High-Accuracy Renal Cell Carcinoma Discrimination through Label-Free SERS of Blood Serum and Multivariate Analysis (original) (raw)
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Breast Cancer Diagnosis by Surface-Enhanced Raman Scattering (SERS) of Urine
Applied Sciences
Background: There is an ongoing research for breast cancer diagnostic tools that are cheaper, more accurate and more convenient than mammography. Methods: In this study, we employed surface-enhanced Raman scattering (SERS) for analysing urine from n = 53 breast cancer patients and n = 22 controls, with the aim of discriminating between the two groups using multivariate data analysis techniques such as principal component analysis—linear discriminant analysis (PCA-LDA). The SERS spectra were acquired using silver nanoparticles synthesized by reduction with hydroxylamine hydrochloride, which were additionally activated with Ca2+ 10−4 M. Results: The addition of Ca(NO3)2 10−4 M promoted the specific adsorption to the metal surface of the anionic purine metabolites such as uric acid, xanthine and hypoxanthine. Moreover, the SERS spectra of urine were acquired without any filtering or processing step for removing protein traces and other contaminants. Using PCA-LDA, the SERS spectra of u...
Potential for Raman spectroscopy to provide cancer screening using a peripheral blood sample
Head & Neck Oncology, 2009
Cancer poses a massive health burden with incidence rates expected to double globally over the next decade. In the United Kingdom screening programmes exists for cervical, breast, and colorectal cancer. The ability to screen individuals for solid malignant tumours using only a peripheral blood sample would revolutionise cancer services and permit early diagnosis and intervention. Raman spectroscopy interrogates native biochemistry through the interaction of light with matter, producing a high definition biochemical 'fingerprint' of the target material. This paper explores the possibility of using Raman spectroscopy to discriminate between cancer and noncancer patients through a peripheral blood sample. Forty blood samples were obtained from patients with Head and Neck cancer and patients with respiratory illnesses to act as a positive control. Raman spectroscopy was carried out on all samples with the resulting spectra being used to build a classifier in order to distinguish between the cancer and respiratory patients' spectra; firstly using principal component analysis (PCA)/linear discriminant analysis (LDA), and secondly with a genetic evolutionary algorithm. The PCA/LDA classifier gave a 65% sensitivity and specificity for discrimination between the cancer and respiratory groups. A sensitivity score of 75% with a specificity of 75% was achieved with a 'trained' evolutionary algorithm. In conclusion this preliminary study has demonstrated the feasibility of using Raman spectroscopy in cancer screening and diagnostics of solid tumours through a peripheral blood sample. Further work needs to be carried out for this technique to be implemented in the clinical setting.
Journal of Clinical Medicine
Early diagnosis based on screening is recognized as one of the most efficient ways of mitigating cancer-associated morbidity and mortality. Therefore, reliable but cost-effective methodologies are needed. By using a portable Raman spectrometer, a small and easily transportable instrument, the needs of modern diagnosis in terms of rapidity, ease of use and flexibility are met. In this study, we analyzed the diagnostic accuracy yielded by the surface-enhanced Raman scattering (SERS)-based profiling of serum, performed with a portable Raman device operating in a real-life hospital environment, in the case of 53 patients with gastrointestinal tumors and 25 control subjects. The SERS spectra of serum displayed intense bands attributed to carotenoids and purine metabolites such as uric acid, xanthine and hypoxanthine, with different intensities between the cancer and control groups. Based on principal component analysis-quadratic discriminant analysis (PCA-QDA), the cancer and control gro...
SIGNAL PROCESSING FOR RAMAN SPECTRA FOR DISEASE DETECTION Review Article
2016
Raman Spectroscopy enables in-depth study into the molecular structure of solid, liquid and gasses from its scattering spectrum. As such, the spectrum could offer a biochemical fingerprint to identify unknown molecules. Surface Enhanced Raman Spectroscopy (SERS) amplifies the weak Raman signal by 10+3 to 10+7 times, revolutionary making the method appealing to the research community. SERS has been proven useful for disease detection from a medium such as a cell, serum, urine, plasma, saliva, tears. The spectra displayed are noisy and complicated by the presence of other molecules, besides the targeted one. Moreover, the difference between the infected and controlled samples is far too minute for detection by the naked human eyes. Hence, signal processing techniques are found crucial to single out fingerprint of the target molecule from biological spectra. Our work here examines signal processing techniques attempted on SERS spectra for disease detection, such as Principal Component ...
International Journal of Pharmacy and Pharmaceutical Sciences, 2016
Raman Spectroscopy enables in-depth study into the molecular structure of solid, liquid and gasses from its scattering spectrum. As such, the spectrum could offer a biochemical fingerprint to identify unknown molecules. Surface Enhanced Raman Spectroscopy (SERS) amplifies the weak Raman signal by 10 +3 to 10 +7 times, revolutionary making the method appealing to the research community. SERS has been proven useful for disease detection from a medium such as a cell, serum, urine, plasma, saliva, tears. The spectra displayed are noisy and complicated by the presence of other molecules, besides the targeted one. Moreover, the difference between the infected and controlled samples is far too minute for detection by the naked human eyes. Hence, signal processing techniques are found crucial to single out fingerprint of the target molecule from biological spectra. Our work here examines signal processing techniques attempted on SERS spectra for disease detection, such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Logistic Regression Analysis (LRA). It is found that PCA-LDA is the most popular (45%), ensued by PCA-ANN (33%) and SVM (22%). PCA-SVM yields the highest in accuracy (99.9%), followed by PCA-ANN (98%) and LRA (97%). PCA-LDA and SVM score the highest in both sensitivity-specificity.
ACS nano, 2018
The use of emerging nanotechnologies, such as plasmonic nanoparticles in diagnostic applications, potentially offers opportunities to revolutionize disease management and patient healthcare. Despite worldwide research efforts in this area, there is still a dearth of nanodiagnostics which have been successfully translated for real-world patient usage due to the predominant sole focus on assay analytical performance and lack of detailed investigations into clinical performance in human samples. In a bid to address this pressing need, we herein describe a comprehensive clinical verification of a prospective label-free surface-enhanced Raman scattering (SERS) nanodiagnostic assay for prostate cancer (PCa) risk stratification. This contribution depicts a roadmap of (1) designing a SERS assay for robust and accurate detection of clinically validated PCa RNA targets; (2) employing a relevant and proven PCa clinical biomarker model to test our nanodiagnostic assay; and (3) investigating the...
Raman Spectroscopy: A Novel Experimental Approach to Evaluating Renal Tumours
European Urology, 2010
Background: New optical techniques of spectroscopy have shown promising results in the evaluation of solid tumours. Objective: To evaluate the potential of Raman spectroscopy (RS) to assess renal tumours at surgery. Design, setting, and participants: Over a 5 months period, Raman optical spectra were prospectively acquired on surgical renal specimen removed due to suspicion of cancer. Measurements: Raman measures were normalized to ensure comparison between spectra. A lower resolution signal was computed using a wavelet decomposition procedure to diminish the size of the signal and exploit the complete spectrum. A support vector machine (SVM) with a linear kernel and a sequential minimal optimization solver was applied. A leave-one-out cross validation technique was used to train and test the SVM. Results and limitations: There were 36 patients with 34 malignant (27 clear cell, 6 papillary and one chromophobe) and two benign (one oncocytoma and one metanephric cyst) tumours. A total of 241 analyzable Raman spectra were obtained. The SVM was able to classify tumoural and normal tissue with an accuracy of 84% (sensitivity 82%, specificity 87%). High grade and low grade tumours were differentiated with a precision of 82% (sensitivity 84%, specificity 80%). Histologic subtype could be categorized with an accuracy of 93% (sensitivity 96%, specificity 87%). SVM could not be applied to classify benign and malignant tumours because of the restricted number of benign spectra. Conclusions: RS can accurately differentiate normal and tumoural renal tissue, low grade and high grade renal tumours, as well as histologic subtype of RCC. Larger prospective studies are needed to confirm these preliminary data.
Analytical and Bioanalytical Chemistry, 2020
Intense label-free surface-enhanced Raman scattering (SERS) spectra of serum samples were rapidly obtained on Ag plasmonic paper substrates upon 785 nm excitation. Spectra from the hepatocellular carcinoma (HCC) patients showed consistent differences with respect to those of the control group. In particular, uric acid was found to be relatively more abundant in patients, while hypoxanthine, ergothioneine, and glutathione were found as relatively more abundant in the control group. A repeated double cross-validation (RDCV) strategy was applied to optimize and validate principal component analysis-linear discriminant analysis (PCA-LDA) models. An analysis of the RDCV results indicated that a PCA-LDA model using up to the first four principal components has a good classification performance (average accuracy was 81%). The analysis also allowed confidence intervals to be calculated for the figures of merit, and the principal components used by the LDA to be interpreted in terms of metab...
Raman spectroscopic characterization of urine of normal and oral cancer subjects
Journal of Raman Spectroscopy, 2014
Urine is considered as one of the diagnostically important bio fluids, as it has many metabolites. The distribution and the physiochemical properties of the metabolites may vary during any altered metabolic and pathological conditions. Raman spectroscopy was employed in the characterization of the metabolites of human urine of normal subjects and oral cancer patients in the finger print region (500-1800 cm À1). Principal component analysis-based linear discriminant analysis was performed to discriminate cancer patients from normal subjects. The discriminant analysis classifies the cancer patients from normal subjects with a sensitivity and specificity of 98.6% and 87.1%, respectively, with an overall accuracy of 93.7%.
2021
Highly sensitive and unique biomarkers are needed for early cancer detection. In particular, biomarkers in biofluids can be useful in detecting the existence of a tumor early in the body. The utility of biofluid markers for cancer detection can be enhanced when multiple biofluids are simultaneously biochemically analyzed in order to acquire complementary information for diagnostic purposes. This work aimed at investigating the universal human whole blood and saliva biomarkers for breast cancer screening using machine learning-assisted Raman spectroscopy. Raman spectroscopy was performed in the 393 – 2063 cm-1 region using 785 nm laser excitation. Machine learning-assisted Raman spectroscopy was implemented by performing principal component analysis, independent component analysis, and support vector machine modeling on the Raman spectra in order to extract the underlying multivariate relationships between the observed biochemical alterations. Ten spectral regions were determined: 61...