Discrimination of prostate carcinoma from benign prostate tissue fragments in vitro by estimating the gross biochemical alterations through Raman spectroscopy

Raman spectroscopy system for real-time diagnosis of clinically significant prostate cancer tissue

Prostate cancer (PCa) is a significant healthcare problem worldwide. Current diagnosis and treatment methods are limited by a lack of precise in vivo tissue analysis methods. Real-time cancer identification and grading could dramatically improve current protocols. Here, we report the testing of a thin optical probe using Raman spectroscopy (RS) and classification methods to detect and grade PCa accurately in real-time. We present the first clinical trial on fresh ex vivo biopsy cores from an 84 patient cohort. Findings from 2395 spectra measured on 599 biopsy cores show high accuracy for diagnosing and grading PCa. We can detect clinically significant PCa from benign and clinically insignificant PCa with 90% sensitivity and 80.2% specificity. We also demonstrate the ability to differentiate cancer grades with 90% sensitivity and specificity ≥82.8%. This work demonstrates the utility of RS for real-time PCa detection and grading during routine transrectal biopsy appointments.

Reflective FT-NIR and SERS studies of HER-II breast cancer biomarker using plasmonic-active nanostructured thin film immobilized oriented antibody

We describe the fabrication of plasmonic-active nanostructured thin film substrate as a label-free surface-enhanced Raman scattering (SERS)-based biosensor immobilized covalently with monoclonal HER-II antibody (mAb) to detect overexpressed HER-II as a biomarker in breast cancer serum (BCS). Oriented conjugation of mAb via hydrazone linkage to provide higher mAb accessibility was characterized by UV-vis and reflective Fourier transform near-infrared (FT-NIR) spectroscopic techniques. The interaction of BCS with mAb was studied by FT-NIR and nonresonant SERS at 637 nm. The results showed detection of glycoprotein content at different laser powers including a rise in amino acid and glycan content with varying results at higher power. With nonresonant SERS we observed nonlinear behavior of peak intensity. Analysis of variance was implemented to determine the effect of laser power which was found not to be a contributing factor. However, at the nanoscale, factors including the heating effect and aggregation of molecules can contribute to the nonlinearity of peak intensity.

Non-invasive optical characterization and detection of CA 15-3 breast cancer biomarker in blood serum using monoclonal antibody-conjugated gold nanourchin and surface-enhanced Raman scattering

A proof-of-concept of colloidal surface-enhanced Raman scattering (SERS) substrate for rapid selective detection of overexpressed CA 15-3 biomarker in breast cancer serum (BCS) is suggested using PEGylated gold nanourchins (GNUs) conjugated with anti-CA 15-3 monoclonal antibody (mAb). UV-vis spectroscopy provided conformational information about mAb where the initial aromatic amino acid peak was red-shifted from 271 to 291 nm. The fluorescence peak of tyrosine in mAb was reduced by ≈ 77%, and red-shifted by ≈ 3 nm after incubation in BCS. Fourier transform near-infrared spectroscopy and SERS were used to study the composition and the molecular structure of the mAb and BCS. Some of the most dominant Raman shifts after GNU-PEG-mAb interaction with BCS are 498, 736, 818, 1397, 1484, 2028, 2271, and 3227 cm-1 mainly corresponding to C-N-C in amines, vibrational modes of amino acids, C-H out-of-plane bend, C-O stretching carboxylic acid, the vibrational mode in phospholipids, NH3+ amine salt, C≡N stretching in nitriles, and O-H stretching. The intensity of SERS signals varied per trial due to the statistical behavior of GNU in BCS, agglomeration, laser power, and the heating effect. Despite very small amount of plasmonic heating, the result of the ANOVA test demonstrated that under our experimental conditions, the heating effect on signal variation is negligible and that the differences in the laser power are insignificant for all SERS observations (p > 0.6); thus, other parameters are responsible. The absorbance of mAb-conjugated GNU was decreased after five minutes of irradiation at 8 mW in the BCS.

Application and Progress of Raman Spectroscopy in Male Reproductive System

Raman spectroscopy is a fast-developing, unmarked, non-invasive, non-destructive technique which allows for real-time scanning and sampling of biological samples in situ, reflecting the subtle biochemical composition alterations of tissues and cells through the variations of spectra. It has great potential to identify pathological tissue and provide intraoperative assistance in clinic. Raman spectroscopy has made many exciting achievements in the study of male reproductive system. In this review, we summarized literatures about the application and progress of Raman spectroscopy in male reproductive system from PubMed and Ovid databases, using MeSH terms associated to Raman spectroscopy, prostate, testis, seminal plasma and sperm. The existing challenges and development opportunities were also discussed and prospected.

Raman spectroscopy and artificial intelligence to predict the Bayesian probability of breast cancer

This study addresses the core issue facing a surgical team during breast cancer surgery: quantitative prediction of tumor likelihood including estimates of prediction error. We have previously reported that a molecular probe, Laser Raman spectroscopy (LRS), can distinguish healthy and tumor tissue. We now report that combining LRS with two machine learning algorithms, unsupervised k-means and stochastic nonlinear neural networks (NN), provides rapid, quantitative, probabilistic tumor assessment with real-time error analysis. NNs were first trained on Raman spectra using human expert histopathology diagnostics as gold standard (74 spectra, 5 patients). K-means predictions using spectral data when compared to histopathology produced clustering models with 93.2-94.6% accuracy, 89.8-91.8% sensitivity, and 100% specificity. NNs trained on k-means predictions generated probabilities of correctness for the autonomous classification. Finally, the autonomous system characterized an extended dataset (203 spectra, 8 patients). Our results show that an increase in DNA|RNA signal intensity in the fingerprint region (600-1800 cm-1) and global loss of high wavenumber signal (2800-3200 cm-1) are particularly sensitive LRS warning signs of tumor. The stochastic nature of NNs made it possible to rapidly generate multiple models of target tissue classification and calculate the inherent error in the probabilistic estimates for each target.

Diagnostic accuracy of Raman spectroscopy for prostate cancer: a systematic review and meta-analysis

Background

Although various studies have been conducted to demonstrate the possibility of Raman spectroscopy (RS) as a diagnostic tool for prostate cancer (PC), it is difficult to use it in the real clinical area because of imitations in various research processes. Therefore, we did a systematic review and meta-analysis about the accuracy in diagnostic use of RS for PC.

Methods

A literature search was done using PubMed, Embase, and Cochrane library databases in March 2019 to analyze the accuracy of RS for diagnosis of PC. The accuracy of RS for diagnosis of PC was evaluated by means of pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and summary receiver operating characteristic (SROC).

Results

Five studies were included for qualitative analysis by screening the remaining articles according to the inclusion and exclusion criteria by means of a systematic review. The pooled sensitivity and specificity of RS were 0.89 (95% CI: 0.87-0.91) and 0.91 (95% CI: 0.89-0.93), respectively. The overall PLR and NLR were 9.12 (95% CI: 4.15-20.08) and 0.14 (95% CI: 0.07-0.29), respectively. The DOR of RS demonstrated high accuracy (73.32; 95% CI: 18.43-291.73). The area under the curves (AUCs) of SROC curves was 0.93.

Conclusions

RS is an optical diagnostic method with high potential for diagnosis and grading of PC and has advantages of real-time and convenient use. In order to consider real-time use of RS in an actual clinical setting, more studies for standardization and generalization of RS performance and analytical method must be conducted.

Use of Raman spectroscopy to evaluate the biochemical composition of normal and tumoral human brain tissues for diagnosis

Raman spectroscopy was used to identify biochemical differences in normal brain tissue (cerebellum and meninges) compared to tumors (glioblastoma, medulloblastoma, schwannoma, and meningioma) through biochemical information obtained from the samples. A total of 263 spectra were obtained from fragments of the normal cerebellum (65), normal meninges (69), glioblastoma (28), schwannoma (8), medulloblastoma (19), and meningioma (74), which were collected using the dispersive Raman spectrometer (830 nm, near infrared, output power of 350 mW, 20 s exposure time to obtain the spectra), coupled to a Raman probe. A spectral model based on least squares fitting was developed to estimate the biochemical concentration of 16 biochemical compounds present in brain tissue, among those that most characterized brain tissue spectra, such as linolenic acid, triolein, cholesterol, sphingomyelin, phosphatidylcholine, β-carotene, collagen, phenylalanine, DNA, glucose, and blood. From the biochemical information, the classification of the spectra in the normal and tumor groups was conducted according to the type of brain tumor and corresponding normal tissue. The classification used in discrimination models were (a) the concentrations of the biochemical constituents of the brain, through linear discriminant analysis (LDA), and (b) the tissue spectra, through the discrimination by partial least squares (PLS-DA) regression. The models obtained 93.3% discrimination accuracy through the LDA between the normal and tumor groups of the cerebellum separated according to the concentration of biochemical constituents and 94.1% in the discrimination by PLS-DA using the whole spectrum. The results obtained demonstrated that the Raman technique is a promising tool to differentiate concentrations of biochemical compounds present in brain tissues, both normal and tumor. The concentrations estimated by the biochemical model and all the information contained in the Raman spectra were both able to classify the pathological groups.

Raman spectroscopy and chemometrics: A potential universal method for diagnosing cancer

Cancer is the second-leading cause of death worldwide. It affects an unfathomable number of people, with almost 16 million Americans currently living with it. While many cancers can be detected, current diagnostic efforts exhibit definite room for improvement. It is imperative that a person be diagnosed with cancer as early on in its progression as possible. An earlier diagnosis allows for the best treatment and intervention options available to be presented. Unfortunately, existing methods for diagnosing cancer can be expensive, invasive, inconclusive or inaccurate, and are not always made during initial stages of the disease. As such, there is a crucial unmet need to develop a singular universal method that is reliable, cost-effective, and non-invasive and can diagnose all forms of cancer early-on. Raman spectroscopy in combination with advanced statistical analysis is offered here as a potential solution for this need. This review covers recently published research in which Raman spectroscopy was used for the purpose of diagnosing cancer. The benefits and the risks of the methodology are presented; however, there is overwhelming evidence that suggests Raman spectroscopy is highly suitable for becoming the first universal method to be used for diagnosing cancer.

Surface-enhanced Raman spectroscopy of serum accurately detects prostate cancer in patients with prostate-specific antigen levels of 4-10 ng/mL

The surface-enhanced Raman spectroscopy (SERS) of blood serum was investigated to differentiate between prostate cancer (PCa) and benign prostatic hyperplasia (BPH) in males with a prostate-specific antigen level of 4-10 ng/mL, so as to reduce unnecessary biopsies. A total of 240 SERS spectra from blood serum were acquired from 40 PCa subjects and 40 BPH subjects who had all received prostate biopsies and were given a pathological diagnosis. Multivariate statistical techniques, including principal component analysis (PCA) and linear discriminant analysis (LDA) diagnostic algorithms, were used to analyze the spectra data of serum from patients in control (CTR), PCa and BPH groups; results offered a sensitivity of 97.5%, a specificity of 100.0%, a precision of 100.0% and an accuracy of 99.2% for CTR; a sensitivity of 90.0%, a specificity of 97.5%, a precision of 94.7% and an accuracy of 98.3% for BPH; a sensitivity of 95.0%, a specificity of 93.8%, a precision of 88.4% and an accuracy of 94.2% for PCa. Similarly, this technique can significantly differentiate low- and high-risk PCa with an accuracy of 92.3%, a specificity of 95% and a sensitivity of 89.5%. The results suggest that analyzing blood serum using SERS combined with PCA-LDA diagnostic algorithms is a promising clinical tool for PCa diagnosis and assessment.

Could the bone mineral density (T-score) be correlated with the Raman spectral features of keratin from women's nails and be used to predict osteoporosis?

Osteoporosis is a disease with great importance in current public health due to the associated risk of fracture; therefore, a rapid and accurate diagnosis becomes increasingly important. Recent literature has described a possible relationship between the changes in the organic phase of bone and the changes in nail keratin measured through Raman spectroscopy, aiming at the development of a standard for measuring bone quality and fracture risk both rapid and accurately. This work evaluated the correlation between the bone mineral density (BMD) scores of women with and without osteoporotic disease with the changes in the Raman spectra of the nail keratin, by assessing the intensity of the peak at 510 cm(-1) (S-S bridge) and the scores of principal component analysis (PCA), correlated with the values of BMD measured at the lumbar and hip. Raman spectra of ex vivo fingernails of 213 women were obtained by means of a dispersive Raman spectrometer (830 nm, 300 mW, in the spectral range between 400 and 1,800 cm(-1)). Peak intensities at ∼510 cm(-1) (assigned to the keratin S-S bridge) were measured, and the scores of first principal component loading vectors were calculated. Results showed no differences in the mean Raman spectra of nails of groups with and without osteoporosis. No correlation was found between the BMD scores and both the intensities of the 510 cm(-1) peak and the scores of the first four principal component vectors. Results suggest that BMD and fracture risk could not be assessed by the nail keratin features.