Limits...
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.

Show MeSH
Populations mean spectra and the standard deviation of cancerous and normal tissue specimens.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4309295&req=5

fig2: Populations mean spectra and the standard deviation of cancerous and normal tissue specimens.

Mentions: Figure 2 shows the populations mean spectra and the standard deviation of cancerous and normal tissue specimens. Spectra from cancerous tissues were different in some regions, which reflected certain changes in the levels of various biochemical compositions due to canceration. It is also clear in Figure 2 that the spectral peaks of raw NIR signal are broad and overlapping and therefore make it impossible to carry out direct quantification analysis. Even if NIR spectrum cannot provide significantly different peaks like midinfrared (MIR) spectroscopy, it still includes much information on chemical composition of the tissue. Also provided in Figure 2 were the assignments of bands to different chemical substructures. From left to right in the region of 10000–4000 cm−1, four subregions correspond to the CH, NH, OH, and CC combinations, CH first overtones, first overtone of OH, NH, and CH combinations, and CH second overtone of fundamental vibration transitions, respectively. These NIR spectroscopic bands coupled with unsupervised pattern recognition have also been used for gastric cancer differentiation [14]. Each NIR spectrum is actually a mixture of the spectral signatures of various tissue components, especially proteins, lipids, and carbohydrates. The differences in composition between cancerous and normal tissues have been extensively investigated by chemical, histochemical, and biochemical means. For example, carbohydrate level was reduced in cancer tissues compared to normal tissues. The phosphate content of normal tissues was higher than cancerous ones. Overall, the NIR spectrum can provide information on tissue blood flow, oxygen saturation and consumption, and compositions. Thus, any alterations in the composition of the tissues can be captured in NIR spectrum and used for diagnostic purpose. It is also noteworthy that the spectral profile variation in some regions is higher for the cancerous tissues. It is maybe due to different stages of carcinogenesis of the tissues and differences in the thickness of the tissues, which influence spectral reflectance caused by photon penetrating depths.


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)

Populations mean spectra and the standard deviation of cancerous and normal tissue specimens.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: Populations mean spectra and the standard deviation of cancerous and normal tissue specimens.
Mentions: Figure 2 shows the populations mean spectra and the standard deviation of cancerous and normal tissue specimens. Spectra from cancerous tissues were different in some regions, which reflected certain changes in the levels of various biochemical compositions due to canceration. It is also clear in Figure 2 that the spectral peaks of raw NIR signal are broad and overlapping and therefore make it impossible to carry out direct quantification analysis. Even if NIR spectrum cannot provide significantly different peaks like midinfrared (MIR) spectroscopy, it still includes much information on chemical composition of the tissue. Also provided in Figure 2 were the assignments of bands to different chemical substructures. From left to right in the region of 10000–4000 cm−1, four subregions correspond to the CH, NH, OH, and CC combinations, CH first overtones, first overtone of OH, NH, and CH combinations, and CH second overtone of fundamental vibration transitions, respectively. These NIR spectroscopic bands coupled with unsupervised pattern recognition have also been used for gastric cancer differentiation [14]. Each NIR spectrum is actually a mixture of the spectral signatures of various tissue components, especially proteins, lipids, and carbohydrates. The differences in composition between cancerous and normal tissues have been extensively investigated by chemical, histochemical, and biochemical means. For example, carbohydrate level was reduced in cancer tissues compared to normal tissues. The phosphate content of normal tissues was higher than cancerous ones. Overall, the NIR spectrum can provide information on tissue blood flow, oxygen saturation and consumption, and compositions. Thus, any alterations in the composition of the tissues can be captured in NIR spectrum and used for diagnostic purpose. It is also noteworthy that the spectral profile variation in some regions is higher for the cancerous tissues. It is maybe due to different stages of carcinogenesis of the tissues and differences in the thickness of the tissues, which influence spectral reflectance caused by photon penetrating depths.

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