Raman spectroscopy in head and neck cancer

Characterization of biomarkers in blood serum for cancer diagnosis in dogs using Raman spectroscopy

Biomarkers of cancer in sera of domestic dogs were detected through Raman spectroscopy with 830 nm excitation. Raman spectra of sera from 61 dogs (31 healthy and 30 with cancer, resulting in 154 and 200 spectra, respectively) were submitted to principal component analysis (PCA) for feature extraction and partial least squares (PLS) regression for discrimination between Healthy and Cancer groups. In the PCA, the peaks at 1132, 1342, 1368, and 1453 cm-1 (albumin and phenylalanine) were higher for the Cancer group. The "redshift" of the peaks at 621, 1003, and 1032 cm-1 (conformational change in proteins and/or bonds at sites close to the aromatic ring of amino acids) occurred in the Cancer group, and the peaks at 451 cm-1 (tryptophan) and 1441 cm-1 (lipids) were higher for the Healthy group. The PLS-DA classified the serum spectra in Healthy and Cancer groups with high accuracy (78%).

The emerging applications and advancements of Raman spectroscopy in pediatric cancers

Although the survival rate of pediatric cancer has significantly improved, it is still an important cause of death among children. New technologies have been developed to improve the diagnosis, treatment, and prognosis of pediatric cancers. Raman spectroscopy (RS) is a non-destructive analytical technique that uses different frequencies of scattering light to characterize biological specimens. It can provide information on biological components, activities, and molecular structures. This review summarizes studies on the potential of RS in pediatric cancers. Currently, studies on the application of RS in pediatric cancers mainly focus on early diagnosis, prognosis prediction, and treatment improvement. The results of these studies showed high accuracy and specificity. In addition, the combination of RS and deep learning is discussed as a future application of RS in pediatric cancer. Studies applying RS in pediatric cancer illustrated good prospects. This review collected and analyzed the potential clinical applications of RS in pediatric cancers.

Diagnostic Biomarkers for Gestational Diabetes Mellitus Using Spectroscopy Techniques: A Systematic Review

Gestational diabetes mellitus (GDM) is associated with adverse maternal and foetal consequences, along with the subsequent risk of type 2 diabetes mellitus (T2DM) and several other diseases. Due to early risk stratification in the prevention of progression of GDM, improvements in biomarker determination for GDM diagnosis will enhance the optimization of both maternal and foetal health. Spectroscopy techniques are being used in an increasing number of applications in medicine for investigating biochemical pathways and the identification of key biomarkers associated with the pathogenesis of GDM. The significance of spectroscopy promises the molecular information without the need for special stains and dyes; therefore, it speeds up and simplifies the necessary ex vivo and in vivo analysis for interventions in healthcare. All the selected studies showed that spectroscopy techniques were effective in the identification of biomarkers through specific biofluids. Existing GDM prediction and diagnosis through spectroscopy techniques presented invariable findings. Further studies are required in larger, ethnically diverse populations. This systematic review provides the up-to-date state of research on biomarkers in GDM, which were identified via various spectroscopy techniques, and a discussion of the clinical significance of these biomarkers in the prediction, diagnosis, and management of GDM.

Raman Spectroscopy: A Potential Diagnostic Tool for Oral Diseases

Oral diseases impose a major health burden worldwide and have a profound effect on general health. Dental caries, periodontal diseases, and oral cancers are the most common oral health conditions. Their occurrence and development are related to oral microbes, and effective measures for their prevention and the promotion of oral health are urgently needed. Raman spectroscopy detects molecular vibration information by collecting inelastic scattering light, allowing a “fingerprint” of a sample to be acquired. It provides the advantages of rapid, sensitive, accurate, and minimally invasive detection as well as minimal interference from water in the “fingerprint region.” Owing to these characteristics, Raman spectroscopy has been used in medical detection in various fields to assist diagnosis and evaluate prognosis, such as detecting and differentiating between bacteria or between neoplastic and normal brain tissues. Many oral diseases are related to oral microbial dysbiosis, and their lesions differ from normal tissues in essential components. The colonization of keystone pathogens, such as Porphyromonas gingivalis, resulting in microbial dysbiosis in subgingival plaque, is the main cause of periodontitis. Moreover, the components in gingival crevicular fluid, such as infiltrating inflammatory cells and tissue degradation products, are markedly different between individuals with and without periodontitis. Regarding dental caries, the compositions of decayed teeth are transformed, accompanied by an increase in acid-producing bacteria. In oral cancers, the compositions and structures of lesions and normal tissues are different. Thus, the changes in bacteria and the components of saliva and tissue can be used in examinations as special markers for these oral diseases, and Raman spectroscopy has been acknowledged as a promising measure for detecting these markers. This review summarizes and discusses key research and remaining problems in this area. Based on this, suggestions for further study are proposed.

Detection of inaccessible head and neck lesions using human saliva and fluorescence spectroscopy

Head and neck cancer detection using fluorescence spectroscopy from human saliva is reported here. This study has been conducted on squamous cell carcinoma (SCC), and dysplastic (precancer) and control (normal) groups using an in-house developed compact set-up. Fluorescence set-up consists of a 375-nm laser diode and optical components. Spectral bands of flavin adenine dinucleotide (FAD), porphyrins, and Raman are observed in the spectral range of 400 to 800 nm. Presence of FAD and porphyrin bands in human saliva is confirmed by the liquid phantoms of FAD and porphyrin. Significant differences in fluorescence intensities among all the three groups are observed. Three spectral ranges from 455 to 600, 605 to 770, and 400 to 800 nm are selected for each group and area values under each spectral range are computed. To differentiate among the groups, receiver operating characteristic (ROC) analysis is employed on the area values. ROC differentiates among the groups with accuracies of 98%, 92.85%, and 81.13% respectively in the spectral ranges of 400 to 800 nm. However, in other two spectral ranges (455 to 600 and 605 to 770 nm), low accuracy values are found. Obtained accuracy values indicate that selection of human saliva for head and neck cancer detection may be a good alternative.

Raman tweezers as an alternative diagnostic tool for paroxysmal nocturnal hemoglobinuria

Paroxysmal nocturnal hemoglobinuria (PNH) is a rare disease characterized by hemolysis of red blood cells (RBC) and venous thrombosis. The gold standard method for the diagnosis of this disease is flow cytometry. Here, we propose a combined optical tweezers and Raman spectral (Raman tweezers) approach to analyze blood samples from volunteers with or without PNH conditions. Raman spectroscopy is a well-known method for investigating a material's chemical structure and is also used in molecular analysis of biological compounds. In this study, we trap individual RBCs found in whole blood samples drawn from PNH patients and the control group. Evaluation of the Raman spectra of these cells by band component analysis and machine learning shows a significant difference between the two groups. The specificity and the sensitivity of the training performed by support vector machine (SVM) analysis were found to be 81.8% and 78.3%, respectively. This study shows that an immediate and high accuracy test result is possible for PNH disease by employing Raman tweezers and machine learning.

An overview of the current clinical status of optical imaging in head and neck cancer with a focus on Narrow Band imaging and fluorescence optical imaging

Early and accurate identification of head and neck squamous cell carcinoma (HNSCC) is important to improve treatment outcomes and prognosis. New optical imaging techniques may assist in both the diagnostic process as well as in the operative setting by real-time visualization and delineation of tumor. Narrow Band Imaging (NBI) is an endoscopic technique that uses blue and green light to enhance mucosal and submucosal blood vessels, leading to better detection of (pre)malignant lesions showing aberrant blood vessel patterns. Fluorescence optical imaging makes use of near-infrared fluorescent agents to visualize and delineate HNSCC, resulting in fewer positive surgical margins. Targeted fluorescent agents, such as fluorophores conjugated to antibodies, show the most promising results. The aim of this review is: (1) to provide the clinical head and neck surgeon an overview of the current clinical status of various optical imaging techniques in head and neck cancer; (2) to provide an in-depth review of NBI and fluorescence optical imaging, as these techniques have the highest potential for clinical implementation; and (3) to describe future improvements and developments within the field of these two techniques.

Optical diagnosis of oral cavity lesions by label-free Raman spectroscopy

Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers and frequently preceded by non-malignant lesions. Using Shifted-Excitation Raman Difference Spectroscopy (SERDS), principal component and linear discriminant analysis in native tissue specimens, 9500 raw Raman spectra of OSCC, 4300 of non-malignant lesions and 4200 of physiological mucosa were evaluated. Non-malignant lesions were distinguished from physiological mucosa with a classification accuracy of 95.3% (95.4% sensitivity, 95.2% specificity, area under the curve (AUC) 0.99). Discriminating OSCC from non-malignant lesions showed an accuracy of 88.4% (93.7% sensitivity, 76.7% specificity, AUC 0.93). OSCC was identified against physiological mucosa with an accuracy of 89.8% (93.7% sensitivity, 81.0% specificity, AUC 0.90). These findings underline the potential of SERDS for the diagnosis of oral cavity lesions.

Diagnosing Hirschsprung disease by detecting intestinal ganglion cells using label-free hyperspectral microscopy

Hirschsprung disease (HD) is a congenital disorder in the distal colon that is characterized by the absence of nerve ganglion cells in the diseased tissue. The primary treatment for HD is surgical intervention with resection of the aganglionic bowel. The accurate identification of the aganglionic segment depends on the histologic evaluation of multiple biopsies to determine the absence of ganglion cells in the tissue, which can be a time-consuming procedure. We investigate the feasibility of using a combination of label-free optical modalities, second harmonic generation (SHG); two-photon excitation autofluorescence (2PAF); and Raman spectroscopy (RS), to accurately locate and identify ganglion cells in murine intestinal tissue without the use of exogenous labels or dyes. We show that the image contrast provided by SHG and 2PAF signals allows for the visualization of the overall tissue morphology and localization of regions that may contain ganglion cells, while RS provides detailed multiplexed molecular information that can be used to accurately identify specific ganglion cells. Support vector machine, principal component analysis and linear discriminant analysis classification models were applied to the hyperspectral Raman data and showed that ganglion cells can be identified with a classification accuracy higher than 95%. Our findings suggest that a near real-time intraoperative histology method can be developed using these three optical modalities together that can aid pathologists and surgeons in rapid, accurate identification of ganglion cells to guide surgical decisions with minimal human intervention.

Molecular margins in head and neck cancer: Current techniques and future directions

Complete tumor extirpation with clear surgical margins remains a central tenet of oncologic head and neck surgery. Rates of locoregional recurrence and survival are both significantly worse when clear margins are unable to be obtained. Current clinical practice relies on the use of frozen sections intra-operatively, followed by traditional histopathologic analysis post-operatively to assess the surgical margin. However, with improved understanding of tumor biology and advances in technology, new techniques have emerged to analyze margins at a molecular level. Such molecular margin analysis interrogates tissue for genetic, epigenetic, or proteomic changes that may belie tumor presence or aggressive features not captured by standard histopathologic techniques. Intra-operatively, this information may be used to guide resection, while post-operatively, it may help to stratify patients for adjuvant treatment. In this review, we summarize the current state of molecular margin analysis and describe directions for future research.

Human salivary Raman fingerprint as biomarker for the diagnosis of Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease leading to progressive and irreversible muscle atrophy. The diagnosis of ALS is time-consuming and complex, with the clinical and neurophysiological evaluation accompanied by monitoring of progression and a long procedure for the discrimination of similar neurodegenerative diseases. The delayed diagnosis strongly slows the potential development of adequate therapies and the time frame for a prompt intervention. The discovery of new biomarkers could improve the disease diagnosis, as well as the therapeutic and rehabilitative effectiveness and monitoring of the pathological progression. In this work saliva collected from 19 patients with ALS, 10 affected by Parkinson’s disease, 10 affected by Alzheimer’s disease and 10 healthy subjects, was analysed using Raman spectroscopy, optimizing the parameters for detailed and reproducible spectra. The statistical multivariate analysis of the data revealed a significant difference between the groups, allowing the discrimination of the disease onset. Correlation of Raman data revealed a direct relationship with paraclinical scores, identifying multifactorial biochemical modifications related to the pathology. The proposed approach showed a promising accuracy in ALS onset discrimination, using a fast and sensitive procedure that can make more efficient the diagnostic procedure and the monitoring of therapeutic and rehabilitative processes in ALS.

The use of optical coherence tomography and convolutional neural networks to distinguish normal and abnormal oral mucosa

Incomplete surgical resection of head and neck squamous cell carcinoma (HNSCC) is the most common cause of local HNSCC recurrence. Currently, surgeons rely on preoperative imaging, direct visualization, palpation and frozen section to determine the extent of tissue resection. It has been demonstrated that optical coherence tomography (OCT), a minimally invasive, nonionizing near infrared mesoscopic imaging modality can resolve subsurface differences between normal and abnormal head and neck mucosa. Previous work has utilized two-dimensional OCT imaging which is limited to the evaluation of small regions of interest generated frame by frame. OCT technology is capable of performing rapid volumetric imaging, but the capacity and expertise to analyze this massive amount of image data is lacking. In this study, we evaluate the ability of a retrained convolutional neural network to classify three-dimensional OCT images of head and neck mucosa to differentiate normal and abnormal tissues with sensitivity and specificity of 100% and 70%, respectively. This method has the potential to serve as a real-time analytic tool in the assessment of surgical margins.

Hyperspectral Raman microscopy can accurately differentiate single cells of different human thyroid nodules

We report on the use of line-scan hyperspectral Raman microscopy in combination with multivariate statistical analyses for identifying and classifying single cells isolated from clinical samples of human thyroid nodules based on their intrinsic Raman spectral signatures. A total of 248 hyperspectral Raman images of single cells from benign thyroid (n = 127) and classic variant of papillary carcinoma (n = 121) nodules were collected. Spectral differences attributed to phenylalanine, tryptophan, proteins, lipids, and nucleic acids were identified for benign and papillary carcinoma cells. Using principal component analysis and linear discriminant analysis, cells were identified with 97% diagnostic accuracy. In addition, preliminary data of cells from follicular adenoma (n = 20), follicular carcinoma (n = 25), and follicular variant of papillary carcinoma (n = 18) nodules suggest the feasibility of further discrimination of subtypes. Our findings indicate that hyperspectral Raman microscopy can potentially be developed into an objective approach for analyzing single cells from fine needle aspiration (FNA) biopsies to enable the minimally invasive diagnosis of "indeterminate" thyroid nodules and other challenging cases.

Raman Spectroscopy for Inverted Papilloma: A Proof-of-Concept Study

Inverted papillomas are tumors of the sinonasal tract with a propensity to recur. Raman spectroscopy can potentially identify inverted papillomas from other tissue based on biochemical signatures. A pilot study comparing Raman spectroscopy to histopathology for 3 types of sinonasal tissue was performed. Spectral data of biopsies from patients with normal sinonasal mucosa, chronic rhinosinusitis, and inverted papillomas are compared to histopathology using principal component analysis and linear discriminant analysis after data preprocessing. A total of 18 normal, 15 chronic rhinosinusitis, and 18 inverted papilloma specimens were evaluated. The model distinguished normal sinonasal mucosa, chronic rhinosinusitis, and inverted papilloma tissue with an overall accuracy of 90.2% (95% confidence interval, 0.86-0.94). In conclusion, Raman spectroscopy can distinguish inverted papilloma, normal sinonasal mucosa, and chronically rhinosinusitis tissue with acceptable accuracy.

In-vivo optical imaging in head and neck oncology: basic principles, clinical applications and future directions

Head and neck cancers become a severe threat to human's health nowadays and represent the sixth most common cancer worldwide. Surgery remains the first-line choice for head and neck cancer patients. Limited resectable tissue mass and complicated anatomy structures in the head and neck region put the surgeons in a dilemma between the extensive resection and a better quality of life for the patients. Early diagnosis and treatment of the pre-malignancies, as well as real-time in vivo detection of surgical margins during en bloc resection, could be leveraged to minimize the resection of normal tissues. With the understanding of the head and neck oncology, recent advances in optical hardware and reagents have provided unique opportunities for real-time pre-malignancies and cancer imaging in the clinic or operating room. Optical imaging in the head and neck has been reported using autofluorescence imaging, targeted fluorescence imaging, high-resolution microendoscopy, narrow band imaging and the Raman spectroscopy. In this study, we reviewed the basic theories and clinical applications of optical imaging for the diagnosis and treatment in the field of head and neck oncology with the goal of identifying limitations and facilitating future advancements in the field.

A monolithic microsphere-fiber probe for spatially resolved Raman spectroscopy: Application to head and neck squamous cell carcinomas

The ability to identify precise cancer margins in vivo during a surgical excision is critical to the well-being of the patient. Decreased operative time has been linked to shorter patient recovery time, and there are risks associated with removing either too much or too little tissue from the surgical site. The more rapidly and accurately a surgeon can identify and excise diseased tissue, the better the prognosis for the patient. To this end, we investigate both malignant and healthy oral cavity tissue using the Raman spectroscopy, with a monolithic microsphere-fiber probe. Our results indicate that this probe has decreased the size of the analyzed area by more than an order of magnitude, as compared to a conventional fiber reflection probe. Scanning the probe across the tissues reveals variations in the Raman spectra that enable us to differentiate between malignant and healthy tissues. Consequently, we anticipate that the high spatial resolution afforded by the probe will permit us to identify tumor margins in detail, thereby optimizing tissue removal and improving patient outcomes.

Raman Spectroscopy of Head and Neck Cancer: Separation of Malignant and Healthy Tissue Using Signatures Outside the "Fingerprint" Region

The ability to rapidly and accurately discriminate between healthy and malignant tissue offers surgeons a tool for in vivo analysis that would potentially reduce operating time, facilitate quicker recovery, and improve patient outcomes. To this end, we investigate discrimination between diseased tissue and adjacent healthy controls from patients with head and neck cancer using near-infrared Raman spectroscopy. Our results indicate previously unreported peaks in the Raman spectra that lie outside the conventional "fingerprint" region (400 cm-1-1800 cm -1) played an important role in our analysis and in discriminating between the tissue classes. Preliminary multivariate statistical analyses of the Raman spectra indicate that discrimination between diseased and healthy tissue is possible based on these peaks.

Micro-Raman spectroscopic study of thyroid tissues

Thyroid carcinomas are the most common endocrine malignancy. Inconclusive results for the analysis of malignancies are an issue in the diagnosis of thyroid carcinomas; 20% of thyroid cancer diagnoses are indeterminate or suspicious, resulting in a surgical procedure without immediate need. The use of Raman spectroscopy may help improve the diagnosis of thyroid carcinoma. In this study, 30 thyroid samples, including normal thyroid, goiter and thyroid cancer, were analyzed by confocal Raman spectroscopy. Principal component analysis (PCA), linear discriminant analysis (LDA) with cross validation and binary logistic regression (BLR) analysis were applied to discriminate among tissues. Significant discrimination was observed, with a consistent rate of concordant pairs of 89.2% for normal thyroid versus cancer, 85.7% for goiter versus cancer and 80.6% for normal thyroid versus goiter using just the amide III region. Raman spectroscopy was thus proven to be an important and fast tool for the diagnosis of thyroid tissues. The spectral region of 1200-1400cm^-1 discriminated normal versus goiter tissues despite the great similarity of these tissues.

Miniature standoff Raman probe for neurosurgical applications

Removal of intrinsic brain tumors is a delicate process, where a high degree of specificity is required to remove all of the tumor tissue without damaging healthy brain. The accuracy of this process can be greatly enhanced by intraoperative guidance. Optical biopsies using Raman spectroscopy are a minimally invasive and lower-cost alternative to current guidance methods. A miniature Raman probe for performing optical biopsies of human brain tissue is presented. The probe allows sampling inside a conventional stereotactic brain biopsy system: a needle of length 200 mm and inner diameter of 1.8 mm. By employing a miniature stand-off Raman design, the probe removes the need for any additional components to be inserted into the brain. Additionally, the probe achieves a very low internal silica background while maintaining good collection of Raman signal. To illustrate this, the probe is compared with a Raman probe that uses a pair of optical fibers for collection. The miniature stand-off Raman probe is shown to collect a comparable number of Raman scattered photons, but the Raman signal to background ratio is improved by a factor of five at Raman shifts below ∼500  cm(−1). The probe’s suitability for use on tissue is demonstrated by discriminating between different types of healthy porcine brain tissue.

Development and validation of Raman spectroscopic classification models to discriminate tongue squamous cell carcinoma from non-tumorous tissue

BACKGROUND

Currently, up to 85% of the oral resection specimens have inadequate resection margins, of which the majority is located in the deeper soft tissue layers. The prognosis of patients with oral cavity squamous cell carcinoma (OCSCC) of the tongue is negatively affected by these inadequate surgical resections. Raman spectroscopy, an optical technique, can potentially be used for intra-operative evaluation of resection margins.

OBJECTIVE

To develop in vitro Raman spectroscopy-based tissue classification models that discriminate OCSCC of the tongue from (subepithelial) non-tumorous tissue.

MATERIALS AND METHODS

Tissue classification models were developed using Principal Components Analysis (PCA) followed by (hierarchical) Linear Discriminant Analysis ((h)LDA). The models were based on a training set of 720 histopathologically annotated Raman spectra, obtained from 25 tongue samples (11 OCSCC and 14 normal) of 10 patients, and were validated by means of an independent validation set of 367 spectra, obtained from 19 tongue samples (6 OCSCC and 13 normal) of 11 patients.

RESULTS

A PCA-LDA tissue classification model 'tumor' versus 'non-tumorous tissue' (i.e. surface squamous epithelium, connective tissue, muscle, adipose tissue, gland and nerve) showed an accuracy of 86% (sensitivity: 100%, specificity: 66%). A two-step PCA-hLDA tissue classification model 'tumor' versus 'non-tumorous tissue' showed an accuracy of 91% (sensitivity: 100%, specificity: 78%).

CONCLUSION

An accurate PCA-hLDA Raman spectroscopy-based tissue classification model for discrimination between OCSCC and (especially the subepithelial) non-tumorous tongue tissue was developed and validated. This model with high sensitivity and specificity may prove to be very helpful to detect tumor in the resection margins.

Developing fibre optic Raman probes for applications in clinical spectroscopy

Raman spectroscopy has been shown by various groups over the last two decades to have significant capability in discriminating disease states in bodily fluids, cells and tissues. Recent development in instrumentation, optics and manufacturing approaches has facilitated the design and demonstration of various novel in vivo probes, which have applicability for myriad of applications. This review focusses on key considerations and recommendations for application specific clinical Raman probe design and construction. Raman probes can be utilised as clinical tools able to provide rapid, non-invasive, real-time molecular analysis of disease specific changes in tissues. Clearly the target tissue location, the significance of spectral changes with disease and the possible access routes to the region of interest will vary for each clinical application considered. This review provides insight into design and construction considerations, including suitable probe designs and manufacturing materials compatible with Raman spectroscopy.

Label-free blood serum detection by using surface-enhanced Raman spectroscopy and support vector machine for the preoperative diagnosis of parotid gland tumors

BACKGROUND

It is difficult for the parotid gland neoplasms to make an accurate preoperative diagnosis due to the restriction of biopsy in the parotid gland neoplasms. The aim of this study is to apply the surface-enhanced Raman spectroscopy (SERS) method for the blood serum biochemical detection and use the support vector machine for the analysis in order to develop a simple but accurate blood serum detection for preoperative diagnosis of the parotid gland neoplasms.

METHODS

The blood serums were collected from four groups: the patients with pleomorphic adenoma, the patients with Warthin's tumor, the patients with mucoepidermoid carcinoma and the volunteers without parotid gland neoplasms. Au nanoparticles (Au NPs) were mixed with the blood serum as the SERS active nanosensor to enhance the Raman scattering signals produced by the various biochemical materials and high quality SERS spectrum were obtained by using the Raman microscope system. Then the support vector machine was utilized to analyze the differences of the SERS spectrum from the blood serum of different groups and established a diagnostic model to discriminate the different groups.

RESULTS

It was demonstrated that there were different intensities of SERS peaks assigned to various biochemical changes in the blood serum between the parotid gland tumor groups and normal control group. Compared with the SERS spectra of the normal serums, the intensities of peaks assigned to nucleic acids and proteins increased in the SERS spectra of the parotid gland tumor serums, which manifested the differences of the biochemical metabolites in the serum from the patients with parotid gland tumors. When the leave-one-sample-out method was used, support vector machine (SVM) played an outstanding performance in the classification of the SERS spectra with the high accuracy (84.1 % ~ 88.3 %), sensitivity (82.2 % ~ 97.4 %) and specificity (73.7 % ~ 86.7 %). Though the accuracy, sensitivity and specificity decreased in the leave-one-patient-out cross validation, the mucoepidermoid carcinoma was still easier to diagnose than other tumors.

DISCUSSION

The specific molecular differences of parotid gland tumors and normal serums were significantly demonstrated through the comparison between the various SERS spectra.But compared with the serum SERS spectra reported in the other studies, some differences exist between the spectra in this study and the ones reported in the lietratures. These differences may result from the various nano-particles, the different preparation of serum and equipment parameters, and we could need a further research to find an exact explanation.Based on the SERS spectra of the serum samples, SVM have shown a giant potential to diagnose the parotid gland tumors in our preliminary study. However, different cross validaiton methods could effect the accuracy and a further study involing a great number of samples should be needed.

CONCLUSIONS

This exploratory research demonstrated the great potential of SERS combined with SVM into a non-invasive clinical diagnostic method for preoperative diagnosis of parotid gland tumors. And the internal relation between the spectra and patients should be established in the further study.

Investigation of the potential of Raman spectroscopy for oral cancer detection in surgical margins

The poor prognosis of oral cavity squamous cell carcinoma (OCSCC) patients is associated with residual tumor after surgery. Raman spectroscopy has the potential to provide an objective intra-operative evaluation of the surgical margins. Our aim was to understand the discriminatory basis of Raman spectroscopy at a histological level. In total, 127 pseudo-color Raman images were generated from unstained thin tissue sections of 25 samples (11 OCSCC and 14 healthy) of 10 patients. These images were clearly linked to the histopathological evaluation of the same sections after hematoxylin and eosin-staining. In this way, Raman spectra were annotated as OCSCC or as a surrounding healthy tissue structure (i.e., squamous epithelium, connective tissue (CT), adipose tissue, muscle, gland, or nerve). These annotated spectra were used as input for linear discriminant analysis (LDA) models to discriminate between OCSCC spectra and healthy tissue spectra. A database was acquired with 88 spectra of OCSCC and 632 spectra of healthy tissue. The LDA models could distinguish OCSCC spectra from the spectra of adipose tissue, nerve, muscle, gland, CT, and squamous epithelium in 100%, 100%, 97%, 94%, 93%, and 75% of the cases, respectively. More specifically, the structures that were most often confused with OCSCC were dysplastic epithelium, basal layers of epithelium, inflammation- and capillary-rich CT, and connective and glandular tissue close to OCSCC. Our study shows how well Raman spectroscopy enables discrimination between OCSCC and surrounding healthy tissue structures. This knowledge supports the development of robust and reliable classification algorithms for future implementation of Raman spectroscopy in clinical practice.

Recurrence prediction in oral cancers: a serum Raman spectroscopy study

High mortality rates associated with oral cancers can be primarily attributed to the failure of current histological procedures in predicting recurrence. Identifying recurrence related factors can lead to improved prognosis, optimized treatment and enhanced overall outcomes. Serum Raman spectroscopy has previously shown potential in the diagnosis of cancers, such as head and neck, cervix, breast, oral cancers, and also in predicting treatment response. In the present study, serum was collected from 22 oral cancer subjects [with recurrence (n = 10) and no-recurrence (n = 12)] before and after surgery and spectra were acquired using a Raman microprobe coupled with a 40× objective. Spectral acquisition parameters were as follows: λex = 785 nm, laser power = 30 mW, integration time: 12 s and averages: 3. Data was analyzed in a patient-wise approach using unsupervised PCA and supervised PC-LDA, followed by LOOCV. PCA and PC-LDA findings suggest that recurrent and non-recurrent cases cannot be classified in before surgery serum samples; an average classification efficiency of ∼78% was obtained in after-surgery samples. Mean and difference spectra and PCA loadings indicate that DNA and protein markers may be potential spectral markers for recurrence. RS of post surgery serum samples may have the potential to predict the probability of recurrence in clinics, after prospective large-scale validation.

Nuclear accumulation of anthracyclines in the endothelium studied by bimodal imaging: fluorescence and Raman microscopy

Anthracycline antibiotics display genotoxic activity towards cancer cells but their clinical utility is limited by their cardiac and vascular toxicity. The aim of this study was to develop a Raman-based methodology to study the nuclear accumulation of anthracyclines in the endothelium. For this purpose bimodal confocal Raman and fluorescence imaging was used to monitor cellular composition changes as a result of anthracycline exposure on endothelial cells (EA.hy926), and nuclear drug accumulation, respectively. Simultaneously effects of anthracyclines on endothelium viability were investigated by caspases-3 and -7 and MTT assays. We demonstrated that nuclear accumulation of DOX and EDOX was similar; however, EDNR accumulated in endothelial nuclei at concentrations 10 times higher than DNR. In turn, epimers of DOX or DNR were both consistently less toxic on the endothelium as compared to their congeners as evidenced by MTT and caspase assays. In summary, bimodal Raman and fluorescence-based nucleus profiling proves to be a valuable tool to study structure-activity relationship of nuclear accumulation and toxicity of anthracyclines in endothelium.

Raman Spectroscopy of Experimental Oral Carcinogenesis: Study on Sequential Cancer Progression in Hamster Buccal Pouch Model

Oral cancers suffer from poor 5-year survival rates, owing to late detection of the disease. Current diagnostic/screening tools need to be upgraded in view of disadvantages like invasiveness, tedious sample preparation, long output times, and interobserver variances. Raman spectroscopy has been shown to identify many disease conditions, including oral cancers, from healthy conditions. Further studies in exploring sequential changes in oral carcinogenesis are warranted. In this Raman spectroscopy study, sequential progression in experimental oral carcinogenesis in Hamster buccal pouch model was investigated using 3 approaches-ex vivo, in vivo sequential, and in vivo follow-up. In all these studies, spectral changes show lipid dominance in early stages while later stages and tumors showed increased protein to lipid ratio and nucleic acids. On similar lines, early weeks of 7,12-dimethylbenz(a)anthracene-treated and control groups showed higher overlap and low classification. The classification efficiency increased progressively, reached a plateau phase and subsequently increased up to 100% by 14 weeks. The misclassifications between treated and control spectra suggested some changes in controls as well, which was confirmed by a careful reexamination of histopathological slides. These findings suggests Raman spectroscopy may be able to identify microheterogeneity, which may often go unnoticed in conventional biochemistry wherein tissue extracts are employed, as well as in histopathology. In vivo findings, quite comparable to gold-standard supported ex vivo findings, give further proof of Raman spectroscopy being a promising label-free, noninvasive diagnostic adjunct for future clinical applications.

Current concepts and future of noninvasive procedures for diagnosing oral squamous cell carcinoma--a systematic review

Background

Oral squamous cell carcinoma (OSCC) has a remarkably high incidence worldwide, and a fairly serious prognosis, encouraging further research into advanced technologies for noninvasive methods of making early diagnoses, ideally in primary care settings.

Objectives

Our purpose was to examine the validity of using advanced noninvasive technologies in diagnosis of OSCC by identifying and evaluating relevant published reports.

Data source

MEDLINE, EMBASE, and CINAHL were searched to identify clinical trials and other information published between 1990 and 10 June 2014; the searches of MEDLINE and EMBASE were updated to November 2014. Study selection: Studies of noninvasive methods of diagnosing OSCC, including oral brush biopsy, optical biopsy, saliva-based oral cancer diagnosis, and others were included.

Data extraction

Data were abstracted and evaluated in duplicate for possible relevance on two occasions at an interval of 2 months before being included or excluded.

Data synthesis

This study identified 163 studies of noninvasive methods for diagnosing OSCC that met the inclusion criteria. These included six studies of oral brush biopsy, 42 of saliva-based oral diagnosis, and 115 of optical biopsy. Sixty nine of these studies were assessed by the modified version of the QUADAS instrument. Saliva-based oral cancer diagnosis and optical biopsy were found to be promising noninvasive methods for diagnosing OSCC.

Limitation

The strength of evidence was rated low for accuracy outcomes because the studies did not report important details required to assess the risk for bias.

Conclusions

It is clear that screening for and early detection of cancer and pre-cancerous lesions have the potential to reduce the morbidity and mortality of this disease. Advances in technologies for saliva-based oral diagnosis and optical biopsy are promising pathways for the future development of more effective noninvasive methods for diagnosing OSCC that are easy to perform clinically in primary care settings.

Distinguishing Cancerous Liver Cells Using Surface-Enhanced Raman Spectroscopy

Raman spectroscopy has been widely used in biomedical research and clinical diagnostics. It possesses great potential for the analysis of biochemical processes in cell studies. In this article, the surface-enhanced Raman spectroscopy (SERS) of normal and cancerous liver cells incubated with SERS active substrates (gold nanoparticle) was measured using confocal Raman microspectroscopy technology. The chemical components of the cells were analyzed through statistical methods for the SERS spectrum. Both the relative intensity ratio and principal component analysis (PCA) were used for distinguishing the normal liver cells (QSG-7701) from the hepatoma cells (SMMC-7721). The relative intensity ratio of the Raman spectra peaks such as I937/I1209, I1276/I1308, I1342/I1375, and I1402/I1435 was set as the judge boundary, and the sensitivity and the specificity using PCA method were calculated. The results indicated that the surface-enhanced Raman spectrum could provide the chemical information for distinguishing the normal cells from the cancerous liver cells and demonstrated that SERS technology possessed the possible applied potential for the diagnosis of liver cancer.

Identification and characterization of colorectal cancer using Raman spectroscopy and feature selection techniques

This study aims to detect colorectal cancer with near-infrared Raman spectroscopy and feature selection techniques. A total of 306 Raman spectra of colorectal cancer tissues and normal tissues are acquired from 44 colorectal cancer patients. Five diagnostically important Raman bands in the regions of 815-830, 935-945, 1131-1141, 1447-1457 and 1665-1675 cm(-1) related to proteins, nucleic acids and lipids of tissues are identified with the ant colony optimization (ACO) and support vector machine (SVM). The diagnostic models built with the identified Raman bands provide a diagnostic accuracy of 93.2% for identifying colorectal cancer from normal Raman spectroscopy. The study demonstrates that the Raman spectroscopy associated with ACO-SVM diagnostic algorithms has great potential to characterize and diagnose colorectal cancer.

Amide proton transfer-weighted imaging of the head and neck at 3 T: a feasibility study on healthy human subjects and patients with head and neck cancer

The aim of this study was to explore the feasibility and repeatability of amide proton transfer-weighted (APTw) MRI for the head and neck on clinical MRI scanners. Six healthy volunteers and four patients with head and neck tumors underwent APTw MRI scanning at 3 T. The APTw signal was quantified by the asymmetric magnetization transfer ratio (MTRasym) at 3.5 ppm. Z spectra of normal tissues in the head and neck (masseter muscle, parotid glands, submandibular glands and thyroid glands) were analyzed in healthy volunteers. Inter-scan repeatability of APTw MRI was evaluated in six healthy volunteers. Z spectra of patients with head and neck tumors were produced and APTw signals in these tumors were analyzed. APTw MRI scanning was successful for all 10 subjects. The parotid glands showed the highest APTw signal (~7.6% average), whereas the APTw signals in other tissues were relatively moderate. The repeatability of APTw signals from the masseter muscle, parotid gland, submandibular gland and thyroid gland of healthy volunteers was established. Four head and neck tumors showed positive mean APTw ranging from 1.2% to 3.2%, distinguishable from surrounding normal tissues. APTw MRI was feasible for use in the head and neck regions at 3 T. The preliminary results on patients with head and neck tumors indicated the potential of APTw MRI for clinical applications.

Raman difference spectroscopy: a non-invasive method for identification of oral squamous cell carcinoma

The feasibility of shifted-excitation Raman difference spectroscopy (SERDS) as a label-free and non-invasive technique for an objective diagnosis of oral cancer (OSCC) was investigated by analyzing 12 ex vivo OSCC samples. 72 mean SERDS spectra from each three physiological tissue points and pathological lesions were correlated with the histo-pathological diagnosis. Principal component analysis (PCA) and linear discriminant analysis (LDA) showed excellent results with an area under the curve of 94.5% and a classification error of 9.7% (sensitivity: 86.1%; specificity: 94.4%). The SERDS Raman spectra of malignant and benignant tissues were discriminable with respect to the spectral features of proteins, lipids and nucleic acids. The presented method is capable of a highly accurate identification of OSCC. These findings suggest a high validity and reproducibility of SERDS combined with PCA and LDA analysis regarding oral cancer tissue.

Fourier transform infrared for noninvasive optical diagnosis of oral, oropharyngeal, and laryngeal cancer

The 5-year survival rate for advanced head and neck cancers is 50%. There is currently no noninvasive method or effective screening procedure available to diagnose head and neck cancer at the earliest stages when it is still highly curable. This study aims to show how Fourier transform infrared (FTIR) spectroscopy could be used as a sensitive, noninvasive, low cost technique to diagnose head and neck cancer at an earlier stage and, thus, increase the likelihood of survival. Sputum samples were collected from 16 cases with oral or oropharyngeal cancer, 8 cases with laryngeal cancer patients and 15 normal controls. Cell pellets were produced from each of these samples and used to generate FTIR spectra within the 'biochemical fingerprint' wavenumber region of 1800 to 950 cm(-1). Discrimination between cancer and normal sputum was achieved using infrared wavenumbers 1650 cm(-1), 1550 cm(-1), and 1042 cm(-1) determined by robust feature selection. These 3 wavenumbers were used to develop potential models to discriminate both oropharyngeal and laryngeal cancer from normal control. In cancer cases, the absorbance levels for 1550 cm(-1) were increased relative to controls, whereas 1042 cm(-1) absorbance was decreased suggesting changes to protein and glycoprotein structure within sputa cells. This preliminary study shows potential for how FTIR could be developed into a simplistic diagnostic tool that could easily be implemented by a nonspecialist to diagnose and monitor head and neck cancer. The method could especially provide a means for detecting laryngeal cancer hidden from noninvasive observation.

Profiling differences in chemical composition of brain structures using Raman spectroscopy

Raman spectroscopy enables non-invasive investigation of chemical composition of biological tissues. Due to similar chemical composition, the analysis of Raman spectra of brain structures and assignment of their spectral features to chemical constituents presents a particular challenge. In this study we demonstrate that standard and independent component analysis of Raman spectra is capable of assessment of differences in chemical composition between functionally related gray and white matter structures. Our results show the ability of Raman spectroscopy to successfully depict variation in chemical composition between structurally similar and/or functionally connected brain structures. The observed differences were attributed to variations in content of proteins and lipids in these structures. Independent component analysis enabled separation of contributions of major constituents in spectra and revealed spectral signatures of low-concentration metabolites. This provided finding of discrepancies between structures of striatum as well as between white matter structures. Raman spectroscopy can provide information about variations in contents of major chemical constituents in brain structures, while the application of independent component analysis performed on obtained spectra can help in revealing minute differences between closely related brain structures.

Design and analysis of a squamous cell carcinoma in vitro model system

Tissue-engineered skin equivalents based on primary isolated fibroblasts and keratinocytes have been shown to be useful tools for functional in vitro tests, including toxicological screenings and drug development. In this study, a commercially available squamous cell carcinoma (SCC) cell line SCC-25 was introduced into epidermal and full-thickness skin equivalents to generate human-based disease-in-a-dish model systems. Interestingly, when cultured either in the epidermis or dermis of full-thickness skin equivalents, SCC-25 cells formed hyper-keratinized tumor cell nests, a phenomenon that is frequently seen in the skin of patients afflicted with SCC. Raman spectroscopy was employed for the label-free cell phenotype characterization within the engineered skin equivalents and revealed the presence of differential protein patterns in keratinocytes and SCC-25 cells. To conclude, the here presented SSC disease-in-a-dish approaches offer the unique opportunity to model SSC in human skin in vitro, which will allow further insight into SSC disease progression, and the development of therapeutic strategies.

In vivo Raman spectroscopy of oral buccal mucosa: a study on malignancy associated changes (MAC)/cancer field effects (CFE)

Occurrence of metachronous and synchronous secondary tumors in oral cavities has been associated with poor prognosis and decreased 5-year disease-free survival rates. The origin of secondary tumors in the oral cavity has been primarily attributed to cancer field effects (CFE) or malignancy-associated changes (MAC) in uninvolved areas. Classification of normal, cancerous and pre-cancerous oral lesions by in vivo Raman spectroscopy (RS) has already been demonstrated. In the present study, MAC/CFE in oral buccal mucosa were explored. In vivo Raman spectra from 84 subjects (722 spectra) under five categories - cancer and contralateral normal (opposite side of tumor), healthy controls (no tobacco habit, no cancer), habitués healthy controls (tobacco habit, no cancer) and non-habitués contralateral normal (no tobacco habit with cancer) were acquired. Mean and difference spectra suggest that loss of lipids and additional features representing proteins and DNA are characteristics of all pathological conditions, with respect to healthy controls. Spectral data were analyzed by PC-LDA followed by leave-one-out cross-validation. Results suggest that Raman characteristics of mucosa of healthy controls are exclusive, while those of habitués healthy controls are similar to those of contralateral normal mucosa. It was observed that the cluster of non-habitués contralateral normal mucosa is different from habitués healthy controls, suggesting that malignancy associated changes can be identified and also indicating that transformation of uninvolved oral mucosa due to tobacco habit or malignancy is different. The findings of the study demonstrate the potential of RS in identifying early transformation changes in oral mucosa and the efficacy of this approach in oral cancer applications.

Multimodal nonlinear microscopic investigations on head and neck squamous cell carcinoma: toward intraoperative imaging

BACKGROUND

Prognosis and appropriate treatment of head and neck squamous cell carcinoma (HNSCC) depend on the tumor type routinely derived by invasive histopathology. A promising noninvasive alternative is nonlinear optical imaging, which is capable of in vivo tissue visualization for tumor typing and grading.

METHODS AND RESULTS

Thin tissue sections from 3 patients aged 56 to 60 years presenting advanced carcinoma of the hypopharynx, larynx, and left tonsil were investigated by coherent anti-Stokes Raman scattering (CARS), second-harmonic generation (SHG), and 2 photon excited fluorescence (TPEF) to study the morphochemistry of the tissues. Morphologic alterations of prognostic significance, such as cell density, nuclear to cytoplasm ratio, and keratinization as well as the underlying compositional changes during malignant transformation were determined, such as the distributions of lipids, collagen, and autofluorophors.

CONCLUSIONS

Nonlinear imaging provides a noninvasive optical biopsy of the epithelial layer comparable to staining microscopy. By integration into an operational microscope, routine screening of suspicious lesions and surgical guidance can be realized.

In vivo, real-time, transnasal, image-guided Raman endoscopy: defining spectral properties in the nasopharynx and larynx

We report for the first time the implementation of transnasal, image-guided Raman endoscopy to directly assess Raman spectral properties of nasopharyngeal and laryngeal tissue in vivo during clinical endoscopic examinations. A rapid 785-nm excitation Raman endoscopy system, coupled with a miniaturized fiber-optic Raman probe, was utilized for real-time, in vivo Raman measurements of different anatomical locations in the head and neck. A total of 874 high-quality in vivo Raman spectra were successfully acquired from different anatomic locations of the nasopharynx and larynx [i.e., posterior nasopharynx (PN) (n=521), the fossa of Rosenmüller (FOR) (n=157), and true laryngeal vocal chords (LVC) (n=196)] in 23 normal subjects at transnasal endoscopy. Difference spectra and principal component analysis (PCA) were employed for tissue characterization, uncovering the tissue variability at the biomolecular level. The PCA-linear discriminant analysis (LDA) provides sensitivity of 77.0% and specificity of 89.2% for differentiation between PN and FOR, and sensitivity of 68.8% and specificity of 76.0% for distinguishing LVC and PN using the leave-one-subject-out, cross-validation method. This work demonstrates that transnasal, image-guided Raman endoscopy can be used to acquire in vivo Raman spectra from the nasopharynx and larynx in real time. Significant Raman spectral differences (p<0.05) identified as reflecting the distinct composition and morphology in the nasopharynx and larynx should be considered to be important parameters in the interpretation and rendering of diagnostic decision algorithms for in vivo tissue diagnosis and characterization in the head and neck.

Raman spectroscopy of normal oral buccal mucosa tissues: study on intact and incised biopsies

Oral squamous cell carcinoma is one of among the top 10 malignancies. Optical spectroscopy, including Raman, is being actively pursued as alternative/adjunct for cancer diagnosis. Earlier studies have demonstrated the feasibility of classifying normal, premalignant, and malignant oral ex vivo tissues. Spectral features showed predominance of lipids and proteins in normal and cancer conditions, respectively, which were attributed to membrane lipids and surface proteins. In view of recent developments in deep tissue Raman spectroscopy, we have recorded Raman spectra from superior and inferior surfaces of 10 normal oral tissues on intact, as well as incised, biopsies after separation of epithelium from connective tissue. Spectral variations and similarities among different groups were explored by unsupervised (principal component analysis) and supervised (linear discriminant analysis, factorial discriminant analysis) methodologies. Clusters of spectra from superior and inferior surfaces of intact tissues show a high overlap; whereas spectra from separated epithelium and connective tissue sections yielded clear clusters, though they also overlap on clusters of intact tissues. Spectra of all four groups of normal tissues gave exclusive clusters when tested against malignant spectra. Thus, this study demonstrates that spectra recorded from the superior surface of an intact tissue may have contributions from deeper layers but has no bearing from the classification of a malignant tissues point of view.