scholarly journals All-in-one Raman spectroscopy approach to diagnosis of colorectal cancer: analysis of spectra in the fingerprint regions

2019 ◽  
Vol 3 (2) ◽  
pp. 84-90 ◽  
Author(s):  
Sumito Sato ◽  
Ryuichi Sekine ◽  
Hirotada Kagoshima ◽  
Keisuke Kazama ◽  
Aya Kato ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Raman Spectroscopy

scholarly journals Detection and time-tracking activation of a photosensitiser on live single colorectal cancer cells using Raman spectroscopy

The Analyst ◽  
2020 ◽  
Vol 145 (17) ◽  
pp. 5878-5888
Author(s):  
Julia Gala de Pablo ◽  
David R. Chisholm ◽  
Carrie A. Ambler ◽  
Sally A. Peyman ◽  
Andrew Whiting ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Raman Spectroscopy ◽  
Cancer Cells ◽  
Cell Response ◽  
Single Cells ◽  
Colorectal Cancer Cells

Tracking of the accumulation, activation, degradation of a photosensitiser and cell response in live colorectal cancer single-cells using Raman spectroscopy.

The Use of Spectral Ellipsometry and Raman Spectroscopy in Screening Diagnostics of Colorectal Cancer

2019 ◽  
Vol 127 (1) ◽  
pp. 170-176
Author(s):  
V. N. Kruchinin ◽  
M. V. Kruchinina ◽  
Ya. I. Prudnikova ◽  
E. V. Spesivtsev ◽  
S. V. Rykhlitskii ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Raman Spectroscopy ◽  
Spectral Ellipsometry ◽  
Screening Diagnostics

scholarly journals Highly accurate colorectal cancer prediction model based on Raman spectroscopy using patient serum

2020 ◽  
Vol 12 (11) ◽  
pp. 1311-1324
Author(s):  
Hiroaki Ito ◽  
Naoyuki Uragami ◽  
Tomokazu Miyazaki ◽  
William Yang ◽  
Kenji Issha ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Raman Spectroscopy ◽  
Prediction Model ◽  
Cancer Prediction ◽  
Model Based

scholarly journals Application of ellipsometry, spr-technic and raman-spectroscopy into diagnosis of colorectal cancer

2019 ◽  
Vol 516 ◽  
pp. 012017
Author(s):  
M V Kruchinina ◽  
A A Gromov ◽  
Y I Prudnikova ◽  
V M Generalov ◽  
V N Kruchinin ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Raman Spectroscopy

Drop coating deposition Raman spectroscopy of blood plasma for the detection of colorectal cancer

2015 ◽  
Vol 20 (3) ◽  
pp. 037004 ◽  
Author(s):  
Pengpeng Li ◽  
Changshui Chen ◽  
Xiaoyuan Deng ◽  
Hua Mao ◽  
Shaoqin Jin
Keyword(s):  
Colorectal Cancer ◽  
Raman Spectroscopy ◽  
Blood Plasma ◽  
Coating Deposition

scholarly journals Raman Spectroscopy in Colorectal Cancer Diagnostics: Comparison of PCA-LDA and PLS-DA Models

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Wenjing Liu ◽  
Zhaotian Sun ◽  
Jinyu Chen ◽  
Chuanbo Jing
Keyword(s):  
Colorectal Cancer ◽  
Raman Spectroscopy ◽  
Discriminant Analysis ◽  
Diagnostic Accuracy ◽  
Raman Spectra ◽  
Principal Component ◽  
Cancer Diagnostics ◽  
Analysis Model ◽  
Tissue Samples ◽  
Linear Discriminant

Raman spectra of human colorectal tissue samples were employed to diagnose colorectal cancer. High-quality Raman spectra were acquired from normal and cancerous colorectal tissues from 81 patients. Subtle Raman variations, such as for peaks at 1134 cm−1 (protein, C-C/C-N stretching) and 1297 cm−1 (lipid, C-H2 twisting), were observed between normal and cancerous colorectal tissues. The average peak intensity at 1134 and 1297 cm−1 was increased from approximately 235 and 72 in the normal group, respectively, to 315 and 273 in the cancer group. The variations of Raman spectra reflected the changes of cell molecules during canceration. The multivariate statistical methods of principal component analysis-linear discriminant analysis (PCA-LDA) and partial least-squares-discriminant analysis (PLS-DA), together with leave-one-patient-out cross-validation, were employed to build the discrimination model. PCA-LDA was used to evaluate the capability of this approach for classifying colorectal cancer, resulting in a diagnostic accuracy of 79.2%. Further PLS-DA modeling yielded a diagnostic accuracy of 84.3% for colorectal cancer detection. Thus, the PLS-DA model is preferable between the two to discriminate cancerous from normal tissues. Our results demonstrate that Raman spectroscopy can be used with an optimized multivariate data analysis model as a sensitive diagnostic alternative to identify pathological changes in the colon at the molecular level.

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