Label-free detection of liver cancer based on silver nanoparticles coated tissue surface-enhanced Raman spectroscopy

Lin, Kan; Xu, Jianshu; Li, Ling; Liao, Fadian; Dong, Xianhu; Lin, Juqiang

doi:10.1088/1612-202x/aae13c

Cited by 10 publications

(4 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Conventional methods for diagnosing collected Raman data of normal and cancerous tissues usually utilize Raman peak intensity comparison − or various multivariate statistical analytical methods, such as PCA, − PCA-LDA, − PC-DFA, or PLS. − These methods pose a limitation toward improving accuracy. Therefore, it is crucial to find more accurate methods for tumor diagnosis.…”

Section: Discussionmentioning

confidence: 99%

“…Raman peak intensity comparison is the most direct method, but the accuracy is not high. − However, for large amounts of Raman data, researchers tend to use various multivariate statistical analytical methods to analyze spectral differences. Multivariate statistical analysis methods include principal component analysis (PCA), − principal component analysis-linear discriminant analysis (PCA-LDA), − principal component differential function analysis (PC-DFA), partial least squares (PLS), − etc. These methods pose a limitation toward improving accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

High-Precision Intelligent Cancer Diagnosis Method: 2D Raman Figures Combined with Deep Learning

Zhang

Yang

et al. 2022

Anal. Chem.

View full text Add to dashboard Cite

Raman spectroscopy, as a label-free detection technology, has been widely used in tumor diagnosis. However, most tumor diagnosis procedures utilize multivariate statistical analysis methods for classification, which poses a major bottleneck toward achieving high accuracy. Here, we propose a concept called the two-dimensional (2D) Raman figure combined with convolutional neural network (CNN) to improve the accuracy. Twodimensional Raman figures can be obtained from four transformation methods: spectral recurrence plot (SRP), spectral Gramian angular field (SGAF), spectral short-time Fourier transform (SSTFT), and spectral Markov transition field (SMTF). Two-dimensional CNN models all yield more than 95% accuracy, which is higher than the PCA-LDA method and the Raman-spectrum-CNN method, indicating that 2D Raman figure inputs combined with CNN may be one reason for gaining excellent performances. Among 2D-CNN models, the main difference is the conversion, where SRP is based on the structure of wavenumber series with the best performances (98.9% accuracy, 99.5% sensitivity, 98.3% specificity), followed by SGAF on the wavenumber series, SSTFT on wavenumber and intensity information, and SMTF on wavenumber position information. The inclusion of external information in the conversion may be another reason for improvement in the accuracy. The excellent capability shows huge potential for tumor diagnosis via 2D Raman figures and may be applied in other spectroscopy analytical fields.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High-Precision Intelligent Cancer Diagnosis Method: 2D Raman Figures Combined with Deep Learning

Zhang

Yang

et al. 2022

Anal. Chem.

View full text Add to dashboard Cite

show abstract

“…However, the common Raman signals are extremely low and easy to be disturbed by fluorescence. In order to overcome this obstacle, in the past decades, the advent of surface-enhanced RS (SERS) has dramatically expanded the application of RS in the field of biomedical analysis [17][18][19][20]. The reason is due to its extremely high Raman scattering efficiency, which is achieved by the interaction between the nano-structured substrate and the analytes of interest.…”

Section: Surface-enhanced Raman Spectroscopy For Classification Of La...mentioning

confidence: 99%

Surface-enhanced Raman spectroscopy for classification of laryngeal cancer and adjacent tissues

Lai¹,

Wan²,

Wu³

et al. 2019

Laser Phys.

View full text Add to dashboard Cite

As an optical spectroscopic technique, Raman spectroscopy (RS) has shown promise for in vivo cancer diagnostics in various organs. Common Raman signals are extremely low and easily disturbed by fluorescence. In this study, we used surface-enhanced RS to scan patients' malignant and adjacent tissues, as well as the normal tissues. Raman scattering signals were acquired from 35 normal tissue samples and 39 laryngeal carcinoma tissue samples. Linear discriminant analysis (LDA), based on principal component analysis, was employed to generate diagnostic algorithms for the classification of different laryngeal tissue types. We found there were several variations in surface-enhanced RS (SERS) peaks, and the same with other malignant ones. When using the principle component-LDA (PC-LDA) algorithms, this study showed that the diagnostic sensitivity and specificity were 99.2% and 98.4%, respectively for the identifying cancer tissues from normal samples. Results show that there are significant spectral differences (different peak intensities) between cancer tissues and normal tissues. High classification accuracy can be achieved by using the PC-LDA diagnostic algorithm, which demonstrates the great potential of tissue-SERS analysis combined with PC-LDA diagnostic algorithms for cancer screening in laryngeal cancer.

show abstract

“…Meanwhile, AuNPs are also one of the most commonly used enhancement substrates in surface enhanced Raman spectroscopy (SERS). SERS has been considered a reliable method for the rapid and non-destructive detection of various analytes [18][19][20][21]. It offers an amplification of 'fingerprint-like' signals for molecules benefiting from the localized surface plasmonic resonance (LSPR) effect of nanostructures [22], which is suitable for directly capturing target molecule signals from the sample surface.…”

Section: Introductionmentioning

confidence: 99%

In situ detection of thiram in fruits and vegetables by colorimetry/surface-enhanced Raman spectroscopy

et al. 2020

View full text Add to dashboard Cite

In this study, a rapid, stable, and sensitive colorimetric/surface-enhanced Raman spectroscopy (SERS) method was developed based on a polyvinyl alcohol -decorated Au nanoparticle (PVA-Au) colloid for the in situ detection of thiram residues in fruits and vegetables. The PVA-Au colloid acted as both a colorimetric and SERS sensor. The colorimetric detection of thiram is based on the color change and absorption band shift caused by aggregation of the PVA-Au colloid. The stable and controllable colorimetric process has been verified. The influence of the media pH and reaction time on the aggregation of the PVA-Au colloid were also investigated. The reflectance and SERS spectra of the thiram samples were acquired and analyzed. The limit of the colorimetric detection of thiram in apples, pears, and vegetables was 4.62, 7.83, and 10.74 ppb (µg kg −1 ), respectively, which is well below the maximum residue level of 5 ppm (mg kg −1 ) set by the National Food Safety Standard of China and the US Environmental Protection Agency. Meanwhile, rapid, in situ, and non-invasive SERS detection was used to acquire SERS spectra of thiram residues based on the localized surface plasmonic resonance effect of the PVA-Au colloid. The naked-eye visible and stable colorimetric detection is suitable for the rapid detection of pesticide residues in a large number of plant samples, while the positive results could be further confirmed and verified by SERS. The results demonstrate that this colorimetric/SERS method has great potential to detect thiram residues in real foods and improve the detection efficiency.

show abstract

Label-free detection of liver cancer based on silver nanoparticles coated tissue surface-enhanced Raman spectroscopy

Cited by 10 publications

References 26 publications

High-Precision Intelligent Cancer Diagnosis Method: 2D Raman Figures Combined with Deep Learning

High-Precision Intelligent Cancer Diagnosis Method: 2D Raman Figures Combined with Deep Learning

Surface-enhanced Raman spectroscopy for classification of laryngeal cancer and adjacent tissues

In situ detection of thiram in fruits and vegetables by colorimetry/surface-enhanced Raman spectroscopy

Contact Info

Product

Resources

About