Discriminating basal cell carcinoma from perilesional skin using high wave-number Raman spectroscopy

Portable System for In-Clinic Differentiation of Skin Cancers from Benign Skin Lesions and Inflammatory Dermatoses

The exquisite sensitivity of Raman spectroscopy for detecting biomolecular changes in skin cancer has previously been explored; however, this mostly required analysis of excised tissue samples using bulky, immobile laboratory instrumentation. In this study, the technique was translated for clinical use with a portable Raman system and customized fiber optic probe and applied to differentiation of skin cancers from benign lesions and inflammatory dermatoses. The aim was to provide an easy-to-use, easy-to-manage assessment tool for clinicians to use in their daily patient examination routine to perform rapid Raman measurements of skin lesions in vivo. Using this system, >867 spectra were measured in vivo from 330 patients with a wide variety of different benign skin lesions (n = 603), inflammatory dermatoses (n = 140), and skin cancers (n = 124). Ethnicities represented were 70% European; 16% Asian; 6% Māori; 5% Pacific people; and 4% Middle East, Latin American, and African. Accurate differentiation of skin cancers from benign lesions and inflammatory dermatoses was achieved using partial least squares discriminant analysis, with area under curve for the receiver operator curves for external validation sets ranging from 0.916 to 0.958. This study shows evidence for robust clinical translation of Raman spectroscopy for rapid, accurate diagnosis of skin cancer.

Measurement of chemical penetration in skin using Stimulated Raman scattering microscopy and multivariate curve resolution - alternating least squares

The mechanistic understanding of skin penetration underpins the design, efficacy and risk assessment of many high-value products including functional personal care products, topical and transdermal drugs. Stimulated Raman scattering (SRS) microscopy, a label free chemical imaging tool, combines molecular spectroscopy with submicron spatial information to map the distribution of chemicals as they penetrate the skin. However, the quantification of penetration is hampered by significant interference from Raman signals of skin constituents. This study reports a method for disentangling exogeneous contributions and measuring their permeation profile through human skin combining SRS measurements with chemometrics. We investigated the spectral decomposition capability of multivariate curve resolution - alternating least squares (MCR-ALS) using hyperspectral SRS images of skin dosed with 4-cyanophenol. By performing MCR-ALS on the fingerprint region spectral data, the distribution of 4-cyanophenol in skin was estimated in an attempt to quantify the amount permeated at different depths. The reconstructed distribution was compared with the experimental mapping of CN, a strong vibrational peak in 4-cyanophenol where the skin is spectroscopically silent. The similarity between MCR-ALS resolved and experimental distribution in skin dosed for 4 h was 0.79 which improved to 0.91 for skin dosed for 1 h. The correlation was observed to be lower for deeper layers of skin where SRS signal intensity is low which is an indication of low sensitivity of SRS. This work is the first demonstration, to the best of our knowledge, of combining SRS imaging technique with spectral unmixing methods for direct observation and mapping of the chemical penetration and distribution in biological tissues.

Autofluorescence-Raman Spectroscopy for Ex Vivo Mapping Colorectal Liver Metastases and Liver Tissue

INTRODUCTION

Identifying colorectal liver metastases (CRLM) during liver resection could assist in achieving clear surgical margins, which is an important prognostic variable for both disease-free and overall survival. The aim of this study was to investigate the effect of auto-fluorescence (AF) and Raman spectroscopy for ex vivo label-free discrimination of CRLMs from normal liver tissue. Secondary aims include exploring options for multimodal AF-Raman integration with respect to diagnosis accuracy and imaging speed on human liver tissue and CRLM.

METHODS

Liver samples were obtained from patients undergoing liver surgery for CRLM who provided informed consent (15 patients were recruited). AF and Raman spectroscopy was performed on CRLM and normal liver tissue samples and then compared to histology.

RESULTS

AF emission spectra demonstrated that the 671 nm and 775/785 nm excitation wavelengths provided the highest contrast, as normal liver tissue elicited on average around eight-fold higher AF intensity compared to CRLM. The use of the 785 nm wavelength had the advantage of enabling Raman spectroscopy measurements from CRLM regions, allowing discrimination of CRLM from regions of normal liver tissue eliciting unusual low AF intensity, preventing misclassification. Proof-of-concept experiments using small pieces of CRLM samples covered by large normal liver tissue demonstrated the feasibility of a dual-modality AF-Raman for detection of positive margins within few minutes.

CONCLUSIONS

AF imaging and Raman spectroscopy can discriminate CRLM from normal liver tissue in an ex vivo setting. These results suggest the potential for developing integrated multimodal AF-Raman imaging techniques for intraoperative assessment of surgical margins.

Combining fiber optical tweezers and Raman spectroscopy for rapid identification of melanoma

Cutaneous melanoma is a skin tumor with a high degree of malignancy and fatality rate, the incidence of which has increased in recent years. Therefore, a rapid and sensitive diagnostic technique of melanoma cells is urgently needed. In this paper, we present a new approach using fiber optical tweezers to manipulate melanoma cells to measure their Raman spectra. Then, combined with Principal Component Analysis and Support Vector Machines (PCA-SVM) classification model, to achieve the classification of common mutant, wild-type and drug-resistant melanoma cells. A total of 150 Raman spectra of 30 cells were collected from mutant, wild-type and drug-resistant melanoma cell lines, and the classification accuracy was 92%, 94%, 97.5%, respectively. These results suggest that the study of tumor cells based on fiber optical tweezers and Raman spectroscopy is a promising method for early and rapid identification and diagnosis of tumor cells.

Novel aspects of Raman spectroscopy in skin research

The analytical technology of Raman spectroscopy has an almost 100‐year history. During this period, many modifications and developments happened in the method like discovery of laser, improvements in optical elements and sensitivity of spectrometer and also more advanced light detection systems. Many types of the innovative techniques appeared (e.g. Transmittance Raman spectroscopy, Coherent Raman Scattering microscopy, Surface‐Enhanced Raman scattering and Confocal Raman spectroscopy/microscopy). This review article gives a short description about these different Raman techniques and their possible applications. Then, a short statistical part is coming about the appearance of Raman spectroscopy in the scientific literature from the beginnings to these days. The third part of the paper shows the main application options of the technique (especially confocal Raman spectroscopy) in skin research, including skin composition analysis, drug penetration monitoring and analysis, diagnostic utilizations in dermatology and cosmeto‐scientific applications. At the end, the possible role of artificial intelligence in Raman data analysis and the regulatory aspect of these techniques in dermatology are briefly summarized. For the future of Raman Spectroscopy, increasing clinical relevance and in vivo applications can be predicted with spreading of non‐destructive methods and appearance with the most advanced instruments with rapid analysis time.

Lung Cancer: Spectral and Numerical Differentiation among Benign and Malignant Pleural Effusions Based on the Surface-Enhanced Raman Spectroscopy

We present here that the surface-enhanced Raman spectroscopy (SERS) technique in conjunction with the partial least squares analysis is as a potential tool for the differentiation of pleural effusion in the course of the cancerous disease and a tool for faster diagnosis of lung cancer. Pleural effusion occurs mainly in cancer patients due to the spread of the tumor, usually caused by lung cancer. Furthermore, it can also be initiated by non-neoplastic diseases, such as chronic inflammatory infection (the most common reason for histopathological examination of the exudate). The correlation between pleural effusion induced by tumor and non-cancerous diseases were found using surface-enhanced Raman spectroscopy combined with principal component regression (PCR) and partial least squares (PLS) multivariate analysis method. The PCR predicts 96% variance for the division of neoplastic and non-neoplastic samples in 13 principal components while PLS 95% in only 10 factors. Similarly, when analyzing the SERS data to differentiate the type of tumor (squamous cell vs. adenocarcinoma), PLS gives more satisfactory results. This is evidenced by the calculated values of the root mean square errors of calibration and prediction but also the coefficients of calibration determination and prediction (R2C = 0.9570 and R2C = 0.7968), which are more robust and rugged compared to those calculated for PCR. In addition, the relationship between cancerous and non-cancerous samples in the dependence on the gender of the studied patients is presented.

Raman Spectroscopy: A Personalized Decision-Making Tool on Clinicians' Hands for In Situ Cancer Diagnosis and Surgery Guidance

Simple Summary

Cancer still constitutes one of the main global health challenges. Novel approaches towards understanding the molecular composition of the disease can be employed as adjuvant tools to current oncological applications. Raman spectroscopy has been contemplated and pursued to serve as a noninvasive, real time, in vivo tool which may uncover the molecular basis of cancer and simultaneously offer high specificity, sensitivity, and multiplexing capacity, as well as high spatial and temporal resolution. In this review, the potential impact of Spontaneous Raman spectroscopy in clinical applications related to cancer diagnosis and surgical removal is analyzed. Moreover, the coupling of Raman systems with modern instrumentation and machine learning methods has been explored as a prominent enhancement factor towards a personalized approach promoting objectivity and accuracy in surgical oncology.

Abstract

Accurate in situ diagnosis and optimal surgical removal of a malignancy constitute key elements in reducing cancer-related morbidity and mortality. In surgical oncology, the accurate discrimination between healthy and cancerous tissues is critical for the postoperative care of the patient. Conventional imaging techniques have attempted to serve as adjuvant tools for in situ biopsy and surgery guidance. However, no single imaging modality has been proven sufficient in terms of specificity, sensitivity, multiplexing capacity, spatial and temporal resolution. Moreover, most techniques are unable to provide information regarding the molecular tissue composition. In this review, we highlight the potential of Raman spectroscopy as a spectroscopic technique with high detection sensitivity and spatial resolution for distinguishing healthy from malignant margins in microscopic scale and in real time. A Raman spectrum constitutes an intrinsic “molecular finger-print” of the tissue and any biochemical alteration related to inflammatory or cancerous tissue state is reflected on its Raman spectral fingerprint. Nowadays, advanced Raman systems coupled with modern instrumentation devices and machine learning methods are entering the clinical arena as adjunct tools towards personalized and optimized efficacy in surgical oncology.

Characterization of Ex Vivo Nonmelanoma Skin Tissue Using Raman Spectroscopy

Raman spectroscopy has shown great potential in detecting nonmelanoma skin cancer accurately and quickly; however, little direct evidence exists on the sensitivity of measurements to the underlying anatomy. Here, we aimed to correlate Raman measurements directly to the underlying tissue anatomy. We acquired Raman spectra of ex vivo skin tissue from 25 patients undergoing Mohs surgery with a fiber probe. We utilized a previously developed biophysical model to extract key biomarkers in the skin from the Raman spectra. We then examined the correlations between the biomarkers and the major skin structures (including the dermis, sebaceous glands, hair follicles, fat, and two types of nonmelanoma skin cancer—basal cell carcinoma (BCC) and squamous cell carcinoma (SCC)). SCC had a significantly different concentration of keratin, collagen, and nucleic acid than normal structures, while ceramide differentiated BCC from normal structures. Our findings identified the key proteins, lipids, and nucleic acids that discriminate nonmelanoma tumors and healthy skin using Raman spectroscopy. These markers may be promising surgical guidance tools for detecting tumors in resection margins.

Selective-sampling Raman imaging techniques for ex vivo assessment of surgical margins in cancer surgery

One of the main challenges in cancer surgery is to ensure the complete excision of the tumour while sparing as much healthy tissue as possible. Histopathology, the gold-standard technique used to assess the surgical margins on the excised tissue, is often impractical for intra-operative use because of the time-consuming tissue cryo-sectioning and staining, and availability of histopathologists to assess stained tissue sections. Raman micro-spectroscopy is a powerful technique that can detect microscopic residual tumours on ex vivo tissue samples with accuracy, based entirely on intrinsic chemical differences. However, raster-scanning Raman micro-spectroscopy is a slow imaging technique that typically requires long data acquisition times wich are impractical for intra-operative use. Selective-sampling Raman imaging overcomes these limitations by using information regarding the spatial properties of the tissue to reduce the number of Raman spectra. This paper reviews the latest advances in selective-sampling Raman techniques and applications, mainly based on multimodal optical imaging. We also highlight the latest results of clinical integration of a prototype device for non-melanoma skin cancer. These promising results indicate the potential impact of Raman spectroscopy for providing fast and objective assessment of surgical margins, helping surgeons ensure the complete removal of tumour cells while sparing as much healthy tissue as possible.

Unveiling Cancer Metabolism through Spontaneous and Coherent Raman Spectroscopy and Stable Isotope Probing

Metabolic reprogramming is a common hallmark in cancer. The high complexity and heterogeneity in cancer render it challenging for scientists to study cancer metabolism. Despite the recent advances in single-cell metabolomics based on mass spectrometry, the analysis of metabolites is still a destructive process, thus limiting in vivo investigations. Being label-free and nonperturbative, Raman spectroscopy offers intrinsic information for elucidating active biochemical processes at subcellular level. This review summarizes recent applications of Raman-based techniques, including spontaneous Raman spectroscopy and imaging, coherent Raman imaging, and Raman-stable isotope probing, in contribution to the molecular understanding of the complex biological processes in the disease. In addition, this review discusses possible future directions of Raman-based technologies in cancer research.

Risk prediction by Raman spectroscopy for disease-free survival in oral cancers

In the present study, the potential of Raman spectroscopy (RS) in predicting disease-free survival (DFS) in oral cancer patients has been explored. Raman spectra were obtained from the tumor and contralateral regions of 94 oral squamous cell carcinoma patients. These patients were managed surgically and recommended for adjuvant therapy. The Cox proportional survival analysis was carried out to identify the spectral regions that can be correlated to DFS. The survival analysis was performed with 95% confidence intervals, hazard ratio, and p-values in the 1200-1800 cm^-1 spectral region. Out of a total of 182 spectral points, 76 were found to be correlating with DFS, suggesting their utility to predict the patient outcome. The cut-off points of each correlating RS-point values were defined and tested towards predicting the DFS. The performance of predicting the power of spectral points was validated through Brier value, and it was found to be closer to the actual progression. The 76 spectral points identified from the tumors have the potential to accurately predict DFS in oral squamous cell carcinoma through a relatively simplistic prediction model in the absence of confounding factors.

Fiber-based SORS-SERDS system and chemometrics for the diagnostics and therapy monitoring of psoriasis inflammatory disease in vivo

Psoriasis is considered a widespread dermatological disease that can strongly affect the quality of life. Currently, the treatment is continued until the skin surface appears clinically healed. However, lesions appearing normal may contain modifications in deeper layers. To terminate the treatment too early can highly increase the risk of relapses. Therefore, techniques are needed for a better knowledge of the treatment process, especially to detect the lesion modifications in deeper layers. In this study, we developed a fiber-based SORS-SERDS system in combination with machine learning algorithms to non-invasively determine the treatment efficiency of psoriasis. The system was designed to acquire Raman spectra from three different depths into the skin, which provide rich information about the skin modifications in deeper layers. This way, it is expected to prevent the occurrence of relapses in case of a too short treatment. The method was verified with a study of 24 patients upon their two visits: the data is acquired at the beginning of a standard treatment (visit 1) and four months afterwards (visit 2). A mean sensitivity of ≥85% was achieved to distinguish psoriasis from normal skin at visit 1. At visit 2, where the patients were healed according to the clinical appearance, the mean sensitivity was ≈65%.

Raman spectroscopy for rapid intra-operative margin analysis of surgically excised tumour specimens

Raman spectroscopy, a form of vibrational spectroscopy, has the ability to provide sensitive and specific biochemical analysis of tissue. This review article provides an in-depth analysis of the suitability of different Raman spectroscopy techniques in providing intra-operative margin analysis in a range of solid tumour pathologies. Surgical excision remains the primary treatment of a number of solid organ cancers. Incomplete excision of a tumour and positive margins on histopathological analysis is associated with a worse prognosis, the need for adjuvant therapies with significant side effects and a resulting financial burden. The provision of intra-operative margin analysis of surgically excised tumour specimens would be beneficial for a number of pathologies, as there are no widely adopted and accurate methods of margin analysis, beyond histopathology. The limitations of Raman spectroscopic studies to date are discussed and future work necessary to enable translation to clinical use is identified. We conclude that, although there remain a number of challenges in translating current techniques into a clinically effective tool, studies so far demonstrate that Raman Spectroscopy has the attributes to successfully perform highly accurate intra-operative margin analysis in a clinically relevant environment.

Brain tumour homogenates analysed by surface-enhanced Raman spectroscopy: Discrimination among healthy and cancer cells

One of the biggest challenge for modern medicine is to make a discrimination among healthy and cancerous tissues. Therefore, nowadays big effort of scientist are devoted to find a new way for as fast as possible diagnosis with as much as possible accuracy in distinguishing healthy from cancerous tissues. That issues are probably the most important in the case of brain tumours, when the diagnosis time plays a great role. Herein we present the surface-enhanced Raman spectroscopy (SERS) together with the principal component analysis (PCA) used to identify the spectra of different brain specimens, healthy and tumour tissues homogenates. The presented analyses include three sets of brain tissues as control samples taken from healthy objects (one set consists of samples from four brain lobes and both hemispheres; eight samples) and the brain tumours from five patients (two Anaplastic Astrocytoma and three Glioblastoma samples). Results prove that tumour brain samples can be discriminated well from the healthy tissues by using only three main principal components, with 96% of accuracy. The largest influence onto the calculated separation is attributed to the spectral regions corresponding in SERS spectra to vibrations of the L-Tryptophan (1450, 1278 cm^-1), protein (1300 cm^-1), phenylalanine and Amide-I (1005, 1654 cm^-1). Therefore, the presented method may open the way for the probable application as a very fast diagnosis tool alternative for conventionally used histopathology or even more as an intraoperative diagnostic tool during brain tumour surgery.

Recent Advances in Spontaneous Raman Spectroscopic Imaging: Instrumentation and Applications

BACKGROUND

Spectroscopic imaging based on the spontaneous Raman scattering effects can provide unique fingerprint information in relation to the vibration bands of molecules. Due to its advantages of high chemical specificity, non-invasive detection capability, low sensitivity to water, and no special sample pretreatment, Raman Spectroscopic Imaging (RSI) has become an invaluable tool in the field of biomedicine and medicinal chemistry.

METHODS

There are three methods to implement RSI, including point scanning, line scanning and wide-field RSI. Point-scanning can achieve two-and three-dimensional imaging of target samples. High spectral resolution, full spectral range and confocal features render this technique highly attractive. However, point scanning based RSI is a time-consuming process that can take several hours to map a small area. Line scanning RSI is an extension of point scanning method, with an imaging speed being 300-600 times faster. In the wide-field RSI, the laser illuminates the entire region of interest directly and all the images then collected for analysis. In general, it enables more accurate chemical imaging at faster speeds.

RESULTS

This review focuses on the recent advances in RSI, with particular emphasis on the latest developments on instrumentation and the related applications in biomedicine and medicinal chemistry. Finally, we prospect the development trend of RSI as well as its potential to translation from bench to bedside.

CONCLUSION

RSI is a powerful technique that provides unique chemical information, with a great potential in the fields of biomedicine and medicinal chemistry.

An early investigative serum Raman spectroscopy study of meningioma

Meningiomas represent one of the most frequently reported non-glial, primary brain and central nervous system (CNS) tumors. Meningiomas often display a spectrum of anomalous locations and morphological attributes, deterring their timely diagnosis. Majority of them are sporadic in nature and thus the present-day screening strategies, including radiological investigations, often result in misdiagnosis due to their aberrant and equivocal radiological facets. Therefore, it is pertinent to explore less invasive and patient-friendly biofluids such as serum for their screening and diagnostics. The utility of serum Raman spectroscopy in diagnosis and therapeutic monitoring of cancers has been reported in the literature. In the present study, for the first time, to the best of our knowledge, we have explored Raman spectroscopy to classify the sera of meningioma and control subjects. For this exploration, 35 samples each of meningioma and control subjects were accrued and the spectra revealed variance in the levels of DNA, proteins, lipids, amino acids and β-carotene, i.e., a relatively higher protein, DNA and lipid content in meningioma. Subsequent Principal Component Analysis (PCA) and Principal Component-Linear Discriminant Analysis (PC-LDA) followed by Leave-One-Out Cross-Validation (LOOCV) and limited independent test data, in a patient-wise approach, yielded a classification efficiency of 92% and 80% for healthy and meningioma, respectively. Additionally, in the analogous analysis between healthy and different grades of meningioma, similar results were obtained. These results indicate the potential of Raman spectroscopy in differentiating meningioma. As present methods suffer from known limitations, with the prospective validation on a larger cohort, serum Raman spectroscopy could be an adjuvant/alternative approach in the clinical management of meningioma.

Improving clinical diagnosis of early-stage cutaneous melanoma based on Raman spectroscopy

Background

Clinical diagnosis of early melanoma (Breslow thickness less than 0.8 mm) is crucial to disease-free survival. However, it is subjective and can be exceedingly difficult, leading to missed melanomas, or unnecessary excision of benign pigmented skin lesions. An objective technique is needed to improve the diagnosis of early melanoma.

Methods

We have developed a method to improve diagnosis of (thin) melanoma, based on Raman spectroscopy. In an ex vivo study in a tertiary referral (pigmented lesions) centre, high-wavenumber Raman spectra were collected from 174 freshly excised melanocytic lesions suspicious for melanoma. Measurements were performed on multiple locations within the lesions. A diagnostic model was developed and validated on an independent data set of 96 lesions.

Results

Approximately 60% of the melanomas included in this study were melanomas in situ. The invasive melanomas had an average Breslow thickness of 0.89 mm. The diagnostic model correctly classified all melanomas (including in situ) with a specificity of 43.8%, and showed a potential improvement of the number needed to treat from 6.0 to 2.7, at a sensitivity of 100%.

Conclusion

This work signifies an important step towards accurate and objective clinical diagnosis of melanoma and in particular melanoma with Breslow thickness <0.8 mm.

Raman spectroscopy with a 1064-nm wavelength laser as a potential molecular tool for prostate cancer diagnosis: a pilot study

Raman spectroscopy is widely used to investigate the structure and property of the molecules from their vibrational transitions and may allow for the diagnosis of cancer in a fast, objective, and nondestructive manner. This experimental study aims to propose the use of the 1064-nm wavelength laser in a Raman spectroscopy and to evaluate its discrimination capability in prostate cancer diagnosis. Seventy-four spectra from patients who underwent radical prostatectomy were evaluated. The acquired signals were filtered, normalized, and corrected for possible oscillations in the laser intensity and fluorescence effects. Wilcoxon tests revealed significant differences between the benign and malign samples associated with the deformation vibration characteristic of nucleic acids, proteins, and lipids. A classifier based on support vector machines was able to predict the Gleason scores of the samples with 95% of accuracy, opening a perspective for the use of the 1064-nm excitatory wavelength in prostatic cancer diagnosis.

Accuracy of Raman spectroscopy for differentiating skin cancer from normal tissue

BACKGROUND

Raman spectroscopy could be applied to distinguish tumor from normal tissues. This meta-analysis assessed the accuracy of Raman spectroscopy in differentiating skin cancer from normal tissue.

METHODS

PubMed, Embase, Cochrane Library, and CNKI were searched to identify suitable studies before Februray 4th, 2018. We estimated the pooled sensitivity, specificity, positive, and negative likelihood ratios, diagnostic odds ratio, and constructed summary receiver-operating characteristics curves to identify the accuracy of Raman spectroscopy in differentiating skin cancer from normal tissue.

RESULTS

A total of 12 studies with 2461 spectra were included. For basal cell skin cancer (BCC) ex vivo detection, the pooled sensitivity and specificity were 0.99 (95% confidence interval [CI] 0.97-0.99) and 0.96 (95% CI 0.95-0.97), respectively. The area under the curve (AUC) was 0.9837. For BCC in vivo detection, the pooled sensitivity and specificity were 0.69 (95% CI 0.61-0.76) and 0.85 (95% CI 0.82-0.87), respectively. The AUC was 0.9213. For melanoma (MM) ex vivo detection, the pooled sensitivity and specificity were 1.00 (95% CI 0.91-1.00) and 0.98 (95% CI 0.95-1.00), respectively. The AUC was 0.9914. For MM in vivo detection, the sensitivity (0.93) and the specificity (0.96) balanced relatively well. For squamous cell skin cancer (SCC) ex vivo detection, the pooled sensitivity and specificity were 0.96 (95% CI 0.81-1.00) and 1.00 (95% CI 0.92-1.00), respectively. For SCC in vivo detection, the sensitivity was 0.81 (95% CI 0.70-0.90) and the specificity was 0.89 (95% CI 0.86-0.91).

CONCLUSION

This meta-analysis suggested that Raman spectroscopy could be an effective and accurate tool for differentiating BCC, MM, SCC from normal tissue, which would assist us in the diagnosis and treatment of skin cancer.

Histological coherent Raman imaging: a prognostic review

Histopathology plays a central role in diagnosis of many diseases including solid cancers. Efforts are underway to transform this subjective art to an objective and quantitative science. Coherent Raman imaging (CRI), a label-free imaging modality with sub-cellular spatial resolution and molecule-specific contrast possesses characteristics which could support the qualitative-to-quantitative transition of histopathology. In this work we briefly survey major themes related to modernization of histopathology, review applications of CRI to histopathology and, finally, discuss potential roles for CRI in the transformation of histopathology that is already underway.

Biological and Medical Applications of Multivariate Curve Resolution Assisted Raman Spectroscopy

Biological specimens such as cells, tissues and biofluids (urine, blood) contain mixtures of many different biomolecules, all of which contribute to a Raman spectrum at any given point. The separation and identification of pure biochemical components remains one of the biggest challenges in Raman spectroscopy. Multivariate curve resolution, a matrix factorization method, is a powerful, yet flexible, method that can be used with constraints, such as non-negativity, to decompose a complex spectroscopic data matrix into a small number of physically meaningful pure spectral components along with their relative abundances. This paper reviews recent applications of multivariate curve resolution by alternating least squares analysis to Raman spectroscopic and imaging data obtained either in vivo or in vitro from biological and medical samples.

Raman Spectroscopy and Imaging for Cancer Diagnosis

Raman scattering has long been used to analyze chemical compositions in biological systems. Owing to its high chemical specificity and noninvasive detection capability, Raman scattering has been widely employed in cancer screening, diagnosis, and intraoperative surgical guidance in the past ten years. In order to overcome the weak signal of spontaneous Raman scattering, coherent Raman scattering and surface-enhanced Raman scattering have been developed and recently applied in the field of cancer research. This review focuses on innovative studies of the use of Raman scattering in cancer diagnosis and their potential to transition from bench to bedside.

Using Raman Spectroscopy to Detect and Diagnose Skin Cancer In Vivo

Raman spectroscopy provides a noninvasive bedside tool that captures unique optical signals via molecular vibrations in tissue samples. Raman theory was discovered at the beginning of the twentieth century, but it was not until the past few decades that it has been used to differentiate skin neoplasms. We provide a brief description of Raman spectroscopy for in vivo skin cancer diagnosis, including the physical principles underlying Raman spectroscopy, its advantages, typical spectra of skin pathologies, and its clinical application for aiding skin cancer diagnosis.

A comparative evaluation of diffuse reflectance and Raman spectroscopy in the detection of cervical cancer

Optical spectroscopic techniques show improved diagnostic accuracy for non-invasive detection of cervical cancers. In this study, sensitivity and specificity of two in vivo modalities, i.e diffuse reflectance spectroscopy (DRS) and Raman spectroscopy (RS), were compared by utilizing spectra recorded from the same sites (67 tumor (T), 22 normal cervix (C), and 57 normal vagina (V)). Data was analysed using principal component - linear discriminant analysis (PC-LDA), and validated using leave-one-out-cross-validation (LOOCV). Sensitivity, specificity, positive predictive value and negative predictive value for classification between normal (N) and tumor (T) sites were 91%, 96%, 95% and 93%, respectively for RS and 85%, 95%, 93% and 88%, respectively for DRS. Even though DRS revealed slightly lower diagnostic accuracies, owing to its lower cost and portability, it was found to be more suited for cervical cancer screening in low resource settings. On the other hand, RS based devices could be ideal for screening patients with centralised facilities in developing countries.

Sensitive Skin: Assessment of the Skin Barrier Using Confocal Raman Microspectroscopy

BACKGROUND/AIMS

Sensitive skin (SS), a frequently reported condition in the Western world, has been suggested to be underlined by an impaired skin barrier. The aim of this study was to investigate the skin barrier molecular composition in SS subjects using confocal Raman microspectroscopy (CRS), and to compare it with that of non-SS (NSS) individuals as well as atopic dermatitis (AD) and allergic rhinoconjunctivitis (AR) subjects, who frequently report SS.

METHODS

Subjects with SS (n = 29), NSS (n = 30), AD (n = 11), and AR (n = 27) were included. Stratum corneum (SC) thickness, water, ceramides/fatty acids, and natural moisturizing factor (NMF) were measured by CRS along with transepidermal water loss and capacitance on the ventral forearm, thenar, and cheek. Sebum levels were additionally measured on the forearm and cheek.

RESULTS

No differences between SS and NSS subjects were found regarding SC thickness, water, and NMF content, yet a trend towards lower ceramides/fatty acids was observed in the cheek. Compared to AD subjects, the SS group showed higher ceramides/fatty acid content in the forearm, whereas no differences emerged with AR. The correlation of macroscopic biophysical techniques and CRS was weak, yet CRS confirmed the well-known lower content of NMF and water, and thinner SC in subjects with filaggrin mutations.

CONCLUSION

The skin barrier in SS is not impaired in terms of SC thickness, water, NMF, and ceramides/fatty acid content. The failure of biophysical techniques to follow alterations in the molecular composition of the skin barrier revealed by CRS emphasizes a strong need in sensitive and specific tools for in vivo skin barrier analysis.

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.

Raman technologies in cancer diagnostics

Despite significant effort, cancer still remains a leading cause of death worldwide. In order to reduce its burden, the development and improvement of noninvasive strategies for early detection and diagnosis of cancer are urgently needed. Raman spectroscopy, an optical technique that relies on inelastic light scattering arising from molecular vibrations, is one such strategy, as it can noninvasively probe cancerous markers using only endogenous contrast. In this review, spontaneous, coherent and surface enhanced Raman spectroscopies and imaging, as well as the fundamental principles governing the successful use of these techniques, are discussed. Methods for spectral data analysis are also highlighted. Utilization of the discussed Raman techniques for the detection and diagnosis of cancer in vitro, ex vivo and in vivo is described. The review concludes with a discussion of the future directions of Raman technologies, with particular emphasis on their clinical translation.

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.

Raman Spectroscopic Characterization of Melanoma and Benign Melanocytic Lesions Suspected of Melanoma Using High-Wavenumber Raman Spectroscopy

Melanoma is a pigmented type of skin cancer, which has the highest mortality of all skin cancers. Because of the low clinical diagnostic accuracy for melanoma, an objective tool is needed to assist clinical assessment of skin lesions that are suspected of (early) melanoma. The aim of this study was to identify spectral differences in the CH region of HWVN (high-wavenumber) Raman spectra between melanoma and benign melanocytic lesions clinically suspected of melanoma. We used these spectral differences to explore preliminary classification models to distinguish melanoma from benign melanocytic lesions. Data from 82 freshly excised melanocytic lesions clinically suspected of melanoma were measured using an in-house built Raman spectrometer, which has been optimized for measurements on pigmented skin lesions (excitation wavelength 976 nm and a wavelength range of the Raman signal 1340-1540 nm). Clear spectral differences were observed between melanoma and benign melanocytic lesions. These differences can be assigned mainly to the symmetric CH2 stretching vibrations of lipids. Our results show that the Raman bands between 2840 and 2930 cm(-1) have increased intensity for melanoma when compared to benign melanocytic lesions, suggesting an increase in lipid content in melanoma. These results demonstrate that spectroscopic information in the CH-stretching region of HWVN Raman spectra can discriminate melanoma from benign melanocytic lesions that are often clinically misdiagnosed as melanoma and that Raman spectroscopy has the potential to provide an objective clinical tool to improve the clinical diagnostic accuracy of skin lesions suspected of melanoma.

Raman Spectroscopy: Incorporating the Chemical Dimension into Dermatological Diagnosis

Raman spectroscopy provides chemical analysis of tissue in vivo. By measuring the inelastic interactions of light with matter, Raman spectroscopy can determine the chemical composition of a sample. Diseases that are visually difficult to visually distinguish can be delineated based on differences in chemical composition of the affected tissue. Raman spectroscopy has successfully found spectroscopic signatures for skin cancers and differentiated those of benign skin growths. With current and on-going advances in optics and computing, inexpensive and effective Raman systems may soon be available for clinical use. Raman spectroscopy provides direct analyses of skin lesions, thereby improving both disease diagnosis and management.

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.

Novel approaches to imaging basal cell carcinoma

The gold standard of diagnosis for nonmelanoma and melanoma skin cancer has been skin biopsy with routine paraffin embedded hematoxylin and eosin histopathology. This practice is frequently carried out on suspicious lesions to rule out a malignant process. Therefore, as a result, many biopsies are done on benign lesions. Unlike other fields of medicine that rely on noninvasive imaging modalities, the use of imaging devices in dermatology has not been as robust. This has been mainly due to the limited resolution offered by imaging devices that is needed to detect malignant changes in the cutaneous layers. However, the demand for more efficient in vivo and ex vivo imaging tools to reduce the amount of biopsies have led to new areas of investigation using noninvasive modalities to augment the clinical diagnosis of skin cancer. The use of noninvasive imaging both in vivo and ex vivo has the potential to increase efficiency of diagnosis and management, decrease healthcare cost, improve clinical care and enhance patient satisfaction.

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.

Applications of Raman spectroscopy in skin research--From skin physiology and diagnosis up to risk assessment and dermal drug delivery

In the field of skin research, confocal Raman microscopy is an upcoming analytical technique. Substantial technical progress in design and performance of the individual setup components like detectors and lasers as well as the combination with confocal microscopy enables chemically selective and non-destructive sample analysis with high spatial resolution in three dimensions. Due to these advantages, the technique bears tremendous potential for diverse skin applications ranging from the analysis of physiological component distribution in skin tissue and the diagnosis of pathological states up to biopharmaceutical investigations such as drug penetration kinetics within the different tissue layers. This review provides a comprehensive introduction about the basic principles of Raman microscopy highlighting the advantages and considering the limitations of the technique for skin applications. Subsequently, an overview about skin research studies applying Raman spectroscopy is given comprising various in vitro as well as in vivo implementations. Furthermore, the future perspective and potential of Raman microscopy in the field of skin research are discussed.

In vivo study for the discrimination of cancerous and normal skin using fibre probe-based Raman spectroscopy

Raman spectroscopy has proved its capability as an objective, non-invasive tool for the detection of various melanoma and non-melanoma skin cancers (NMSC) in a number of studies. Most publications are based on a Raman microspectroscopic ex vivo approach. In this in vivo clinical evaluation, we apply Raman spectroscopy using a fibre-coupled probe that allows access to a multitude of affected body sites. The probe design is optimized for epithelial sensitivity, whereby a large part of the detected signal originates from within the epidermal layer's depth down to the basal membrane where early stages of skin cancer develop. Data analysis was performed on measurements of 104 subjects scheduled for excision of lesions suspected of being malignant melanoma (MM) (n = 36), basal cell carcinoma (BCC) (n = 39) and squamous cell carcinoma (SCC) (n = 29). NMSC were discriminated from normal skin with a balanced accuracy of 73% (BCC) and 85% (SCC) using partial least squares discriminant analysis (PLS-DA). Discriminating MM and pigmented nevi (PN) resulted in a balanced accuracy of 91%. These results lie within the range of comparable in vivo studies and the accuracies achieved by trained dermatologists using dermoscopy. Discrimination proved to be unsuccessful between cancerous lesions and suspicious lesions that had been histopathologically verified as benign by dermoscopy.

Emerging technology: applications of Raman spectroscopy for prostate cancer

There is a need in prostate cancer diagnostics and research for a label-free imaging methodology that is nondestructive, rapid, objective, and uninfluenced by water. Raman spectroscopy provides a molecular signature, which can be scaled from micron-level regions of interest in cells to macroscopic areas of tissue. It can be used for applications ranging from in vivo or in vitro diagnostics to basic science laboratory testing. This work describes the fundamentals of Raman spectroscopy and complementary techniques including surface enhanced Raman scattering, resonance Raman spectroscopy, coherent anti-Stokes Raman spectroscopy, confocal Raman spectroscopy, stimulated Raman scattering, and spatially offset Raman spectroscopy. Clinical applications of Raman spectroscopy to prostate cancer will be discussed, including screening, biopsy, margin assessment, and monitoring of treatment efficacy. Laboratory applications including cell identification, culture monitoring, therapeutics development, and live imaging of cellular processes are discussed. Potential future avenues of research are described, with emphasis on multiplexing Raman spectroscopy with other modalities.

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..

Raman spectroscopic study of radioresistant oral cancer sublines established by fractionated ionizing radiation

Radiotherapy is an important treatment modality for oral cancer. However, development of radioresistance is a major hurdle in the efficacy of radiotherapy in oral cancer patients. Identifying predictors of radioresistance is a challenging task and has met with little success. The aim of the present study was to explore the differential spectral profiles of the established radioresistant sublines and parental oral cancer cell lines by Raman spectroscopy. We have established radioresistant sublines namely, 50Gy-UPCI:SCC029B and 70Gy-UPCI:SCC029B from its parental UPCI:SCC029B cell line, by using clinically admissible 2Gy fractionated ionizing radiation (FIR). The developed radioresistant character was validated by clonogenic cell survival assay and known radioresistance-related protein markers like Mcl-1, Bcl-2, Cox-2 and Survivin. Altered cellular morphology with significant increase (p<0.001) in the number of filopodia in radioresistant cells with respect to parental cells was observed. The Raman spectra of parental UPCI:SCC029B, 50Gy-UPCI:SCC029B and 70Gy-UPCI:SCC029B cells were acquired and spectral features indicate possible differences in biomolecules like proteins, lipids and nucleic acids. Principal component analysis (PCA) provided three clusters corresponding to radioresistant 50Gy, 70Gy-UPCI:SCC029B sublines and parental UPCI:SCC029B cell line with minor overlap, which suggest altered molecular profile acquired by the radioresistant cells due to multiple doses of irradiation. The findings of this study support the potential of Raman spectroscopy in prediction of radioresistance and possibly contribute to better prognosis of oral cancer.

Raman and coherent anti-Stokes Raman scattering microscopy studies of changes in lipid content and composition in hormone-treated breast and prostate cancer cells

Increasing interest in the role of lipids in cancer cell proliferation and resistance to drug therapies has motivated the need to develop better tools for cellular lipid analysis. Quantification of lipids in cells is typically done by destructive chromatography protocols that do not provide spatial information on lipid distribution and prevent dynamic live cell studies. Methods that allow the analysis of lipid content in live cells are therefore of great importance. Using micro-Raman spectroscopy and coherent anti-Stokes Raman scattering (CARS) microscopy, we generated a lipid profile for breast (T47D, MDA-MB-231) and prostate (LNCaP, PC3) cancer cells upon exposure to medroxyprogesterone acetate (MPA) and synthetic androgen R1881. Combining Raman spectra with CARS imaging, we can study the process of hormone-mediated lipogenesis. Our results show that hormone-treated cancer cells T47D and LNCaP have an increased number and size of intracellular lipid droplets and higher degree of saturation than untreated cells. MDA-MB-231 and PC3 cancer cells showed no significant changes upon treatment. Principal component analysis with linear discriminant analysis of the Raman spectra was able to differentiate between cancer cells that were treated with MPA, R1881, and untreated.

Clinical study of noninvasive in vivo melanoma and nonmelanoma skin cancers using multimodal spectral diagnosis

The goal of this study was to determine the diagnostic capability of a multimodal spectral diagnosis (SD) for in vivo noninvasive disease diagnosis of melanoma and nonmelanoma skin cancers. We acquired reflectance, fluorescence, and Raman spectra from 137 lesions in 76 patients using custom-built optical fiber-based clinical systems. Biopsies of lesions were classified using standard histopathology as malignant melanoma (MM), nonmelanoma pigmented lesion (PL), basal cell carcinoma (BCC), actinic keratosis (AK), and squamous cell carcinoma (SCC). Spectral data were analyzed using principal component analysis. Using multiple diagnostically relevant principal components, we built leave-one-out logistic regression classifiers. Classification results were compared with histopathology of the lesion. Sensitivity/specificity for classifying MM versus PL (12 versus 17 lesions) was 100%/100%, for SCC and BCC versus AK (57 versus 14 lesions) was 95%/71%, and for AK and SCC and BCC versus normal skin (71 versus 71 lesions) was 90%/85%. The best classification for nonmelanoma skin cancers required multiple modalities; however, the best melanoma classification occurred with Raman spectroscopy alone. The high diagnostic accuracy for classifying both melanoma and nonmelanoma skin cancer lesions demonstrates the potential for SD as a clinical diagnostic device.

Serum based diagnosis of asthma using Raman spectroscopy: an early phase pilot study

The currently prescribed tests for asthma diagnosis require compulsory patient compliance, and are usually not sensitive to mild asthma. Development of an objective test using minimally invasive samples for diagnosing and monitoring of the response of asthma may help better management of the disease. Raman spectroscopy (RS) has previously shown potential in several biomedical applications, including pharmacology and forensics. In this study, we have explored the feasibility of detecting asthma and determining treatment response in asthma patients, through RS of serum. Serum samples from 44 asthma subjects of different grades (mild, moderate, treated severe and untreated severe) and from 15 reference subjects were subjected to Raman spectroscopic analysis and YKL-40 measurements. The force expiratory volume in 1 second (FEV1) values were used as gold standard and the serum YKL-40 levels were used as an additional parameter for diagnosing the different grades of asthma. For spectral acquisition, serum was placed on a calcium fluoride (CaF2) window and spectra were recorded using Raman microprobe. Mean and difference spectra comparisons indicated significant differences between asthma and reference spectra. Differences like changes in protein structure, increase in DNA specific bands and increased glycosaminoglycans-like features were more prominent with increase in asthma severity. Multivariate tools using Principal-component-analysis (PCA) and Principal-component based-linear-discriminant analysis (PC-LDA) followed by Leave-one-out-cross-validation (LOOCV), were employed for data analyses. PCA and PC-LDA results indicate separation of all asthma groups from the reference group, with minor overlap (19.4%) between reference and mild groups. No overlap was observed between the treated severe and untreated severe groups, indicating that patient response to treatment could be determined. Overall promising results were obtained, and a large scale validation study on random subjects is warranted before the routine clinical usage of this technique.

Time-lens based hyperspectral stimulated Raman scattering imaging and quantitative spectral analysis

We demonstrate a hyperspectral stimulated Raman scattering (SRS) microscope through spectral-transformed excitation. The 1064 nm Stokes pulse was from a synchronized time-lens source, generated through time-domain phase modulation of a continuous wave (CW) laser. The tunable pump pulse was from linear spectral filtering of a femtosecond laser output with an intra-pulse spectral scanning pulse shaper. By electronically modulating the time-lens source at 2.29 MHz, hyperspectral stimulated Raman loss (SRL) images were obtained on a laser-scanning microscope. Using this microscope, DMSO in aqueous solution with a concentration down to 28 mM could be detected at 2 μs time constant. Hyperspectral SRL images of prostate cancer cells were obtained. Multivariate curve resolution analysis was further applied to decompose the SRL images into concentration maps of CH₂ and CH₃ bonds. This method offers exciting potential in label-free imaging of live cells using fingerprint Raman bands.