Biochemical correlation of Raman spectra of normal, benign and malignant breast tissues: A spectral deconvolution study

Label-free Raman spectroscopy reveals tumor microenvironmental changes induced by intermittent fasting for the prevention of breast cancer in animal model

The development of tools that can provide a holistic picture of the evolution of the tumor microenvironment in response to intermittent fasting on the prevention of breast cancer is highly desirable. Here, we show, for the first time, the use of label-free Raman spectroscopy to reveal biomolecular alterations induced by intermittent fasting in the tumor microenvironment of breast cancer using a dimethyl-benzanthracene induced rat model. To quantify biomolecular alterations in the tumor microenvironment, chemometric analysis of Raman spectra obtained from untreated and treated tumors was performed using multivariate curve resolution-alternative least squares and support vector machines. Raman measurements revealed remarkable and robust differences in lipid, protein, and glycogen content prior to morphological manifestations in a dynamically changing tumor microenvironment, consistent with the proteomic changes observed by quantitative mass spectrometry. Taken together with its non-invasive nature, this research provides prospective evidence for the clinical translation of Raman spectroscopy to identify biomolecular variations in the microenvironment induced by intermittent fasting for the prevention of breast cancer, providing new perspectives on the specific molecular effects in the tumorigenesis of breast cancer.

Quantitative 1H NMR with global spectral deconvolution approach for the determination of gamma-aminobutyric acid in Chinese yam (Dioscorea polystachya Turczaninow)

We developed a quantitative proton nuclear magnetic resonance (qNMR) with global spectral deconvolution (GSD) method to determine the gamma-aminobutyric acid content in Chinese yam with the proton signal at δ_H 2.30. Trimethylsilyl-2,2,3,3-tetradeuteropropionic acid sodium salt was set as the internal standard. The method was validated and showed admissible stability, repeatability, and precision. Compared to the traditional high-performance liquid chromatography method, this method did not involve tedious pre-treatment and expensive standard. Compared to ordinary qNMR, GSD algorithm could effectively remove the effect of noise, baseline distortions and signal overlapping. Overall, qNMR with GSD method is a rapid, simple and reliable method to quantitatively determine functional metabolites even overlapped with other compounds in herbs or foods.

Confocal Raman microspectral analysis and imaging of the drug response of osteosarcoma to cisplatin

Confocal Raman microspectral analysis and imaging were used to elucidate the drug response of osteosarcoma (OS) to cisplatin. Raman spectral data were obtained from OS cells that were untreated (UT group) and treated with 20 µM (20T group) and 40 µM (40T group) cisplatin for 24 hours. Statistical analysis of the changes in specific Raman signals was performed using a one-way ANOVA and multiple Tukey's honest significant difference (HSD) post hoc tests. Principal component analysis-linear discriminant analysis (PCA-LDA) was used to highlight the featured cellular drug responses based on the obtained spectral information. For spectral imaging analysis, k-means cluster analysis (KCA) was adopted to clarify the effect of cisplatin dose changes on the subcellular structure and its biochemical composition. The results suggest that the major biochemical changes induced by cisplatin in OS cells undergoing apoptosis are reduced protein and nucleic acid content. Through univariate analysis, the changes in the distribution of nucleic acids in OS cells induced by different doses of cisplatin were obtained. The combination of Raman spectroscopy and multivariate analysis shows that cisplatin mainly acts on the nucleus and causes changes in the secondary structure of proteins. These results indicate that Raman imaging technology has the potential to offer the basis of dose optimization for personalized cancer treatment by helping to understand in vitro cellular drug interactions.

Spectral Analysis and Deconvolution of the Amide I Band of Proteins Presenting with High-Frequency Noise and Baseline Shifts

The challenge of deriving quantitative information from the infrared spectra of proteins arises from the large number of secondary structures and amino acid side-chain functional groups that all contribute to the spectral intensity, such as within the amide I band (1600-1700 cm^-1). The band is invariably heavily convoluted from overlapping spectral features, thereby making interpretation difficult such that deconvolution is usually required. This work critically examines the methods available to deconvolute the spectra and assesses the commonly used methods and algorithms applied to vibrational spectra for smoothing and peak identification. We show that unless their spectra have very high signal-to-noise ratios, quantitative analysis to decipher protein constituents is not feasible. The advantages and disadvantages of spectral smoothing using adjacent averaging, the Savitzky-Golay filter and the fast Fourier transform filter are examined in detail. The use of derivative spectra to identify peaks is described with particular reference to the influence and reduction of interfering water bands in the amide I region. The reliability of band narrowing techniques such as second-derivative analysis or Fourier deconvolution that lead to the identification of the contributing protein peaks is investigated. Both methods are shown to be limited in their capacity to resolve features with very similar frequencies. Additionally, the presence of narrow bands arising from high-frequency noise whether from atmospheric water vapor, acoustic vibrations, or electrical interference results in both methods becoming increasingly unusable as narrow bands are preferentially enhanced at the expense of broad ones such as the amide I bands. An optimal strategy is critically developed to allow accurate determination and quantification of protein constituents and their conformations. Additionally, quantitative methods are proposed to account for baseline shifts, which would otherwise introduce significant errors in similarity indices.

Molecular Effects and Tissue Penetration Depth of Physical Plasma in Human Mucosa Analyzed by Contact- and Marker-Independent Raman Microspectroscopy

Noninvasive epithelial tissue treatment with cold atmospheric plasma (CAP) is a promising option for local treatment of chronic inflammatory and precancerous lesions as well as various mucosal cancer diseases. Atmospheric pressure plasma jets (APPJ) are well-characterized and medically approved plasma sources. There are numbers of medically approved plasma sources for the treatment of epithelial diseases; however, little is known about the biochemical effects of CAP at the plasma-tissue interface. Furthermore, the actual penetration depth of CAP into tissue is currently unclear. Noninvasive and marker-independent Raman microspectroscopy was employed to assess the molecular effects of CAP on single cells and primary human cervical tissue samples. CAP treatment showed immediate and persisting changes of specific molecular tissue components determined by multivariate analysis. Raman imaging identified CAP-dependent changes in the morphology of the tissue, as well as molecular tissue components. The expression of the different components was not significantly altered within 24 h of incubation. DNA and lipids showed the strongest changes upon CAP treatment, which were traced to the basal cell layer of cervical epithelium, corresponding to an average functional plasma penetration depth of roughly 270 μm. In this study, Raman microspectroscopy is shown to be a promising method for molecular single-cell and solid tissue characterization. Regarding CAP treatment of tissues, Raman microspectroscopy could be suitable for the screening of biological mechanisms as well as for future contact- and marker-independent monitoring of plasma tissue effects.

Breast Tumor Analysis Using Shifted-Excitation Raman Difference Spectroscopy (SERDS)

We used a shifted-excitation Raman difference spectroscopy method for the ex vivo classification of resected and formalin-fixed breast tissue samples as normal (healthy) tissue, fibroadenoma, or invasive carcinoma. We analyzed 8 tissue samples containing invasive carcinoma that were surrounded by normal tissue and 3 tissue samples with fibroadenoma only. We made various measurement sites on various tissue samples, in total 240 measurements for each type of tissue. Although the acquired raw spectra contain enough information to clearly differentiate between normal and tumor (fibroadenoma and invasive carcinoma) tissue, the differentiation between fibroadenoma and invasive carcinoma was possible only after the shifted-excitation Raman difference spectroscopy isolation of pure Raman spectra from the heavily fluorescence interfered raw spectra. We used 784 and 785 nm as excitation wavelengths for the shifted-excitation Raman difference spectroscopy method. The differences in the obtained pure Raman spectra are assigned to the different chemical compositions of normal breast tissue, fibroadenoma, and invasive breast carcinoma. Principal component analysis and linear discriminant analysis showed excellent classification results in the Raman shift range between 1000 and 1800 cm-1. Invasive breast carcinoma was identified with 99.15% sensitivity, and the absence of invasive carcinoma was identified with 90.40% specificity. Tumor tissue in tumor-containing tissue was identified with 100% sensitivity, and the absence of tumor in no-tumor containing tissue was identified with 100% specificity. As gold standard for the determination of the sensitivity and the specificity, we considered the conventional histopathological classification. In summary, shifted-excitation Raman difference spectroscopy could be potentially very useful to support histopathological diagnosis in breast pathology.

Proof-of-concept Raman spectroscopy study aimed to differentiate thyroid follicular patterned lesions

Inter-observer variability and cancer over-diagnosis are emerging clinical problems, especially for follicular patterned thyroid lesions. This challenge strongly calls for a new clinical tool to reliably identify neoplastic lesions and to improve the efficiency of differentiation between benign and malignant neoplasms, especially considering the increased diagnosis of small carcinomas and the growing number of thyroid nodules. In this study, we employed a Raman spectroscopy (RS) microscope to investigate frozen thyroid tissues from fourteen patients with thyroid nodules. To generate tissue classification models, a supervised statistical analysis of the Raman spectra was performed. The results obtained demonstrate an accuracy of 78% for RS based diagnosis to discriminate between normal parenchyma and follicular patterned thyroid nodules, and 89% accuracy - for very challenging follicular lesions (carcinoma versus adenoma). RS translation into intraoperative diagnosis of frozen sections and in preoperative analysis of biopsies can be very helpful to reduce unnecessary surgery in patients with indeterminate cytological reports.

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.

Wavenumber selection based analysis in Raman spectroscopy improves skin cancer diagnostic specificity

Real-time Raman spectroscopy can be used to assist in assessing skin lesions suspicious for cancer. Most of the diagnostic algorithms are based on full band of the Raman spectra, either in the fingerprint region or the high wavenumber region. In this paper we explored wavenumber selection based analysis in Raman spectroscopy for skin cancer diagnosis. Wavenumber selection was implemented using windows of wavenumber and leave-one-out cross-validated stepwise regression or least and shrinkage selection operator (LASSO). The diagnostic algorithms were then generated from the selected windows of wavenumber using multivariate statistical analyses, including principal component and general discriminate analysis (PC-GDA) and partial least squares (PLS). In total a combined cohort of 645 confirmed lesions from 573 patients encompassing skin cancers, precancers and benign skin lesions were included, which were divided into training cohort (n = 518) and testing cohort (n = 127) according to the measurement time. It was found that the area under the receiver operating characteristic curve (ROC) was improved from 0.861-0.891 to 0.891-0.911 and the diagnostic specificity for fixed sensitivity 0.99-0.90 was improved from 0.17-0.65 to 0.20-0.75 with wavenumber selection based analysis.

Preliminary study of differentiating smears from cancerous and non-cancerous nasopharyngeal tissue using confocal Raman spectroscopy

PURPOSE

Current practice for diagnosing nasopharyngeal carcinoma (NPC) is based on invasive tissue biopsy. This study aims to explore the feasibility of using Raman spectroscopy to differentiate cancerous and non-cancerous nasopharyngeal tissue smears, expecting to realize minimal invasive diagnosis using smears from in vivo mucosa tissue by Raman spectroscopy.

METHODS

Biopsy tissue smears were acquired from 74 patients with pathologically diagnosed nasopharyngeal diseases and measured using confocal Raman spectroscopy.

RESULTS

Both fingerprint region and high wavenumber Raman spectra were acquired with distinguish features. Multivariate statistical analysis was used to differentiate cancerous and non-cancerous groups, achieving a diagnostic sensitivity of 87.2 and specificity of 85.7 % for differentiating NPC from nasopharyngeal non-cancerous smears.

CONCLUSIONS

This work indicates that the method has a unique advantage in microanalysis for tissue smears which may provide a promising minimal invasive (or noninvasive) diagnosing tool for cancer diagnosis.

Label-free DNA imaging in vivo with stimulated Raman scattering microscopy

Label-free DNA imaging is highly desirable in biology and medicine to perform live imaging without affecting cell function and to obtain instant histological tissue examination during surgical procedures. Here we show a label-free DNA imaging method with stimulated Raman scattering (SRS) microscopy for visualization of the cell nuclei in live animals and intact fresh human tissues with subcellular resolution. Relying on the distinct Raman spectral features of the carbon-hydrogen bonds in DNA, the distribution of DNA is retrieved from the strong background of proteins and lipids by linear decomposition of SRS images at three optimally selected Raman shifts. Based on changes on DNA condensation in the nucleus, we were able to capture chromosome dynamics during cell division both in vitro and in vivo. We tracked mouse skin cell proliferation, induced by drug treatment, through in vivo counting of the mitotic rate. Furthermore, we demonstrated a label-free histology method for human skin cancer diagnosis that provides comparable results to other conventional tissue staining methods such as H&E. Our approach exhibits higher sensitivity than SRS imaging of DNA in the fingerprint spectral region. Compared with spontaneous Raman imaging of DNA, our approach is three orders of magnitude faster, allowing both chromatin dynamic studies and label-free optical histology in real time.

A preliminary Raman spectroscopic study of urine: diagnosis of breast cancer in animal models

Analysis of urine by Raman spectroscopy (RS) as an alternative screening and diagnostic tool for breast cancer..

Rapid detection of nasopharyngeal cancer using Raman spectroscopy and multivariate statistical analysis

Optical spectroscopic techniques, including Raman spectroscopy, have shown promise for in vivo cancer diagnostics in a variety of organs. In this study, the potential use of a home-made Raman spectral system with a millimeter order excitation laser spot size combined with a multivariate statistical analysis for the rapid detection and discrimination of nasopharyngeal cancer from normal nasopharyngeal tissue was evaluated. Raman scattering signals were acquired from 16 normal and 32 nasopharyngeal carcinoma tissue samples. Linear discriminant analysis (LDA) based on principal component analysis (PCA) and partial least squares (PLS) were employed to generate diagnostic algorithms for the classification of different nasopharyngeal tissue types. Spectral differences in Raman spectra between the two types of tissues were revealed; the normalized intensities of Raman peaks at 1,001, 1,207 and 1,658 cm^-1 were more intense for nasopharyngeal carcinoma tissue compared to normal tissue, while Raman bands at 848, 936 and 1,446 cm^-1 were stronger in normal nasopharyngeal samples. The PCA-LDA algorithm together with leave-one-out cross validation yields a diagnostic sensitivity of 81% and a specificity of 87%, while the PLS method coupled with subwindow permutation analysis improves the diagnostic sensitivity and specificity to 85 and 88%, respectively. Therefore, Raman spectroscopy combined with PCA-LDA/PLS demonstrated good potential for improving the clinical diagnosis of nasopharyngeal cancers.

Exploring type II microcalcifications in benign and premalignant breast lesions by shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS)

The characteristics of type II microcalcifications in fibroadenoma (FB), atypical ductal hyperplasia (ADH), and ductal carcinoma in situ (DCIS) breast tissues has been analyzed by the fingerprint features of Raman spectroscopy. Fresh breast tissues were first handled to frozen sections and then they were measured by normal Raman spectroscopy. Due to inherently low sensitivity of Raman scattering, Au@SiO2 shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS) technique was utilized. A total number of 71 Raman spectra and 70 SHINERS spectra were obtained from the microcalcifications in benign and premalignant breast tissues. Principal component analysis (PCA) was used to distinguish the type II microcalcifications between these tissues. This is the first time to detect type II microcalcifications in premalignant (ADH and DCIS) breast tissue frozen sections, and also the first time SHINERS has been utilized for breast cancer detection. Conclusions demonstrated in this paper confirm that SHINERS has great potentials to be applied to the identification of breast lesions as an auxiliary method to mammography in the early diagnosis of breast cancer.

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.

Vibrational biospectroscopy coupled with multivariate analysis extracts potentially diagnostic features in blood plasma/serum of ovarian cancer patients

Despite numerous advances in "omics" research, early detection of ovarian cancer still remains a challenge. The aim of this study was to determine whether attenuated total reflection Fourier-transform infrared (ATR-FTIR) or Raman spectroscopy could characterise alterations in the biomolecular signatures of human blood plasma/serum obtained from ovarian cancer patients compared to non-cancer controls. Blood samples isolated from ovarian cancer patients (n = 30) and healthy controls (n = 30) were analysed using ATR-FTIR spectroscopy. For comparison, a smaller cohort of samples (n = 8) were analysed using an InVia Renishaw Raman spectrometer. Resultant spectra were pre-processed prior to being inputted into principal component analysis (PCA) and linear discriminant analysis (LDA). Statistically significant differences (P < 0.001) were observed between spectra of ovarian cancer versus control subjects for both biospectroscopy methods. Using a support vector machine classifier for Raman spectra of blood plasma, a diagnostic accuracy of 74% was achieved, while the same classifier showed 93.3% accuracy for IR spectra of blood plasma. These observations suggest that a biospectroscopy approach could be applied to identify spectral alterations associated with the presence of insidious ovarian cancer.

Application of Raman spectroscopy to identify microcalcifications and underlying breast lesions at stereotactic core needle biopsy

Microcalcifications are a feature of diagnostic significance on a mammogram and a target for stereotactic breast needle biopsy. Here, we report development of a Raman spectroscopy technique to simultaneously identify microcalcification status and diagnose the underlying breast lesion, in real-time, during stereotactic core needle biopsy procedures. Raman spectra were obtained ex vivo from 146 tissue sites from fresh stereotactic breast needle biopsy tissue cores from 33 patients, including 50 normal tissue sites, 77 lesions with microcalcifications, and 19 lesions without microcalcifications, using a compact clinical system. The Raman spectra were modeled on the basis of the breast tissue components, and a support vector machine framework was used to develop a single-step diagnostic algorithm to distinguish normal tissue, fibrocystic change (FCC), fibroadenoma, and breast cancer, in the absence and presence of microcalcifications. This algorithm was subjected to leave-one-site-out cross-validation, yielding a positive predictive value, negative predictive value, sensitivity, and specificity of 100%, 95.6%, 62.5%, and 100% for diagnosis of breast cancer (with or without microcalcifications) and an overall accuracy of 82.2% for classification into specific categories of normal tissue, FCC, fibroadenoma, or breast cancer (with and without microcalcifications). Notably, the majority of breast cancers diagnosed are ductal carcinoma in situ (DCIS), the most common lesion associated with microcalcifications, which could not be diagnosed using previous Raman algorithm(s). Our study shows the potential of Raman spectroscopy to concomitantly detect microcalcifications and diagnose associated lesions, including DCIS, and thus provide real-time feedback to radiologists during such biopsy procedures, reducing nondiagnostic and false-negative biopsies.

Raman spectroscopic investigation of the chemopreventive response of naringenin and its nanoparticles in DMBA-induced oral carcinogenesis

Raman spectroscopy is a vibrational spectroscopic technique that can be used to optically probe the biomolecular changes associated with tumor progression. The aim of the present study is to investigate the biomolecular changes in chemopreventive response of prepared naringenin-loaded nanoparticles (NARNPs) relative to efficacy of free naringenin (NAR) during 7,12-dimethyl benz(a)anthracene (DMBA)-induced oral carcinogenesis by Fourier Transform Raman (FT-Raman) spectroscopy. Oral squamous cell carcinoma (OSCC) was developed in the buccal pouch of golden Syrian hamsters by painting with 0.5% DMBA in liquid paraffin three times a week for 14weeks. Raman spectra differed significantly between the control and tumor tissues, with tumors showing higher percentage signals for nucleic acids, phenylalanine and tryptophan and a lower in the percentage of phospholipids. Moreover, oral administration of free NAR and NARNPs significantly increased phospholipids and decreased the levels of tryptophan, phenylalanine and nucleic acid contents. On a comparative basis, NARNPs was found to have a more potent antitumor effect than free NAR in completely preventing the formation of squamous cell carcinoma and in improving the biochemical status to a normal range in DMBA-induced oral carcinogenesis. The present study further suggest that Raman spectroscopy could be a valuable tool for rapid and sensitive detection of specific biomolecular changes in response to chemopreventive agents.

Precision of Raman spectroscopy measurements in detection of microcalcifications in breast needle biopsies

Microcalcifications are an early mammographic sign of breast cancer and a target for stereotactic breast needle biopsy. We developed Raman spectroscopy decision algorithms to detect breast microcalcifications, based on fit coefficients (FC) derived by modeling tissue Raman spectra as a linear combination of the Raman spectra of 9 chemical and morphologic components of breast tissue. However, little or no information is available on the precision of such measurements and its effect on the ability of Raman spectroscopy to make predictions for breast microcalcification detection. Here we report the precision, that is, the closeness of agreement between replicate Raman spectral measurements--and the model FC derived from them--obtained ex vivo from fresh breast biopsies from patients undergoing stereotactic breast needle biopsy, using a compact clinical Raman system. The coefficients of variation of the model FC averaged 0.03 for normal breast tissue sites, 0.12 for breast lesions without, and 0.22 for breast lesions with microcalcifications. Imprecision in the FC resulted in diagnostic discordance among replicates only for line-sitters, that is, tissue sites with FC values near the decision line or plane. The source of this imprecision and their implications for the use of Raman spectroscopy for guidance of stereotactic breast biopsies for microcalcifications are also discussed. In summary, we conclude that the precision of Raman spectroscopy measurements in breast tissue obtained using our compact clinical system is more than adequate to make accurate and repeatable predictions of microcalcifications in breast tissue using decision algorithms based on model FC. This provides strong evidence of the potential of Raman spectroscopy guidance of stereotactic breast needle biopsies for microcalcifications.

Discriminating adenocarcinoma from normal colonic mucosa through deconvolution of Raman spectra

In this work, we considered the feasibility of Raman spectroscopy for discriminating between adenocarcinomatous and normal mucosal formalin-fixed colonic tissues. Unlike earlier studies in colorectal cancer, a spectral deconvolution model was implemented to derive spectral information. Eleven samples of human colon were used, and 55 spectra were analyzed. Each spectrum was resolved into 25 bands from 975 to 1720 cm(-1), where modes of proteins, lipids, and nucleic acids are observed. From a comparative study of band intensities, those presenting higher differences between tissue types were correlated to biochemical assignments. Results from fitting procedure were further used as inputs for linear discriminant analysis, where combinations of band intensities and intensity ratios were tested, yielding accuracies up to 81%. This analysis yields objective discriminating parameters after fitting optimization. The bands with higher diagnosis relevance detected by spectra deconvolution enable to confine the study to some spectral regions instead of broader ranges. A critical view upon limitations of this approach is presented, along with a comparison of our results to earlier ones obtained in fresh colonic tissues. This enabled to assess the effect of formalin fixation in colonic tissues, and determine its relevance in the present analysis.

Sensitivity of Raman spectroscopy to normal patient variability

Many groups have used Raman spectroscopy for diagnosing cervical dysplasia; however, there have been few studies looking at the effect of normal physiological variations on Raman spectra. We assess four patient variables that may affect normal Raman spectra: Race/ethnicity, body mass index (BMI), parity, and socioeconomic status. Raman spectra were acquired from a diverse population of 75 patients undergoing routine screening for cervical dysplasia. Classification of Raman spectra from patients with a normal cervix is performed using sparse multinomial logistic regression (SMLR) to determine if any of these variables has a significant effect. Results suggest that BMI and parity have the greatest impact, whereas race/ethnicity and socioeconomic status have a limited effect. Incorporating BMI and obstetric history into classification algorithms may increase sensitivity and specificity rates of disease classification using Raman spectroscopy. Studies are underway to assess the effect of these variables on disease.

Raman spectroscopic methods for classification of normal and malignant hypopharyngeal tissues: an exploratory study

Laryngeal cancer is more common in males. The present study is aimed at exploration of potential of conventional Raman spectroscopy in classifying normal from a malignant laryngopharyngeal tissue. We have recorded Raman spectra of twenty tissues (aryepiglottic fold) using an in-house built Raman setup. The spectral features of mean malignant spectrum suggests abundance proteins whereas spectral features of mean normal spectrum indicate redundancy of lipids. PCA was employed as discriminating algorithm. Both, unsupervised and supervised modes of analysis as well as match/mismatch "limit test" methodology yielded clear classification among tissue types. The findings of this study demonstrate the efficacy of conventional Raman spectroscopy in classification of normal and malignant laryngopharyngeal tissues. A rigorous evaluation of the models with development of suitable fibreoptic probe may enable real-time Raman spectroscopic diagnosis of laryngopharyngeal cancers in future.

In vivo bladder cancer diagnosis by high-volume Raman spectroscopy

We studied the feasibility of Raman spectroscopy for the diagnosis of bladder cancer in vivo. Since the invasion stage is crucial for the treatment choice, a high-volume based Raman probe was used to investigate the potential of determining the invasiveness of bladder cancer. High quality spectra were obtained from suspicious and nonsuspicious bladder locations during the procedure of transurethral resection of bladder tumors (TURBT) with collection times of 1-5 s. Multivariate analysis was used to generate the classification models. The algorithm was able to distinguish bladder cancer from normal bladder locations with a sensitivity of 85% and a specificity of 79%. The Raman spectra of bladder cancer stages showed a gradual increase in the intensity of specific amino acid peaks and, most likely, an increase in the intensity of DNA peaks.

Raman spectroscopy in head and neck cancer

In recent years there has been much interest in the use of optical diagnostics in cancer detection. Early diagnosis of cancer affords early intervention and greatest chance of cure. Raman spectroscopy is based on the interaction of photons with the target material producing a highly detailed biochemical 'fingerprint' of the sample. It can be appreciated that such a sensitive biochemical detection system could confer diagnostic benefit in a clinical setting. Raman has been used successfully in key health areas such as cardiovascular diseases, and dental care but there is a paucity of literature on Raman spectroscopy in Head and Neck cancer. Following the introduction of health care targets for cancer, and with an ever-aging population the need for rapid cancer detection has never been greater. Raman spectroscopy could confer great patient benefit with early, rapid and accurate diagnosis. This technique is almost labour free without the need for sample preparation. It could reduce the need for whole pathological specimen examination, in theatre it could help to determine margin status, and finally peripheral blood diagnosis may be an achievable target.