2 region removed. (B) Vector normalisation (model A), (C) vector normalisation combined with first derivatisation (model B) and (D) vector normalisation combined with second derivatisation (model C).
Mentions: Data processing was carried out immediately after spectral acquisition using OMNIC v.5.1a software. OMNIC software was used to convert the FTIR absorbance spectra into first derivatives (Savitzky–Golay algorithm, nine smoothing points) and second derivatives. The spectral region used was 750.1859–3999.706 cm−1 with the variable CO2 region from 1847.501 to 2809.22 cm−1 removed. This resulted in 1188 spectral data points for analysis. The spectra were then vector normalised in Matlab™, to correct for baseline shifts (scattering). Three different pre-processing procedures were used on raw spectra and assessed for optimum discrimination; these steps were (1) vector normalisation (model A), (2) vector normalisation combined with first derivatisation (model B) and (3) vector normalisation combined with second derivatisation (model C). Figure 1 shows raw spectral data and the effect of three pre-processing steps above on an example subset of the FTIR data with the variable CO2 region removed. Matlab coupled with an in-house written software was used to perform PC-DFA. Principal component-discriminant function analysis uses principal component analysis to reduce the dimensionality of the data before DFA. Discriminant function analysis then discriminates between groups on the basis of the resulting PCs and the a priori knowledge of the group memberships that are fed into the DFA algorithm (Baker et al, 2008). Maximising the inter-group variance and minimising the intra-group variance achieve this. The maximum number of discriminant functions available is the number of groups minus one. The optimum number of PCs was determined through an iterative process of chemometric model generation and validation. Discriminant function analysis is a supervised technique and the model is supplied with information about group membership, so any result produced by the model needs to be tested. This testing was carried out by retaining one-fifth of the total filtered pre-processed spectra, randomly selected as an independent test set, and then supplying the spectra to the model as a test set and observing where the model places the spectra on a graphical output. Error ellipses with 95 and 90% confidence are added to the discriminant function plots. This was achieved using error_ellipse.m written by AJ Johnson and obtained from Matlab central file exchange. Covariance matrices were calculated from the discriminant function analysis score matrix for each grouping, where the centroid was defined as the mean of the discriminant function analysis score matrix for each grouping.

Bottom Line: Principal component-discriminant function analysis improved the sensitivity and specificity of a three-band Gleason score criterion diagnosis previously reported by attaining an overall sensitivity of 92.3% and specificity of 99.4%.For the first time, we present the use of a two-band criterion showing an association of FTIR-based spectral characteristics with clinically aggressive behaviour in CaP manifest as local and/or distal spread.This paper shows the potential for the use of spectroscopic analysis for the evaluation of the biopotential of CaP in an accurate and reproducible manner.

View Article: PubMed Central - PubMed

Affiliation: Manchester Interdisciplinary Biocentre, Centre for Instrumentation and Analytical Science, School of Chemical Engineering and Analytical Science, The University of Manchester, Manchester, M1 7DN, UK. M.J.Baker@manchester.ac.uk

ABSTRACT
Fourier transform infrared (FTIR) spectroscopy is a vibrational spectroscopic technique that uses infrared radiation to vibrate molecular bonds within the sample that absorbs it. As different samples contain different molecular bonds or different configurations of molecular bonds, FTIR allows us to obtain chemical information on molecules within the sample. Fourier transform infrared microspectroscopy in conjunction with a principal component-discriminant function analysis (PC-DFA) algorithm was applied to the grading of prostate cancer (CaP) tissue specimens. The PC-DFA algorithm is used alongside the established diagnostic measures of Gleason grading and the tumour/node/metastasis system. Principal component-discriminant function analysis improved the sensitivity and specificity of a three-band Gleason score criterion diagnosis previously reported by attaining an overall sensitivity of 92.3% and specificity of 99.4%. For the first time, we present the use of a two-band criterion showing an association of FTIR-based spectral characteristics with clinically aggressive behaviour in CaP manifest as local and/or distal spread. This paper shows the potential for the use of spectroscopic analysis for the evaluation of the biopotential of CaP in an accurate and reproducible manner.

Show MeSH
Related in: MedlinePlus