Identifying benign and malignant thyroid nodules based on blood serum surface-enhanced Raman spectroscopy

Xia, Lu; Lu, Jin‐Jian; Chen, Zhilin; Cui, Xiaoyu; Chen, Shuo; Pei, Dongmei

doi:10.1016/j.nano.2020.102328

Cited by 28 publications

(12 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All two groups of spectral were simultaneously analyzed using PCA, in order to reduce the spectral dataset to a smaller number of variables (principal components (PCs)) that describe the majority of the variance in the spectral dataset [16] . Partial least squares discriminant analysis (PLS-DA) is a supervised classi cation model that is performed on the spectral data of liver cancer and healthy individual samples as X-variables (predictors) and their class information as Y-variables [22][23][24][25][26] . The second derivative spectrum can improve spectral resolution by amplifying small differences [6] .…”

Section: Discussionmentioning

confidence: 99%

Based on Serum Raman and Fluorescence Spectra to Diagnose Liver Cancer

Yang

et al. 2022

Preprint

View full text Add to dashboard Cite

Background: Raman and fluorescence spectra techniques are potential tools for disease diagnosis. In recent years, the application of Raman and fluorescence spectra techniques in biological studies has increased a great deal, and clinical investigations relevant to cancer detection by spectroscopic means have attracted particularly attention from both clinical and non-clinical researchers. Methods: In this article, Raman and fluorescence spectra were employed for the detection of liver cancer and healthy individuals using their serum samples. These serum samples were compared with their spectral features acquired by Raman and fluorescence spectroscopy to initially establish spectral features that can be considered spectral markers of liver cancer diagnosis. Resuits: The intensity differences from characteristic peaks of carotene, protein and lipid associated Raman spectra were clearly observed in liver cancer patient serum samples versus normal human serum. The changes in the serum fluorescence profiles of liver cancer patients were also analyzed. To probe the capacity and contrast of Raman spectroscopy as an analytical implement for the early diagnosis of liver cancer, principal component analysis (PCA) was used to analyze the Raman spectra of controls , liver cancer patients and healthy individuals. Furthermore, the Partial Least Squares-Discriminant Analysis (PLS-DA) was performed to compare the diagnostic performance of Raman spectroscopy for the classification of disease samples and healthy samples.Conclusion: Compare with the existing diagnostic techniques, the Raman spectroscopy technique has an excellent advantage in extremely low sample requirements, ease of use and ideal screening procedures. Thus, Raman spectroscopy has great potential to be developed as a powerful tool for distinguishing between healthy and liver cancer serum samples.

show abstract

Section: Discussionmentioning

confidence: 99%

Based on Serum Raman and Fluorescence Spectra to Diagnose Liver Cancer

Yang

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…49 The PLS-LDA model based on SERS spectra produced an accuracy, sensitivity, and specificity of 93.65%, 92.68%, and 95.45%, respectively, for differentiating between healthy and thyroid nodular groups, while those of 82.93%, 81.82%, and 84.21% were achieved for benign versus malignant groups. 49 However, Liang et al performed SERS measurements of blood plasma from patients with a benign thyroid tumor (n = 32) and thyroid cancer (n = 70) after utilizing filter membranes to remove macromolecular proteins in blood plasma. PCA-LDA and Lasso-PLS-DA models of SERS spectra yielded 84.3% and 90.2% discrimination accuracy, respectively.…”

Section: ■ Raman Spectroscopy For Diagnosticsmentioning

confidence: 93%

Diagnostics and Therapy Assessment Using Label-Free Raman Imaging

El-Mashtoly

Gerwert

2021

Anal. Chem.

View full text Add to dashboard Cite

“…[ 103 ] Based on blood serum surface‐enhanced Raman spectroscopy (SERS) combined with partial least squares analysis and linear discriminant analysis (PLS‐LDA), patients with benign and malignant thyroid nodules were identified rapidly and accurately. [ 104 ]…”

Section: Applicationmentioning

confidence: 99%

A review of artificial intelligence methods combined with Raman spectroscopy to identify the composition of substances

Pan

Zhang

Daengngam

et al. 2021

J Raman Spectroscopy

View full text Add to dashboard Cite

In general, most of the substances in nature exist in mixtures, and the noninvasive identification of mixture composition with high speed and accuracy remains a difficult task. However, the development of Raman spectroscopy, machine learning, and deep learning techniques has paved the way for achieving efficient analytical tools capable of identifying mixture components, thus leading to an apparent breakthrough in the identification of mixtures beyond traditional chemical analysis methods. This review summarizes the work of Raman spectroscopy in identifying the composition of substances; reviews the preprocessing process of Raman spectroscopy, artificial intelligence analysis methods, and analysis procedures; and examines the application of artificial intelligence. Finally, the advantages and disadvantages and development prospects of Raman spectroscopy are discussed in detail.

show abstract

Identifying benign and malignant thyroid nodules based on blood serum surface-enhanced Raman spectroscopy

Cited by 28 publications

References 46 publications

Based on Serum Raman and Fluorescence Spectra to Diagnose Liver Cancer

Based on Serum Raman and Fluorescence Spectra to Diagnose Liver Cancer

Diagnostics and Therapy Assessment Using Label-Free Raman Imaging

A review of artificial intelligence methods combined with Raman spectroscopy to identify the composition of substances

Contact Info

Product

Resources

About