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Near-infrared spectroscopy as a diagnostic tool for distinguishing between normal and malignant colorectal tissues.

Chen H, Lin Z, Mo L, Wu T, Tan C - Biomed Res Int (2015)

Bottom Line: The successive projection algorithm-linear discriminant analysis (SPA-LDA) was used to seek a reduced subset of variables/wavenumbers and build a diagnostic model of LDA.The results showed that, compared to PLS-DA, SPA-LDA provided more parsimonious model using only three wavenumbers/variables (4065, 4173, and 5758 cm(-1)) to achieve the sensitivity of 84.6%, 92.3%, and 92.3% for the training, validation, and test sets, respectively, and the specificity of 100% for each subset.It indicated that the combination of NIR spectroscopy and SPA-LDA algorithm can serve as a potential tool for distinguishing between normal and malignant colorectal tissues.

View Article: PubMed Central - PubMed

Affiliation: Yibin University Hospital, Yibin, Sichuan 644000, China ; Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, China.

ABSTRACT
Cancer diagnosis is one of the most important tasks of biomedical research and has become the main objective of medical investigations. The present paper proposed an analytical strategy for distinguishing between normal and malignant colorectal tissues by combining the use of near-infrared (NIR) spectroscopy with chemometrics. The successive projection algorithm-linear discriminant analysis (SPA-LDA) was used to seek a reduced subset of variables/wavenumbers and build a diagnostic model of LDA. For comparison, the partial least squares-discriminant analysis (PLS-DA) based on full-spectrum classification was also used as the reference. Principal component analysis (PCA) was used for a preliminary analysis. A total of 186 spectra from 20 patients with partial colorectal resection were collected and divided into three subsets for training, optimizing, and testing the model. The results showed that, compared to PLS-DA, SPA-LDA provided more parsimonious model using only three wavenumbers/variables (4065, 4173, and 5758 cm(-1)) to achieve the sensitivity of 84.6%, 92.3%, and 92.3% for the training, validation, and test sets, respectively, and the specificity of 100% for each subset. It indicated that the combination of NIR spectroscopy and SPA-LDA algorithm can serve as a potential tool for distinguishing between normal and malignant colorectal tissues.

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Three-dimensional scatter plot of the first three principal components (PCs) and its 2-dimensional projection. The variance explained by each PC is indicated in parenthesis.
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fig3: Three-dimensional scatter plot of the first three principal components (PCs) and its 2-dimensional projection. The variance explained by each PC is indicated in parenthesis.

Mentions: Principal component analysis (PCA) was used to examine the possible clustering in samples and investigate the extent to which NIR features can differentiate cancerous and normal tissues. Figure 3 provided the three-dimensional scatter plot of the first three principal components (PCs) and its 2-dimensional projection. The first three PCs accounted for about 80% of the total variation in the NIR spectra. As can be seen in Figure 3, the separation was not clear and there existed some overlaps between cancerous and normal samples, implying that the data structure or relationship was maybe complex and nonlinear. Therefore, to determine whether a tissue is cancerous or not from its NIR spectrum, a mathematical model needs to be trained by using some known samples.


Near-infrared spectroscopy as a diagnostic tool for distinguishing between normal and malignant colorectal tissues.

Chen H, Lin Z, Mo L, Wu T, Tan C - Biomed Res Int (2015)

Three-dimensional scatter plot of the first three principal components (PCs) and its 2-dimensional projection. The variance explained by each PC is indicated in parenthesis.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4309295&req=5

fig3: Three-dimensional scatter plot of the first three principal components (PCs) and its 2-dimensional projection. The variance explained by each PC is indicated in parenthesis.
Mentions: Principal component analysis (PCA) was used to examine the possible clustering in samples and investigate the extent to which NIR features can differentiate cancerous and normal tissues. Figure 3 provided the three-dimensional scatter plot of the first three principal components (PCs) and its 2-dimensional projection. The first three PCs accounted for about 80% of the total variation in the NIR spectra. As can be seen in Figure 3, the separation was not clear and there existed some overlaps between cancerous and normal samples, implying that the data structure or relationship was maybe complex and nonlinear. Therefore, to determine whether a tissue is cancerous or not from its NIR spectrum, a mathematical model needs to be trained by using some known samples.

Bottom Line: The successive projection algorithm-linear discriminant analysis (SPA-LDA) was used to seek a reduced subset of variables/wavenumbers and build a diagnostic model of LDA.The results showed that, compared to PLS-DA, SPA-LDA provided more parsimonious model using only three wavenumbers/variables (4065, 4173, and 5758 cm(-1)) to achieve the sensitivity of 84.6%, 92.3%, and 92.3% for the training, validation, and test sets, respectively, and the specificity of 100% for each subset.It indicated that the combination of NIR spectroscopy and SPA-LDA algorithm can serve as a potential tool for distinguishing between normal and malignant colorectal tissues.

View Article: PubMed Central - PubMed

Affiliation: Yibin University Hospital, Yibin, Sichuan 644000, China ; Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, China.

ABSTRACT
Cancer diagnosis is one of the most important tasks of biomedical research and has become the main objective of medical investigations. The present paper proposed an analytical strategy for distinguishing between normal and malignant colorectal tissues by combining the use of near-infrared (NIR) spectroscopy with chemometrics. The successive projection algorithm-linear discriminant analysis (SPA-LDA) was used to seek a reduced subset of variables/wavenumbers and build a diagnostic model of LDA. For comparison, the partial least squares-discriminant analysis (PLS-DA) based on full-spectrum classification was also used as the reference. Principal component analysis (PCA) was used for a preliminary analysis. A total of 186 spectra from 20 patients with partial colorectal resection were collected and divided into three subsets for training, optimizing, and testing the model. The results showed that, compared to PLS-DA, SPA-LDA provided more parsimonious model using only three wavenumbers/variables (4065, 4173, and 5758 cm(-1)) to achieve the sensitivity of 84.6%, 92.3%, and 92.3% for the training, validation, and test sets, respectively, and the specificity of 100% for each subset. It indicated that the combination of NIR spectroscopy and SPA-LDA algorithm can serve as a potential tool for distinguishing between normal and malignant colorectal tissues.

Show MeSH