Detection of cervical metastatic lymph nodes in papillary thyroid carcinoma by Fourier transform infrared spectroscopy

Liu, Y; Xu, Yizhuang; Liu, Y; Zhang, Y; Wang, D; Xiu, Dianrong; Xu, Zhi; Zhou, Xiaosi; Wu, Jun; Ling, Xiaofeng

doi:10.1002/bjs.7330

Cited by 34 publications

(21 citation statements)

References 15 publications

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“…As presented in Table 3 and Table 4, nine parameters are found to be pronouncedly different between cancer and normal tissues, including I 1640 (related to protein), I 1550 (related to protein), I 1460 (related to lipid), I 1400 (related to lipid), I 1250 (related to nucleic acid), I 1120 (related to carbohydrate), I 1080 (related to nucleic acid), I 1040 (related to carbohydrate) and P 1040 (related to carbohydrate). As shown, these parameters in the cancer group are both higher than those in the normal group, indicating that there may be high cellular metabolism, enhanced cell proliferation and an increased expression of proteins in cancer cell, as we have observed in the previous papers [16][17][18] .…”

Section: Characteristic Of Ftir Spectroscopic Datasupporting

confidence: 63%

Detection of cholangiocarcinoma with Fourier transform infrared spectroscopy and radial basis function neural network classification

Cui

Ling

et al. 2016

Chem. Res. Chin. Univ.

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The aim of this study was to explore the possibility of applying Fourier transform infrared(FTIR) spectroscopy as a medical diagnostic tool based on a neural network classifier for detecting and classifying cholangiocarcinoma. A total of 51 cases of bile duct tissues were obtained and later characterized by FTIR spectroscopy prior to pathological diagnosis. The criteria for classification included 30 parameters for each FTIR spectra, including peak position(P), intensity(I) and full width at half-maximum(FWHM), were measured, calculated and subsequently compared against the normal and cancer groups. The FTIR spectra were classified by the radial basis function(RBF) network model. For establishing the RBF, 23 cases were used to train the RBF classifier, and 28 cases were applied to validate the model. Using the RFB model, nine parameters were observed to be pronouncedly different between cancerous and normal tissue, including I and P 1040 . In the RBF training classification, the accuracy, sensitivity, and specificity of diagnosis were 82.6%, 80.0%, and 84.6%, respectively. While validating the classification, the accuracy, sensitivity, and specificity of diagnosis were 78.6%, 75.0%, and 81.2%, respectively. The results suggest that FTIR spectroscopy combined with neural network classifier could be applied as a medical diagnostic tool in cholangiocarcinoma diagnosis.

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Section: Characteristic Of Ftir Spectroscopic Datasupporting

confidence: 63%

Detection of cholangiocarcinoma with Fourier transform infrared spectroscopy and radial basis function neural network classification

Cui

Ling

et al. 2016

Chem. Res. Chin. Univ.

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show abstract

“…investigate colorectal cancer [7,8], gastric cancer [9], prostate cancer [10], papillary thyroid carcinoma [11], breast cancer [12], ovarian cancer [13,14], urinary bladder cancer [15] and other cancers [16][17][18][19][20][21][22]. There were also some reports on FTIR spectroscopic study of liver diseases such as liver cancer [5,23,24] and hepatic fibrosis [25,26] [26].…”

Section: Introductionmentioning

confidence: 98%

A study of structural differences between liver cancer cells and normal liver cells using FTIR spectroscopy

Sheng

et al. 2015

Journal of Molecular Structure

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“…These and other works demonstrate that FT-IR spectroscopy is a new and promising method for diagnosing diseases. Although the high absorption of water in the fingerprint region limits its usefulness in vivo, recent developments have also been directed at intraoperative applications [15,16]. The spectral region from 1500 to 1000 cm −1 , corresponding to diminished water absorption, can be used to provide surgeons with rapid and accurate information about the tissue [13].…”

Section: Introductionmentioning

confidence: 99%

Label-free differentiation of human pituitary adenomas by FT-IR spectroscopic imaging

et al. 2012

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Fourier transform infrared (FT-IR) spectroscopic imaging has been used to characterize different types of pituitary gland tumors and normal pituitary tissue. Freshly resected tumor tissue from surgery was prepared as thin cryosections and examined by FT-IR spectroscopic imaging. Tissue types were discriminated via k-means cluster analysis and a supervised classification algorithm based on linear discriminant analysis. Spectral classification allowed us to discriminate between tumor and non-tumor cells, as well as between tumor cells that produce human growth hormone (hGH+) and tumor cells that do not produce that hormone (hGH-). The spectral classification was compared and contrasted with a histological PAS and orange G stained image. It was further shown that hGH+ pituitary tumor cells show stronger amide bands than tumor cells that do not produce hGH. This study demonstrates that FT-IR spectroscopic imaging can not only potentially serve as a fast and objective approach for discriminating pituitary gland tumors from normal tissue, but that it can also detect hGH-producing tumor cells.

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Detection of cervical metastatic lymph nodes in papillary thyroid carcinoma by Fourier transform infrared spectroscopy

Abstract: FTIR spectroscopy is a novel technique for detection of metastatic lymph nodes and may prove useful in surgery for papillary thyroid cancer.

Cited by 34 publications

References 15 publications

Detection of cholangiocarcinoma with Fourier transform infrared spectroscopy and radial basis function neural network classification

Detection of cholangiocarcinoma with Fourier transform infrared spectroscopy and radial basis function neural network classification

A study of structural differences between liver cancer cells and normal liver cells using FTIR spectroscopy

Label-free differentiation of human pituitary adenomas by FT-IR spectroscopic imaging

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