Distinction of leukemia patients’ and healthy persons’ serum using FTIR spectroscopy

Sheng, Daping; Liu, Xingcun; Li, Weizu; Wang, Yuchan; Chen, Xianliang; Wang, Xin

doi:10.1016/j.saa.2012.09.072

Cited by 60 publications

(35 citation statements)

References 15 publications

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“…Several studies have obtained good results applying FTIR associated to chemometrics in the discrimination of healthy and cancer patients (FUJIOKA et al, 2004;HANDS et al, 2016;KHANMOHAMMADI et al, 2007;LI et al, 2013LI et al, , 2012SHENG et al, 2013). Li et al (2013) discriminated normal tissues of cancerous tissues in gastric biopsies with 100% sensitivity and specificity of 83.3% for cancer and Fujioka et al (2004) with sensitivity and specificity of 96% and 75%, respectively.…”

Section: Rmsep=0mentioning

confidence: 99%

“…However, few studies have used conventional FTIR for discriminating samples from patients with CRC and healthy ones. It is also important to highlight that most of the studies of FTIR application in the detection of cancer do not use plasma as a sample nor the PLS-DA as Revista Jovens Pesquisadores ISSN 2237 048X, DOI: 10.17058/rjp.v9i2.13372 chemometric analysis (CHABER et al, 2018;HANDS et al, 2016;KHANMOHAMMADI et al 2007, LI, QB et al, 2005, LI, X. et al, 2012, SHENG et al, 2013.…”

Section: Rmsep=0mentioning

confidence: 99%

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Discrimination of healthy and colorectal cancer patients using FTIR and PLS-DA

Riça

Cassol²,

Rieger

et al. 2019

RJP

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Spectroscopic methods have already been used as effective tools in several studies involving the detection of cancer. Fourier transform infrared spectroscopy (FTIR) has already been applied in the discrimination of cancer cells and tissues or blood of patients with the disease, observing that this technique requires the use of chemometric algorithms to obtain such results. The aim of this study was to employ a partial least squares discriminant analysis (PLS-DA) with FTIR data in the discrimination of plasma samples from patients with colorectal cancer (RCC) and healthy individuals. Multivariate analysis was performed using PLS-DA of the sample triplicates (n=90) with different types of processing. The best PLS-DA condition was obtained using the 1st derivative, 1 orthogonal signal correction (OSC) and no pre-processing. With 1 factor only, the model presented a mean square error of cross-validation (RMSECV) of 0.0004 and coefficient of determination (r^2) of 1.0000. The accuracy, precision and sensitivity of the model were 100%.

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Section: Rmsep=0mentioning

confidence: 99%

Section: Rmsep=0mentioning

confidence: 99%

Discrimination of healthy and colorectal cancer patients using FTIR and PLS-DA

Riça

Cassol²,

Rieger

et al. 2019

RJP

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“…These biomolecules are closely tied to the carcinogenesis of cells and tissues. Thus, FTIR spectroscopy has been used to investigate various tumors, including breast cancer [3,4] , gastric cancer [5] and leukemia [6] . Related research results show that FTIR spectroscopy can be utilized to detect cancer at the early stage, differentiate cancerous tissue from normal tissue, and even can be used to define surgical boundaries of cancer resection [7] .…”

Section: Introductionmentioning

confidence: 99%

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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“…The metabolites of cancer cells cause the component changes of biomolecules in serum [24], and detecting these changes with FTIR spectroscopy may provide a new method for cancer diagnosis. Several cancers were investigated using serum's IR spectra, involving gastric cancer [16], leukemia [25], esophageal cancer [26], etc. Although Yang et al studied IR spectra of lung cancer patients' serum, their results had limitations as following: 1.…”

Section: Introductionmentioning

confidence: 99%