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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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(A) Discriminant function plot showing separation based on model A (vector-normalised model) upon the training set (red digits) and test set (blue digits) with 95% (blue ellipse) and 90% (green ellipse) confidence limits, where 1=T1 and T2 and 2=T3 and T4, and (B) loading plots for discriminant function 1. DFA – DF1 vs DF2.
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fig4: (A) Discriminant function plot showing separation based on model A (vector-normalised model) upon the training set (red digits) and test set (blue digits) with 95% (blue ellipse) and 90% (green ellipse) confidence limits, where 1=T1 and T2 and 2=T3 and T4, and (B) loading plots for discriminant function 1. DFA – DF1 vs DF2.

Mentions: The best-performing model is model A, which attained overall sensitivity and specificity of 91.2 and 93.0%. This model used 62 PCs and one discriminant function. Figure 4A shows the discriminant function plot based upon the training set (red digits) and test set (blue digits) with 95% (blue ellipse) and 90% (green ellipse) confidence limits, where 1=T1 and T2, and 2=T3 and T4. Figure 4B shows the loading plots for discriminant function 1, from which it is possible to obtain the peaks responsible for discrimination. As the model has only two groups, only one discriminant function is sufficient for the separation.


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)

(A) Discriminant function plot showing separation based on model A (vector-normalised model) upon the training set (red digits) and test set (blue digits) with 95% (blue ellipse) and 90% (green ellipse) confidence limits, where 1=T1 and T2 and 2=T3 and T4, and (B) loading plots for discriminant function 1. DFA – DF1 vs DF2.
© Copyright Policy
Related In: Results  -  Collection

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

fig4: (A) Discriminant function plot showing separation based on model A (vector-normalised model) upon the training set (red digits) and test set (blue digits) with 95% (blue ellipse) and 90% (green ellipse) confidence limits, where 1=T1 and T2 and 2=T3 and T4, and (B) loading plots for discriminant function 1. DFA – DF1 vs DF2.
Mentions: The best-performing model is model A, which attained overall sensitivity and specificity of 91.2 and 93.0%. This model used 62 PCs and one discriminant function. Figure 4A shows the discriminant function plot based upon the training set (red digits) and test set (blue digits) with 95% (blue ellipse) and 90% (green ellipse) confidence limits, where 1=T1 and T2, and 2=T3 and T4. Figure 4B shows the loading plots for discriminant function 1, from which it is possible to obtain the peaks responsible for discrimination. As the model has only two groups, only one discriminant function is sufficient for the separation.

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