High-wavenumber FT-Raman spectroscopy for in vivo and ex vivo measurements of breast cancer

Characterization of vaginal Lactobacillus in biologically relevant fluid using surface-enhanced Raman spectroscopy

The native vaginal microbiome plays a crucial role in maintaining vaginal health and disruption can have significant consequences for women during their lifetime. While the composition of the vaginal microbiome is important, current methods for monitoring this community are lacking. Clinically used techniques routinely rely on subjective analysis of vaginal fluid characteristics or time-consuming microorganism culturing. Surface-enhanced Raman spectroscopy (SERS) can aid in filling this gap in timely detection of alterations in the vaginal microbiome as it can discriminate between bacterial species in complex solutions including bacterial mixtures and biofluids. SERS has not previously been applied to study variations in vaginal Lactobacillus, the most common species found in the vaginal microbiome, in complex solutions. Herein, the SERS spectra of Lactobacillus crispatus (L. crispatus) and Lactobacillus iners (L. iners), two of the most common vaginal bacteria, was characterized at physiologically relevant concentrations. Subsequently, the ability of SERS to detect L. crispatus and L. iners in both pure mixtures and when mixed with a synthetic vaginal fluid mimicking solution was determined. In both pure and complex solutions, SERS coupled with partial least squares regression predicted the ratiometric bacterial content with less than 10% error and strong goodness of prediction (Q2 > 0.9). This developed method highlights the applicability of SERS to predict the dominant Lactobacillus in the vaginal micro-environment toward the monitoring of this community.

High wavenumber Raman spectroscopy for intraoperative assessment of breast tumour margins

Optimal oncological results and patient outcomes are achieved in surgery for early breast cancer with breast conserving surgery (BCS) where this is appropriate. A limitation of BCS occurs when cancer is present at, or close, to the resection margin - termed a 'positive' margin - and re-excision is recommended to reduce recurrence rate. This is occurs in 17% of BCS in the UK and there is therefore a critical need for a way to assess margin status intraoperatively to ensure complete excision with adequate margins at the first operation. This study presents the potential of high wavenumber (HWN) Raman spectroscopy to address this. Freshly excised specimens from thirty patients undergoing surgery for breast cancer were measured using a surface Raman probe, and a multivariate classification model to predict normal versus tumour was developed from the data. This model achieved 77.1% sensitivity and 90.8% specificity following leave one patient out cross validation, with the defining features being differences in water content and lipid versus protein content. This demonstrates the feasibility of HWN Raman spectroscopy to facilitate future intraoperative margin assessment at specific locations. Clinical utility of the approach will require further research.

Raman spectroscopy: current applications in breast cancer diagnosis, challenges and future prospects

Despite significant improvements in the way breast cancer is managed and treated, it continues to persist as a leading cause of death worldwide. If detected and diagnosed early, when tumours are small and localised, there is a considerably higher chance of survival. However, current methods for detection and diagnosis lack the required sensitivity and specificity for identifying breast cancer at the asymptomatic or very early stages. Thus, there is a need to develop more rapid and reliable methods, capable of detecting disease earlier, for improved disease management and patient outcome. Raman spectroscopy is a non-destructive analytical technique that can rapidly provide highly specific information on the biochemical composition and molecular structure of samples. In cancer, it has the capacity to probe very early biochemical changes that accompany malignant transformation, even prior to the onset of morphological changes, to produce a fingerprint of disease. This review explores the application of Raman spectroscopy in breast cancer, including discussion on its capabilities in analysing both ex-vivo tissue and liquid biopsy samples, and its potential in vivo applications. The review also addresses current challenges and potential future uses of this technology in cancer research and translational clinical application.

Clinical Spectroscopy: Lost in Translation?

This Focal Point Review paper discusses the developments of biomedical Raman and infrared spectroscopy, and the recent strive towards these technologies being regarded as reliable clinical tools. The promise of vibrational spectroscopy in the field of biomedical science, alongside the development of computational methods for spectral analysis, has driven a plethora of proof-of-concept studies which convey the potential of various spectroscopic approaches. Here we report a brief review of the literature published over the past few decades, with a focus on the current technical, clinical, and economic barriers to translation, namely the limitations of many of the early studies, and the lack of understanding of clinical pathways, health technology assessments, regulatory approval, clinical feasibility, and funding applications. The field of biomedical vibrational spectroscopy must acknowledge and overcome these hurdles in order to achieve clinical efficacy. Current prospects have been overviewed with comment on the advised future direction of spectroscopic technologies, with the aspiration that many of these innovative approaches can ultimately reach the frontier of medical diagnostics and many clinical applications.

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.

Review of Laser Raman Spectroscopy for Surgical Breast Cancer Detection: Stochastic Backpropagation Neural Networks

Laser Raman spectroscopy (LRS) is a highly specific biomolecular technique which has been shown to have the ability to distinguish malignant and normal breast tissue. This paper discusses significant advancements in the use of LRS in surgical breast cancer diagnosis, with an emphasis on statistical and machine learning strategies employed for precise, transparent and real-time analysis of Raman spectra. When combined with a variety of "machine learning" techniques LRS has been increasingly employed in oncogenic diagnostics. This paper proposes that the majority of these algorithms fail to provide the two most critical pieces of information required by the practicing surgeon: a probability that the classification of a tissue is correct, and, more importantly, the expected error in that probability. Stochastic backpropagation artificial neural networks inherently provide both pieces of information for each and every tissue site examined by LRS. If the networks are trained using both human experts and an unsupervised classification algorithm as gold standards, rapid progress can be made understanding what additional contextual data is needed to improve network classification performance. Our patients expect us to not simply have an opinion about their tumor, but to know how certain we are that we are correct. Stochastic networks can provide that information.

Study on the biochemical mechanisms of the micro-wave ablation treatment of lung cancer by ex vivo confocal Raman microspectral imaging

As a highly invasive and the most prevalent malignancy, lung cancer remains the leading cause of cancer-associated mortality worldwide, especially in China. Microwave ablation (MWA) is an effective, safe, and the least invasive ablative treatment modality, which has been increasingly used for the management of unrespectable lung tumors. However, the underlying biochemical mechanisms of MWA treatment remain to be incompletely elucidated. Therefore, to illustrate the complex biochemical responses of lung squamous cell carcinoma (LSCC) to MWA treatment, confocal Raman micro-spectral imaging (CRMI) was applied in combination with multivariate analysis. A total of twelve LSCC tissues were acquired from patients undergoing clinical treatment, and their spectral characteristics were analyzed to determine significant spectral variations following cancer progression and MWA treatment in comparison with healthy lung tissues. Point-scanned Raman datasets were acquired from sectioned tissue samples in both pre-therapy (Pre-MWA group) and post-therapy groups (Post-MWA group) and further analyzed using K-means cluster analysis (KCA) and principal component analysis (PCA) to highlight the detailed compositional variations of the biochemical constituents. The spectral variations of essential amino acids (such as phenylalanine and tryptophan), collagen, and nucleic acids in the cancerous tissues of the Post-MWA group were significantly enhanced compared to those in the Pre-MWA group. The acquired information further confirmed a remarkable increase in the content of nucleic acid, protein, and lipid in the cancerous tissue following MWA treatment and, a comparative spectral imaging investigation indicated that MWA had no noticeable adverse effects on the paracancerous tissues. Thus, the findings not only illustrated the underlying biochemical variability in lung cancer during MWA treatment but also further confirmed the feasibility of a combined analytical procedure for assessing the biochemical responses during thermal ablation, which could be applied to prominently enhance the effectiveness of MWA in lung cancer treatment in clinical settings.

Raman spectroscopy combined with multivariate analysis to study the biochemical mechanism of lung cancer microwave ablation

Lung cancer is the leading cause of death in cancer patients, and microwave ablation (MWA) has been extensively used in clinical treatment. In this study, we characterized the spectra of MWA-treated and untreated lung squamous cell carcinoma (LSCC) tissues, as well as healthy lung tissue, and conducted a preliminary analysis of spectral variations associated with MWA treatment. The results of characteristic spectral analysis of different types of tissues indicated that MWA treatment induces an increase in the content of nucleic acids, proteins, and lipid components in lung cancer tissues. The discriminant model based on the principal component analysis - linear discriminant analysis (PCA-LDA) algorithm together with leave-one-out cross validation (LOOCV) method yield the sensitivities of 90%, 80%, and 96%, and specificities of 86.2%, 93.8%, and 100% among untreated and MWA-treated cancerous tissue, and healthy lung tissue, respectively. These results indicate that Raman spectroscopy combined with multivariate analysis techniques can be used to explore the biochemical response mechanism of cancerous tissue to MWA therapy.

Raman Spectroscopy for Rapid Evaluation of Surgical Margins during Breast Cancer Lumpectomy

Failure to precisely distinguish malignant from healthy tissue has severe implications for breast cancer surgical outcomes. Clinical prognoses depend on precisely distinguishing healthy from malignant tissue during surgery. Laser Raman spectroscopy (LRS) has been previously shown to differentiate benign from malignant tissue in real time. However, the cost, assembly effort, and technical expertise needed for construction and implementation of the technique have prohibited widespread adoption. Recently, Raman spectrometers have been developed for non-medical uses and have become commercially available and affordable. Here we demonstrate that this current generation of Raman spectrometers can readily identify cancer in breast surgical specimens. We evaluated two commercially available, portable, near-infrared Raman systems operating at excitation wavelengths of either 785 nm or 1064 nm, collecting a total of 164 Raman spectra from cancerous, benign, and transitional regions of resected breast tissue from six patients undergoing mastectomy. The spectra were classified using standard multivariate statistical techniques. We identified a minimal set of spectral bands sufficient to reliably distinguish between healthy and malignant tissue using either the 1064 nm or 785 nm system. Our results indicate that current generation Raman spectrometers can be used as a rapid diagnostic technique distinguishing benign from malignant tissue during surgery.

An improved k-nearest neighbour method to diagnose breast cancer

As a molecular and noninvasive detection technology, Raman spectroscopy is promising for use in the early diagnosis of tumors. The SNR of spectra obtained from portable Raman spectrometers is low, which makes classification more difficult. A classification algorithm with a high recognition rate is required. In this paper, an algorithm of entropy weighted local-hyperplane k-nearest-neighbor (EWHK) is proposed for the identification of the spectra. When calculating the weighted distance between the prediction and the sample hyperplane, EWHK introduces the information entropy weighting to improve the algorithm of adaptive weighted k-local hyperplane (AWKH). It can reflect all of the sample information in the classification objectively and improve the classification accuracy. The breast cancer detection experimental results of EWHK showed a significant improvement compared with those of AWKH and k-nearest neighbor (KNN). The EWHK classifier yielded an average diagnostic accuracy of 92.33%, a sensitivity of 93.81%, a specificity of 87.77%, a positive prediction rate of 95.99% and a negative prediction rate of 83.69% during randomized grouping validation. The algorithm is effective for cancer diagnosis.

Ratiometric Raman imaging reveals the new anti-cancer potential of lipid targeting drugs

De novo lipid synthesis is upregulated in cancer cells and inhibiting these pathways has displayed anti-tumour activity. Here we use Raman spectroscopy, focusing solely on high wavenumber spectra, to detect changes in lipid composition in single cells in response to drugs targeting de novo lipid synthesis. Unexpectedly, the beta-blocker propranolol showed selectively towards cancerous PC3 compared to non-cancerous PNT2 prostate cells, demonstrating the potential of this approach to identify new anti-cancer drug leads. A unique and simple ratiometric approach for intracellular lipid investigation is reported using statistical analysis to create phenotypic 'barcodes', a globally applicable strategy for Raman drug-cell studies. High wavenumber spectral analysis is compatible with low cost glass substrates, easily translatable into the cytological work stream. The analytical strength of this technique could have a significant impact on cancer treatment through vastly improved understanding of cancer cell metabolism, and thus guide drug design and enhance personalised medicine strategies.

Diagnosis of Breast Cancer Tissues Using 785 nm Miniature Raman Spectrometer and Pattern Regression

For achieving the development of a portable, low-cost and in vivo cancer diagnosis instrument, a laser 785 nm miniature Raman spectrometer was used to acquire the Raman spectra for breast cancer detection in this paper. However, because of the low spectral signal-to-noise ratio, it is difficult to achieve high discrimination accuracy by using the miniature Raman spectrometer. Therefore, a pattern recognition method of the adaptive net analyte signal (NAS) weight k-local hyperplane (ANWKH) is proposed to increase the classification accuracy. ANWKH is an extension and improvement of K-local hyperplane distance nearest-neighbor (HKNN), and combines the advantages of the adaptive weight k-local hyperplane (AWKH) and the net analyte signal (NAS). In this algorithm, NAS was first used to eliminate the influence caused by other non-target factors. Then, the distance between the test set samples and hyperplane was calculated with consideration of the feature weights. The HKNN only works well for small values of the nearest-neighbor. However, the accuracy decreases with increasing values of the nearest-neighbor. The method presented in this paper can resolve the basic shortcoming by using the feature weights. The original spectra are projected into the vertical subspace without the objective factors. NAS was employed to obtain the spectra without irrelevant information. NAS can improve the classification accuracy, sensitivity, and specificity of breast cancer early diagnosis. Experimental results of Raman spectra detection in vitro of breast tissues showed that the proposed algorithm can obtain high classification accuracy, sensitivity, and specificity. This paper demonstrates that the ANWKH algorithm is feasible for early clinical diagnosis of breast cancer in the future.

Demonstration of the Protein Involvement in Cell Electropermeabilization using Confocal Raman Microspectroscopy

Confocal Raman microspectroscopy was used to study the interaction between pulsed electric fields and live cells from a molecular point of view in a non-invasive and label-free manner. Raman signatures of live human adipose-derived mesenchymal stem cells exposed or not to pulsed electric fields (8 pulses, 1 000 V/cm, 100 μs, 1 Hz) were acquired at two cellular locations (nucleus and cytoplasm) and two spectral bands (600–1 800 cm⁻¹ and 2 800–3 100 cm⁻¹). Vibrational modes of proteins (phenylalanine and amide I) and lipids were found to be modified by the electropermeabilization process with a statistically significant difference. The relative magnitude of four phenylalanine peaks decreased in the spectra of the pulsed group. On the contrary, the relative magnitude of the amide I band at 1658 cm⁻¹ increased by 40% when comparing pulsed and control group. No difference was found between the control and the pulsed group in the high wavenumber spectral band. Our results reveal the modification of proteins in living cells exposed to pulsed electric fields by means of confocal Raman microspectroscopy.

High wavenumber Raman spectroscopy in the characterization of urinary metabolites of normal subjects, oral premalignant and malignant patients

Urine has emerged as one of the diagnostically potential bio fluids, as it has many metabolites. As the concentration and the physiochemical properties of the urinary metabolites may vary under pathological transformation, Raman spectroscopic characterization of urine has been exploited as a significant tool in identifying several diseased conditions, including cancers. In the present study, an attempt was made to study the high wavenumber (HWVN) Raman spectroscopic characterization of urine samples of normal subjects, oral premalignant and malignant patients. It is concluded that the urinary metabolites flavoproteins, tryptophan and phenylalanine are responsible for the observed spectral variations between the normal and abnormal groups. Principal component analysis-based linear discriminant analysis was carried out to verify the diagnostic potentiality of the present technique. The discriminant analysis performed across normal and oral premalignant subjects classifies 95.6% of the original and 94.9% of the cross-validated grouped cases correctly. In the second analysis performed across normal and oral malignant groups, the accuracy of the original and cross-validated grouped cases was 96.4% and 92.1% respectively. Similarly, the third analysis performed across three groups, normal, oral premalignant and malignant groups, classifies 93.3% and 91.2% of the original and cross-validated grouped cases correctly.

Evaluation of the diagnostic potential of ex vivo Raman spectroscopy in gastric cancers: fingerprint versus high wavenumber

The aim of this study was to apply Raman spectroscopy in the high wavenumber (HW) region (2800 to 3000??cm?1) for ex vivo detection of gastric cancer and compare its diagnostic potential with that of the fingerprint (FP) region (800 to 1800??cm?1). Raman spectra were collected in the FP and HW regions to differentiate between normal mucosa (n=38) and gastric cancer (n=37). The distinctive Raman spectral differences between normal and cancer tissues are observed at 853, 879, 1157, 1319, 1338, 1448, and 2932??cm?1 and are primarily related to proteins, lipids, nucleic acids, collagen, and carotenoids in the tissue. In FP and HW Raman spectroscopy for diagnosis of gastric cancer, multivariate diagnostic algorithms based on partial-least-squares discriminant analysis, together with leave-one-sample-out cross validation, yielded diagnostic sensitivities of 94.59% and 81.08%, and specificities of 86.84% and 71.05%, respectively. Receiver operating characteristic analysis further confirmed that the FP region model performance is superior to that of the HW region model. Better differentiation between normal and gastric cancer tissues can be achieved using FP Raman spectroscopy and PLS-DA techniques, but the complementary natures of the FP and HW regions make both of them useful in diagnosis of gastric cancer.

Discrimination between oral cancer and healthy tissue based on water content determined by Raman spectroscopy

Tumor-positive resection margins are a major problem in oral cancer surgery. High-wavenumber Raman spectroscopy is a reliable technique to determine the water content of tissues, which may contribute to differentiate between tumor and healthy tissue. The aim of this study was to examine the use of Raman spectroscopy to differentiate tumor from surrounding healthy tissue in oral squamous cell carcinoma. From 14 patients undergoing tongue resection for squamous cell carcinoma, the water content was determined at 170 locations on freshly excised tongue specimens using the Raman bands of the OH-stretching vibrations (3350-3550 cm(-1)) and of the CH-stretching vibrations (2910-2965 cm(-1)). The results were correlated with histopathological assessment of hematoxylin and eosin stained thin tissue sections obtained from the Raman measurement locations. The water content values from squamous cell carcinoma measurements were significantly higher than from surrounding healthy tissue (p-value < 0.0001). Tumor tissue could be detected with a sensitivity of 99% and a specificity of 92% using a cutoff water content value of 69%. Because the Raman measurements are fast and can be carried out on freshly excised tissue without any tissue preparation, this finding signifies an important step toward the development of an intraoperative tool for tumor resection guidance with the aim of enabling oncological radical surgery and improvement of patient outcome.

Classification for breast cancer diagnosis with Raman spectroscopy

In order to promote the development of the portable, low-cost and in vivo cancer diagnosis instrument, a miniature laser Raman spectrometer was employed to acquire the conventional Raman spectra for breast cancer detection in this paper. But it is difficult to achieve high discrimination accuracy. Then a novel method of adaptive weight k-local hyperplane (AWKH) is proposed to increase the classification accuracy. AWKH is an extension and improvement of K-local hyperplane distance nearest-neighbor (HKNN). It considers the features weights of the training data in the nearest neighbor selection and local hyperplane construction stage, which resolve the basic shortcoming of HKNN works well only for small values of the nearest-neighbor. Experimental results on Raman spectra of breast tissues in vitro show the proposed method can realize high classification accuracy.

Synthesis of apolipoprotein B lipoparticles to deliver hydrophobic/amphiphilic materials

To develop a drug delivery system (DDS), it is critical to address challenging tasks such as the delivery of hydrophobic and amphiphilic compounds, cell uptake, and the metabolic fate of the drug delivery carrier. Low-density lipoprotein (LDL) has been acknowledged as the human serum transporter of natively abundant lipoparticles such as cholesterol, triacylglycerides, and lipids. Apolipoprotein B (apo B) is the only protein contained in LDL, and possesses a binding moiety for the LDL receptor that can be internalized and degraded naturally by the cell. Therefore, synthetic/reconstituting apoB lipoparticle (rABL) could be an excellent delivery carrier for hydrophobic or amphiphilic materials. Here, we synthesized rABL in vitro, using full-length apoB through a five-step solvent exchange method, and addressed its potential as a DDS. Our rABL exhibited good biocompatibility when evaluated with cytotoxicity and cell metabolic response assays, and was stable during storage in phosphate-buffered saline at 4 °C for several months. Furthermore, hydrophobic superparamagnetic iron oxide nanoparticles (SPIONPs) and the anticancer drug M4N (tetra-O-methyl nordihydroguaiaretic acid), used as an imaging enhancer and lipophilic drug model, respectively, were incorporated into the rABL, leading to the formation of SPIONPs- and M4N- containing rABL (SPIO@rABL and M4N@rABL, respectively). Fourier transform infrared spectroscopy suggested that rABL has a similar composition to that of LDL, and successfully incorporated SPIONPs or M4N. SPIO@rABL presented significant hepatic contrast enhancement in T2-weighted magnetic resonance imaging in BALB/c mice, suggesting its potential application as a medical imaging contrast agent. M4N@rABL could reduce the viability of the cancer cell line A549. Interestingly, we developed solution-phase high-resolution transmission electron microscopy to observe both LDL and SPIO@rABL in the liquid state. In summary, our LDL-based DDS, rABL, has significant potential as a novel DDS for hydrophobic and amphiphilic materials, with good cell internalization properties and metabolicity.

Illuminating disease and enlightening biomedicine: Raman spectroscopy as a diagnostic tool

The discovery of the Raman effect in 1928 not only aided fundamental understanding about the quantum nature of light and matter but also opened up a completely novel area of optics and spectroscopic research that is accelerating at a greater rate during the last decade than at any time since its inception. This introductory overview focuses on some of the most recent developments within this exciting field and how this has enabled and enhanced disease diagnosis and biomedical applications. We highlight a small number of stimulating high-impact studies in imaging, endoscopy, stem cell research, and other recent developments such as spatially offset Raman scattering amongst others. We hope this stimulates further interest in this already exciting field, by 'illuminating' some of the current research being undertaken by the latest in a very long line of dedicated experimentalists interested in the properties and potential beneficial applications of light.

Simultaneous fingerprint and high-wavenumber confocal Raman spectroscopy enhances early detection of cervical precancer in vivo

Raman spectroscopy is a vibrational spectroscopic technique capable of nondestructively probing endogenous biomolecules and their changes associated with dysplastic transformation in the tissue. The main objectives of this study are (i) to develop a simultaneous fingerprint (FP) and high-wavenumber (HW) confocal Raman spectroscopy and (ii) to investigate its diagnostic utility for improving in vivo diagnosis of cervical precancer (dysplasia). We have successfully developed an integrated FP/HW confocal Raman diagnostic system with a ball-lens Raman probe for simultaneous acquistion of FP/HW Raman signals of the cervix in vivo within 1 s. A total of 476 in vivo FP/HW Raman spectra (356 normal and 120 precancer) are acquired from 44 patients at clinical colposcopy. The distinctive Raman spectral differences between normal and dysplastic cervical tissue are observed at ~854, 937, 1001, 1095, 1253, 1313, 1445, 1654, 2946, and 3400 cm(-1) mainly related to proteins, lipids, glycogen, nucleic acids and water content in tissue. Multivariate diagnostic algorithms developed based on partial least-squares-discriminant analysis (PLS-DA) together with the leave-one-patient-out, cross-validation yield the diagnostic sensitivities of 84.2%, 76.7%, and 85.0%, respectively; specificities of 78.9%, 73.3%, and 81.7%, respectively; and overall diagnostic accuracies of 80.3%, 74.2%, and 82.6%, respectively, using FP, HW, and integrated FP/HW Raman spectroscopic techniques for in vivo diagnosis of cervical precancer. Receiver operating characteristic (ROC) analysis further confirms the best performance of the integrated FP/HW confocal Raman technique, compared to FP or HW Raman spectroscopy alone. This work demonstrates, for the first time, that the simultaneous FP/HW confocal Raman spectroscopy has the potential to be a clinically powerful tool for improving early diagnosis and detection of cervical precancer in vivo during clinical colposcopic examination.

Raman Spectroscopy for Clinical Oncology

Cancer is one of the leading causes of death throughout the world. Advancements in early and improved diagnosis could help prevent a significant number of these deaths. Raman spectroscopy is a vibrational spectroscopic technique which has received considerable attention recently with regards to applications in clinical oncology. Raman spectroscopy has the potential not only to improve diagnosis of cancer but also to advance the treatment of cancer. A number of studies have investigated Raman spectroscopy for its potential to improve diagnosis and treatment of a wide variety of cancers. In this paper the most recent advances in dispersive Raman spectroscopy, which have demonstrated promising leads to real world application for clinical oncology are reviewed. The application of Raman spectroscopy to breast, brain, skin, cervical, gastrointestinal, oral, and lung cancers is reviewed as well as a special focus on the data analysis techniques, which have been employed in the studies.