Near-Infrared Spectroscopy as a Diagnostic Tool for Distinguishing between Normal and Malignant Colorectal Tissues

Chen, Hui; Lin, Zan; Mo, Lin; Wu, Tong; Tan, Chao

doi:10.1155/2015/472197

Cited by 22 publications

(24 citation statements)

References 27 publications

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“…The biggest spectral differences between cancer and normal tissues were observed in the interval characteristic for first overtones and combination vibrations of OH, NH and CH bonds. It is well known that carbohydrate level is reduced in cancer tissues as compared to the normal tissue and phosphate content of normal tissues is higher than cancerous ones 19 .…”

Section: Resultsmentioning

confidence: 99%

Spectral fiber sensors for cancer diagnosticsin vitro

et al. 2015

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Cancer is one of the leading causes for morbidity and mortality worldwide. Therefore, efforts are concentrated on cancer detection in an early stage to enhance survival rates for cancer patients. A certain intraoperative navigation in the tumor border zone is also an essential task to lower the mortality rate after surgical treatment. Molecular spectroscopy methods proved to be powerful tools to differentiate cancerous and healthy tissue. Within our project comparison of different vibration spectroscopy methods were tested to select the better one or to reach synergy from their combination.One key aspect was in special fiber probe development for each technique. Using fiber optic probes in Raman, MIR and NIR spectroscopy is a very powerful method for non-invasive in vivo applications. Miniaturization of Raman probes was achieved by deposition of dielectric filters directly onto the silica fiber end surfaces. Raman, NIR and MIR spectroscopy were used to analyze samples from kidney tumors. The differentiation between cancer and healthy samples was successfully obtained by multivariate data analysis.

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Section: Resultsmentioning

confidence: 99%

Spectral fiber sensors for cancer diagnosticsin vitro

et al. 2015

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“…First of all, direct comparison of single features or simple combinations of features, has been performed to distinguish different tissues [4,[30][31][32][33][34]. In addition, several machine learning techniques have been used for the tissue classification such as classification and regression tree algorithms [5,6,[35][36][37], linear discriminant analysis (LDA) [24,[38][39][40] and support vector machines (SVM) [3,20,26,37,38,[41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Optimizing algorithm development for tissue classification in colorectal cancer based on diffuse reflectance spectra

Baltussen

Sterenborg

Ruers

et al. 2019

Biomed. Opt. Express

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Diffuse reflectance spectroscopy can be used in colorectal cancer surgery for tissue classification. The main challenge in the classification task is to separate healthy colorectal wall from tumor tissue. In this study, four normalization techniques, four feature extraction methods and five classifiers are applied to nine datasets, to obtain the optimal method to separate spectra measured on healthy colorectal wall from spectra measured on tumor tissue. All results are compared to the use of the entire non-normalized spectra. It is found that the most optimal classification approach is to apply a feature extraction method on non-normalized spectra combined with support vector machine or neural network classifier.

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“…Considerable progress has been made by applying FTIR spectral imaging to the examination of cancerous tissue and this approach has considerable potential to improve the accuracy of cancer diagnosis. However, the spatial resolution that can be obtained with FTIR is limited by diffraction and the strength of the technique derives from the capability to record images at several thousand wavelengths simultaneously.…”

Section: Introductionmentioning

confidence: 99%

SNOM Imaging of a Crypt‐Like Feature in Adenocarcinoma Associated with Barrett's Oesophagus

Craig

Smith

Holder

et al. 2018

Physica Status Solidi (b)

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The development of more accurate and sensitive diagnostic techniques is a key factor in efforts to improve cancer survival rates. The technique of infrared aperture fibre scanning near-field optical microscopy (IR-SNOM), together with radiation from the infrared free-electron laser (IR-FEL) on ALICE at Daresbury Laboratory (UK), has been used to obtain IR images of a cryptlike feature and the surrounding tissue; the tissue was taken from a patient with oesophageal adenocarcinoma and with a history of Barrett's oesophagus. We have shown that the DNA signal is enhanced relative to other contributions in the region of the crypt, and the glycoprotein signal shows a less pronounced increase in the region of the crypt. The Amide II signal is found to be anti-correlated with the DNA and glycoprotein profiles. The absorbance of the Amide II signal is found to differ for three different types of cancer tissue. High-resolution IR images of the crypt reveal additional structure that would not be resolved in diffraction-limited techniques.

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Near-Infrared Spectroscopy as a Diagnostic Tool for Distinguishing between Normal and Malignant Colorectal Tissues

Cited by 22 publications

References 27 publications

Spectral fiber sensors for cancer diagnosticsin vitro

Spectral fiber sensors for cancer diagnosticsin vitro

Optimizing algorithm development for tissue classification in colorectal cancer based on diffuse reflectance spectra

SNOM Imaging of a Crypt‐Like Feature in Adenocarcinoma Associated with Barrett's Oesophagus

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