Is Raman Spectroscopy of Fingernails a Promising Tool for Diagnosing Systemic and Dermatological Diseases in Adult and Pediatric Populations?

Background: Raman spectroscopy is a well-known tool used in criminology, molecular biology, and histology. It is also applied to diagnose bone mineral disorders by taking advantage of the similarity of the structure of keratin and bone collagen. Raman spectroscopy can also be used in dermatology and diabetology. The purpose of the present review is to critically evaluate the available research about the use of Raman spectroscopy in the mentioned areas of medicine. Methodology: PubMed was searched for peer-reviewed articles on the subject of use of Raman spectroscopy in bone mineral disorders, dermatology, and diabetes mellitus. Results: Nail keratin and bone collagen are related structural proteins that require disulfide bond for structural stability. Therefore, Raman spectroscopy of keratin may have potential as a diagnostic tool for screening bone quality and distinguishing patients at risk of fracture for reasons different from low bone mineral density (BMD) in the adult women population. Raman spectroscopy can also investigate the changes in keratin's structure in nails affected by onychomycosis and distinguish between healthy and onychomycosis nail samples. It could also reduce the need for nail biopsy by distinguishing between dermatophytic and non-dermatophytic agents of onychomycosis. Additionally, Raman spectroscopy could expedite the diagnostic process in psoriasis (by assessing the secondary structure of keratin) and in diabetes mellitus (by examining the protein glycation level). Conclusions: In adult populations, Raman spectroscopy is a promising and safe method for assessing the structure of fingernails. However, data are scarce in the pediatric population; therefore, more studies are required in children.

Improving skin cancer detection by Raman spectroscopy using convolutional neural networks and data augmentation

Background

Our previous studies have demonstrated that Raman spectroscopy could be used for skin cancer detection with good sensitivity and specificity. The objective of this study is to determine if skin cancer detection can be further improved by combining deep neural networks and Raman spectroscopy.

Patients and methods

Raman spectra of 731 skin lesions were included in this study, containing 340 cancerous and precancerous lesions (melanoma, basal cell carcinoma, squamous cell carcinoma and actinic keratosis) and 391 benign lesions (melanocytic nevus and seborrheic keratosis). One-dimensional convolutional neural networks (1D-CNN) were developed for Raman spectral classification. The stratified samples were divided randomly into training (70%), validation (10%) and test set (20%), and were repeated 56 times using parallel computing. Different data augmentation strategies were implemented for the training dataset, including added random noise, spectral shift, spectral combination and artificially synthesized Raman spectra using one-dimensional generative adversarial networks (1D-GAN). The area under the receiver operating characteristic curve (ROC AUC) was used as a measure of the diagnostic performance. Conventional machine learning approaches, including partial least squares for discriminant analysis (PLS-DA), principal component and linear discriminant analysis (PC-LDA), support vector machine (SVM), and logistic regression (LR) were evaluated for comparison with the same data splitting scheme as the 1D-CNN.

Results

The ROC AUC of the test dataset based on the original training spectra were 0.886±0.022 (1D-CNN), 0.870±0.028 (PLS-DA), 0.875±0.033 (PC-LDA), 0.864±0.027 (SVM), and 0.525±0.045 (LR), which were improved to 0.909±0.021 (1D-CNN), 0.899±0.022 (PLS-DA), 0.895±0.022 (PC-LDA), 0.901±0.020 (SVM), and 0.897±0.021 (LR) respectively after augmentation of the training dataset (p<0.0001, Wilcoxon test). Paired analyses of 1D-CNN with conventional machine learning approaches showed that 1D-CNN had a 1-3% improvement (p<0.001, Wilcoxon test).

Conclusions

Data augmentation not only improved the performance of both deep neural networks and conventional machine learning techniques by 2-4%, but also improved the performance of the models on spectra with higher noise or spectral shifting. Convolutional neural networks slightly outperformed conventional machine learning approaches for skin cancer detection by Raman spectroscopy.

Combined ultrasound and photoacoustic C-mode imaging system for skin lesion assessment

Accurate assessment of the size and depth of infiltration is critical for effectively treating and removing skin cancer, especially melanoma. However, existing methods such as skin biopsy and histologic examination are invasive, time-consuming, and may not provide accurate depth results. We present a novel system for simultaneous and co-localized ultrasound and photoacoustic imaging, with the application for non-invasive skin lesion size and depth measurement. The developed system integrates an acoustical mirror that is placed on an ultrasound transducer, which can be translated within a flexible water tank. This allows for 3D (C-mode) imaging, which is useful for mapping the skin structure and determine the invasion size and depth of lesions including skin cancer. For efficient reconstruction of photoacoustic images, we applied the open-source MUST library. The acquisition time per 2D image is <1 s and the pulse energies are below the legal Maximum Permissible Exposure (MPE) on human skin. We present the depth and resolution capabilities of the setup on several self-designed agar phantoms and demonstrate in vivo imaging on human skin. The setup also features an unobstructed optical window from the top, allowing for simple integration with other optical modalities. The perspective towards clinical application is demonstrated.

Near-Field Microwave Tomography of Biological Tissues: Future Perspectives

This overview shows the mapping of specific visualization techniques, depth assessment of the structure of the underlying tissues and used wavelengths of radiation. Medical imaging is currently one of the most dynamically developing areas of medical science. The main aim of the review is a systematization of information on the current status of the microwave imaging of biological objects, primarily of body tissues. The main options of microwave sensing of biological objects are analyzed. Two basic techniques for sensing differing evaluation parameters are characterized. They are microwave thermometry (passive) and near-field resonance imaging. The physical principles of microwave sensing application are discussed. It is shown that the resonant near-field microwave tomography allows visualization of the structure of biological tissues on the basis of the spatial distribution of their electrodynamic characteristics - permittivity and conductivity. Potential areas for this method in dermatology, including dermatooncology, are shown. The known results of applying the method to patients with dermatoses are given. The informativeness of the technology in the early diagnosis of melanoma is shown. The prospects of microwave diagnostics in combustiology, reconstructive and plastic surgery are demonstrated. Thus, microwave sensing is a modern, dynamically developing method of biophysical assessment of body tissues. There is a strong indication of the feasibility of application of microwave sensing in combustiology (in different periods of burn disease), as well as in reconstructive surgery. Further research in this and other areas of biomedicine will significantly expand the range of possibilities of modern technologies of visualization.

[The Rise of Artificial Intelligence - High Prediction Accuracy in Early Detection of Pigmented Melanoma]

The incidence of malignant melanoma is increasing worldwide. If detected early, melanoma is highly treatable, so early detection is vital.Skin cancer early detection has improved significantly in recent decades, for example by the introduction of screening in 2008 and dermoscopy. Nevertheless, in particular visual detection of early melanomas remains challenging because they show many morphological overlaps with nevi. Hence, there continues to be a high medical need to further develop methods for early skin cancer detection in order to be able to reliably diagnosemelanomas at a very early stage.Routine diagnostics for melanoma detection include visual whole body inspection, often supplemented by dermoscopy, which can significantly increase the diagnostic accuracy of experienced dermatologists. A procedure that is additionally offered in some practices and clinics is wholebody photography combined with digital dermoscopy for the early detection of malignant melanoma, especially for monitoring high-risk patients.In recent decades, numerous noninvasive adjunctive diagnostic techniques were developed for the examination of suspicious pigmented moles, that may have the potential to allow improved and, in some cases, automated evaluation of these lesions. First, confocal laser microscopy should be mentioned here, as well as electrical impedance spectroscopy, multiphoton laser tomography, multispectral analysis, Raman spectroscopy or optical coherence tomography. These diagnostic techniques usually focus on high sensitivity to avoid malignant melanoma being overlooked. However, this usually implies lower specificity, which may lead to unnecessary excision of benign lesions in screening. Also, some of the procedures are time-consuming and costly, which also limits their applicability in skin cancer screening. In the near future, the use of artificial intelligence might change skin cancer diagnostics in many ways. The most promising approach may be the analysis of routine macroscopic and dermoscopic images by artificial intelligence.For the classification of pigmented skin lesions based on macroscopic and dermoscopic images, artificial intelligence, especially in form of neural networks, has achieved comparable diagnostic accuracies to dermatologists under experimental conditions in numerous studies. In particular, it achieved high accuracies in the binary melanoma/nevus classification task, but it also performed comparably well to dermatologists in multiclass differentiation of various skin diseases. However, proof of the basic applicability and utility of such systems in clinical practice is still pending. Prerequisites that remain to be established to enable translation of such diagnostic systems into dermatological routine are means that allow users to comprehend the system's decisions as well as a uniformly high performance of the algorithms on image data from other hospitals and practices.At present, hints are accumulating that computer-aided diagnosis systems could provide their greatest benefit as assistance systems, since studies indicate that a combination of human and machine achieves the best results. Diagnostic systems based on artificial intelligence are capable of detecting morphological characteristics quickly, quantitatively, objectively and reproducibly, and could thus provide a more objective analytical basis - in addition to medical experience.

Multimodal system for optical biopsy of melanoma with integrated ultrasound, optical coherence tomography and Raman spectroscopy

We introduce a new single-head multimodal optical system that integrates optical coherence tomography (OCT), 18 MHz ultrasound (US) tomography and Raman spectroscopy (RS), allowing for fast (<2 min) and noninvasive skin cancer diagnostics and lesion depth measurement. The OCT can deliver structural and depth information of smaller skin lesions (<1 mm), while the US allows to measure the penetration depth of thicker lesions (≥4 mm), and the RS analyzes the chemical composition from a small chosen spot (≤300 μm) that can be used to distinguish between benign and malignant melanoma. The RS and OCT utilize the same scanning and optical setup, allowing for co-localized measurements. The US on the other side is integrated with an acoustical reflector, which enables B-mode measurements on the same position as OCT and RS. The US B-mode scans can be translated across the sample by laterally moving the US transducer, which is made possible by the developed adapter with a flexible membrane. We present the results on custom-made liquid and agar phantoms that show the resolution and depth capabilities of the setup, as well as preliminary ex vivo measurements on mouse models with ∼4.3 mm thick melanoma.

Künstliche Intelligenz auf dem Vormarsch – Hohe Vorhersage-Genauigkeit bei der Früherkennung pigmentierter Melanome

Weltweit steigt die Inzidenz des malignen Melanoms an. Bei frühzeitiger Erkennung ist das Melanom gut behandelbar, eine Früherkennung ist also lebenswichtig.

Die Hautkrebs-Früherkennung hat sich in den letzten Jahrzehnten bspw. durch die Einführung des Screenings im Jahr 2008 und die Dermatoskopie deutlich verbessert. Dennoch bleibt die visuelle Erkennung insbesondere von frühen Melanomen eine Herausforderung, weil diese viele morphologische Überlappungen mit Nävi zeigen. Daher ist der medizinische Bedarf weiterhin hoch, die Methoden zur Hautkrebsfrüherkennung gezielt weiterzuentwickeln, um Melanome bereits in einem sehr frühen Stadium sicher diagnostizieren zu können.

Die Routinediagnostik zur Hautkrebs-Früherkennung umfasst die visuelle Ganzkörperinspektion, oft ergänzt durch die Dermatoskopie, durch die sich die diagnostische Treffsicherheit erfahrener Hautärzte deutlich erhöhen lässt. Ein Verfahren, was in einigen Praxen und Kliniken zusätzlich angeboten wird, ist die kombinierte Ganzkörperfotografie mit der digitalen Dermatoskopie für die Früherkennung maligner Melanome, insbesondere für das Monitoring von Hochrisiko-Patienten.

In den letzten Jahrzenten wurden zahlreiche nicht invasive zusatzdiagnostische Verfahren zur Beurteilung verdächtiger Pigmentmale entwickelt, die das Potenzial haben könnten, eine verbesserte und z. T. automatisierte Bewertung dieser Läsionen zu ermöglichen. In erster Linie ist hier die konfokale Lasermikroskopie zu nennen, ebenso die elektrische Impedanzspektroskopie, die Multiphotonen-Lasertomografie, die Multispektralanalyse, die Raman-Spektroskopie oder die optische Kohärenztomografie. Diese diagnostischen Verfahren fokussieren i. d. R. auf hohe Sensitivität, um zu vermeiden, ein malignes Melanom zu übersehen. Dies bedingt allerdings üblicherweise eine geringere Spezifität, was im Screening zu unnötigen Exzisionen vieler gutartiger Läsionen führen kann. Auch sind einige der Verfahren zeitaufwendig und kostenintensiv, was die Anwendbarkeit im Screening ebenfalls einschränkt.

In naher Zukunft wird insbesondere die Nutzung von künstlicher Intelligenz die Diagnosefindung in vielfältiger Weise verändern. Vielversprechend ist v. a. die Analyse der makroskopischen und dermatoskopischen Routine-Bilder durch künstliche Intelligenz. Für die Klassifizierung von pigmentierten Hautläsionen anhand makroskopischer und dermatoskopischer Bilder erzielte die künstliche Intelligenz v. a. in Form neuronaler Netze unter experimentellen Bedingungen in zahlreichen Studien bereits eine vergleichbare diagnostische Genauigkeit wie Dermatologen. Insbesondere bei der binären Klassifikationsaufgabe Melanom/Nävus erreichte sie hohe Genauigkeiten, doch auch in der Multiklassen-Differenzierung von verschiedenen Hauterkrankungen zeigt sie sich vergleichbar gut wie Dermatologen. Der Nachweis der grundsätzlichen Anwendbarkeit und des Nutzens solcher Systeme in der klinischen Praxis steht jedoch noch aus. Noch zu schaffende Grundvoraussetzungen für die Translation solcher Diagnosesysteme in die dermatologischen Routine sind Möglichkeiten für die Nutzer, die Entscheidungen des Systems nachzuvollziehen, sowie eine gleichbleibend gute Leistung der Algorithmen auf Bilddaten aus fremden Kliniken und Praxen.

Derzeit zeichnet sich ab, dass computergestützte Diagnosesysteme als Assistenzsysteme den größten Nutzen bringen könnten, denn Studien deuten darauf hin, dass eine Kombination von Mensch und Maschine die besten Ergebnisse erzielt. Diagnosesysteme basierend auf künstlicher Intelligenz sind in der Lage, Merkmale schnell, quantitativ, objektiv und reproduzierbar zu erfassen, und könnten somit die Medizin auf eine mathematische Grundlage stellen – zusätzlich zur ärztlichen Erfahrung.

Novel Non-Invasive Quantification and Imaging of Eumelanin and DHICA Subunit in Skin Lesions by Raman Spectroscopy and MCR Algorithm: Improving Dysplastic Nevi Diagnosis

Malignant melanoma (MM) is the most aggressive form of skin cancer, and around 30% of them may develop from pre-existing dysplastic nevi (DN). Diagnosis of DN is a relevant clinical challenge, as these are intermediate lesions between benign and malignant tumors, and, up to date, few studies have focused on their diagnosis. In this study, the accuracy of Raman spectroscopy (RS) is assessed, together with multivariate analysis (MA), to classify 44 biopsies of MM, DN and compound nevus (CN) tumors. For this, we implement a novel methodology to non-invasively quantify and localize the eumelanin pigment, considered as a tumoral biomarker, by means of RS imaging coupled with the Multivariate Curve Resolution-Alternative Least Squares (MCR-ALS) algorithm. This represents a step forward with respect to the currently established technique for melanin analysis, High-Performance Liquid Chromatography (HPLC), which is invasive and cannot provide information about the spatial distribution of molecules. For the first time, we show that the 5, 6-dihydroxyindole (DHI) to 5,6-dihydroxyindole-2-carboxylic acid (DHICA) ratio is higher in DN than in MM and CN lesions. These differences in chemical composition are used by the Partial Least Squares-Discriminant Analysis (PLS-DA) algorithm to identify DN lesions in an efficient, non-invasive, fast, objective and cost-effective method, with sensitivity and specificity of 100% and 94.1%, respectively.

Assessment of Skin Deep Layer Biochemical Profile Using Spatially Offset Raman Spectroscopy

Skin cancer is currently the most common type of cancer with millions of cases diagnosed worldwide yearly. The current gold standard for clinical diagnosis of skin cancer is an invasive and relatively time-consuming procedure, consisting of visual examination followed by biopsy collection and histopathological analysis. Raman spectroscopy has been shown to efficiently aid the non-invasive diagnosis of skin cancer when probing the surface of the skin. In this study, we employ a recent development of Raman spectroscopy (Spatially Offset Raman Spectroscopy, SORS) which is able to look deeper in tissue and create a deep layer biochemical profile of the skin in areas where cancer lesions subtly evolve. After optimizing the measurement parameters on skin tissue phantoms, we then adopted SORS on human skin tissue from different anatomical areas to investigate the contribution of the different skin layers to the recorded Raman signal. Our results show that using a diffuse beam with zero offset to probe a sampling volume where the lesion is typically included (surface to epidermis-dermis junction), provides the optimum signal-to-noise ratio (SNR) and may be employed in future skin cancer screening applications.

Probing glycosaminoglycan spectral signatures in live cells and their conditioned media by Raman microspectroscopy

Spectroscopic markers characteristic of reference glycosaminoglycan molecules were identified previously based on their vibrational signatures. Infrared spectral signatures of glycosaminoglycans in fixed cells were also recently demonstrated but probing live cells still remains challenging. Raman microspectroscopy is potentially interesting to perform studies under physiological conditions. The aim of the present work was to identify the Raman spectral signatures of GAGs in fixed and live cells and in their conditioned media. Biochemical and Raman analyses were performed on five cell types: chondrocytes, dermal fibroblasts, melanoma (SK-MEL-28), wild type CHO, and glycosaminoglycan-defective mutant CHO-745 cells. The biochemical assay of sulfated GAGs in conditioned media was only possible for chondrocytes, dermal fibroblasts, and wild type CHO due to the detection limit of the test. In contrast, Raman microspectroscopy allowed probing total glycosaminoglycan content in conditioned media, fixed and live cells and the data were analysed by principal component analysis. Our results showed that the Raman technique is sensitive enough to identify spectral markers of glycosaminoglycans that were useful to characterise the conditioned media of the five cell types. The results were confirmed at the single cell level on both live and fixed cells with a good differentiation between the cell types. Furthermore, the principal component loadings revealed prominent glycosaminoglycan-related spectral information. Raman microspectroscopy allows monitoring of the glycosaminoglycan profiles of single live cells and could therefore be developed for cell screening purposes and holds promise for identifying glycosaminoglycan signatures as a marker of cancer progression in tissues.

Raman characterizations of red blood cells with β-thalassemia using laser tweezers Raman spectroscopy

This study aimed to study the differences in Raman spectra of red blood cells (RBCs) among patients with β-thalassemia and controls using laser tweezers Raman spectroscopy (LTRS) system.A total of 33 patients with β-thalassemia major, 49 with β-thalassemia minor, and 65 controls were studied. Raman spectra of RBCs for each sample were recorded. Principal component analysis (PCA), one-way analysis of variance (ANOVA), and independent-sample t test were performed.The intensities of Raman spectra of β-thalassemia (major and minor) RBCs were lower than those of controls, especially at bands 1546, 1603, and 1619 cm. The intensity ratio of band 1546 cm to band 1448 cm demonstrated that there was a significant difference between the spectra of β-thalassemia major (mostly below 2.15) and those of controls. The spectra of controls could be well distinguished from those of β-thalassemia major using PCA. After normalization, the spectra of two different genotypes with β/β mutations mainly overlapped, while those with β/β mutations had lower intensity at bands 1546, 1603, and 1619 cm.The present study provided Raman characteristics of RBCs in patients with β-thalassemia major and supported the use of LTRS as a method for screening β-thalassemia major. The recognition rate for β-thalassemia minor needs to be further improved.

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.

Trimodal system for in vivo skin cancer screening with combined optical coherence tomography-Raman and colocalized optoacoustic measurements

A new multimodal system for rapid, noninvasive in vivo skin cancer screening is presented, combining optical coherence tomography (OCT) and optoacoustic (OA) modalities to provide precise tumor depth determination with a Raman spectroscopic modality capable of detecting the lesion type and, thus, providing diagnostic capability. Both OA and Raman setups use wide field skin illumination to ensure the compliance with maximum permissible exposure (MPE) requirements. The Raman signal is collected via the OCT scanning lens to maximize the signal-to-noise ratio of the measured signal while keeping radiation levels below MPE limits. OCT is used to optically determine the tumor thickness and for volumetric imaging whereas OA utilizes acoustic signals generated by optical absorption contrast for thickness determination at potentially higher penetration depths compared to OCT. Preliminary results of first clinical trials using our setup are presented. The measured lesion depth is in good agreement with histology results, while Raman measurements show distinctive differences between normal skin and melanocytic lesions, and, moreover, between different skin areas. In future, we will validate the setup presented for reliable detection of pathophysiological parameters, morphology and thickness of suspicious skin lesions.

Real-time In vivo Diagnosis of Nasopharyngeal Carcinoma Using Rapid Fiber-Optic Raman Spectroscopy

We report the utility of a simultaneous fingerprint (FP) (i.e., 800-1800 cm^-1) and high-wavenumber (HW) (i.e., 2800-3600 cm^-1) fiber-optic Raman spectroscopy developed for real-time in vivo diagnosis of nasopharyngeal carcinoma (NPC) at endoscopy. A total of 3731 high-quality in vivo FP/HW Raman spectra (normal=1765; cancer=1966) were acquired in real-time from 204 tissue sites (normal=95; cancer=109) of 95 subjects (normal=57; cancer=38) undergoing endoscopic examination. FP/HW Raman spectra differ significantly between normal and cancerous nasopharyngeal tissues that could be attributed to changes of proteins, lipids, nucleic acids, and the bound water content in NPC. Principal components analysis (PCA) and linear discriminant analysis (LDA) together with leave-one subject-out, cross-validation (LOO-CV) were implemented to develop robust Raman diagnostic models. The simultaneous FP/HW Raman spectroscopy technique together with PCA-LDA and LOO-CV modeling provides a diagnostic accuracy of 93.1% (sensitivity of 93.6%; specificity of 92.6%) for nasopharyngeal cancer identification, which is superior to using either FP (accuracy of 89.2%; sensitivity of 89.9%; specificity of 88.4%) or HW (accuracy of 89.7%; sensitivity of 89.0%; specificity of 90.5%) Raman technique alone. Further receiver operating characteristic (ROC) analysis reconfirms the best performance of the simultaneous FP/HW Raman technique for in vivo diagnosis of NPC. This work demonstrates for the first time that simultaneous FP/HW fiber-optic Raman spectroscopy technique has great promise for enhancing real-time in vivo cancer diagnosis in the nasopharynx during endoscopic examination.

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.

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.

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.

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.

Advances in the in Vivo Raman Spectroscopy of Malignant Skin Tumors Using Portable Instrumentation

Raman spectroscopy has emerged as a promising tool for real-time clinical diagnosis of malignant skin tumors offering a number of potential advantages: it is non-intrusive, it requires no sample preparation, and it features high chemical specificity with minimal water interference. However, in vivo tissue evaluation and accurate histopathological classification remain a challenging task for the successful transition from laboratory prototypes to clinical devices. In the literature, there are numerous reports on the applications of Raman spectroscopy to biomedical research and cancer diagnostics. Nevertheless, cases where real-time, portable instrumentations have been employed for the in vivo evaluation of skin lesions are scarce, despite their advantages in use as medical devices in the clinical setting. This paper reviews the advances in real-time Raman spectroscopy for the in vivo characterization of common skin lesions. The translational momentum of Raman spectroscopy towards the clinical practice is revealed by (i) assembling the technical specifications of portable systems and (ii) analyzing the spectral characteristics of in vivo measurements.

Advantages and limitations of Raman spectroscopy for molecular diagnostics: an update

Over the last decade, Raman spectroscopy has gained more and more interest in research as well as in clinical laboratories. As a vibrational spectroscopy technique, it is complementary to the also well-established infrared spectroscopy. Through specific spectral patterns, substances can be identified and molecular changes can be observed with high specificity. Because of a high spatial resolution due to an excitation wavelength in the visible and near-infrared range, Raman spectroscopy combined with microscopy is very powerful for imaging biological samples. Individual cells can be imaged on the subcellular level. In vivo tissue examinations are becoming increasingly important for clinical applications. In this review, we present currently ongoing research in different fields of medical diagnostics involving linear Raman spectroscopy and imaging. We give a wide overview over applications for the detection of atherosclerosis, cancer, inflammatory diseases and pharmacology, with a focus on developments over the past 5 years. Conclusions drawn from Raman spectroscopy are often validated by standard methods, for example, histopathology or PCR. The future potential of Raman spectroscopy and its limitations are discussed in consideration of other non-linear Raman techniques.

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

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

Raman spectroscopy for the discrimination of cancerous and normal skin

Various studies have shown promising results in using Raman spectroscopy (RS) for the detection of skin cancers.

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.

1064 nm dispersive Raman spectroscopy of tissues with strong near-infrared autofluorescence

Raman spectroscopy is an established technique for molecularly specific characterization of tissues. However, even with near-infrared (NIR) excitation, some tissues possess background autofluorescence, which can overwhelm Raman scattering. Here, we report collection of spectra from tissues with strong autofluorescence using a 1064 nm system with a high-throughput dispersive spectrometer and deep-cooled InGaAs array. Spectra collected at 1064 nm were compared with those collected at 785 nm in specimens from human breast, liver, and kidney. The results demonstrate superior performance at 1064 nm in the liver and kidney, where NIR autofluorescence is intense. The results indicate the feasibility of new biomedical applications for Raman spectroscopy at 1064 nm in tissues with strong autofluorescence.

Raman spectroscopy: in vivo quick response code of skin physiological status

Dermatologists need to combine different clinically relevant characteristics for a better understanding of skin health. These characteristics are usually measured by different techniques, and some of them are highly time consuming. Therefore, a predicting model based on Raman spectroscopy and partial least square (PLS) regression was developed as a rapid multiparametric method. The Raman spectra collected from the five uppermost micrometers of 11 healthy volunteers were fitted to different skin characteristics measured by independent appropriate methods (transepidermal water loss, hydration, pH, relative amount of ceramides, fatty acids, and cholesterol). For each parameter, the obtained PLS model presented correlation coefficients higher than R2=0.9. This model enables us to obtain all the aforementioned parameters directly from the unique Raman signature. In addition to that, in-depth Raman analyses down to 20 μm showed different balances between partially bound water and unbound water with depth. In parallel, the increase of depth was followed by an unfolding process of the proteins. The combinations of all these information led to a multiparametric investigation, which better characterizes the skin status. Raman signal can thus be used as a quick response code (QR code). This could help dermatologic diagnosis of physiological variations and presents a possible extension to pathological characterization.