Raman biophysical markers in skin cancer diagnosis

Multi-scale representation of surface-enhanced Raman spectroscopy data for deep learning-based liver cancer detection

The early detection of liver cancer greatly improves survival rates and allows for less invasive treatment options. As a non-invasive optical detection technique, Surface-Enhanced Raman Spectroscopy (SERS) has shown significant potential in early cancer detection, providing multiple advantages over conventional methods. The majority of existing cancer detection methods utilize multivariate statistical analysis to categorize SERS data. However, these methods are plagued by issues such as information loss during dimensionality reduction and inadequate ability to handle nonlinear relationships within the data. To overcome these problems, we first use wavelet transform with its multi-scale analysis capability to extract multi-scale features from SERS data while minimizing information loss compared to traditional methods. Moreover, deep learning is employed for classification, leveraging its strong nonlinear processing capability to enhance accuracy. In addition, the chosen neural network incorporates a data augmentation method, thereby enriching our training dataset and mitigating the risk of overfitting. Moreover, we acknowledge the significance of selecting the appropriate wavelet basis functions in SERS data processing, prompting us to choose six specific ones for comparison. We employ SERS data from serum samples obtained from both liver cancer patients and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding performance, surpassing the majority of multivariate statistical analysis and traditional machine learning classification methods, with an accuracy of 99.38 %, a sensitivity of 99.8 %, and a specificity of 97.0 %. These results indicate that the combination of SERS, wavelet transform, and deep learning has the potential to function as a non-invasive tool for the rapid detection of liver cancer.

Noninvasive Glucose Measurements Through Transcutaneous Raman Spectroscopy: A Review

BACKGROUND

People living with diabetes need constant glucose monitoring to avoid health complications. However, they do not monitor their glucose levels as often as recommended, probably because glucose measurement devices can be painful, costly, need testing strips or sensors, require lancing the finger or inserting a sensor with risk of infection, and can be inaccurate or have failures. Therefore, developing new alternatives for noninvasive glucose measurements that overcome these disadvantages is necessary, being Raman spectroscopy (RS) a solution.

OBJECTIVE

This review aims to provide an overview of the current glucose-monitoring technologies and the uses and advantages of RS to improve noninvasive transcutaneously glucose-monitoring devices.

RESULTS

The skin has been used to assess glucose levels noninvasively because it is an accessible tissue where glucose can be measured in the interstitial fluid (ISF) in the epidermis (especially in the stratum corneum). The most selected skin sites to apply RS for noninvasive glucose measurements were the nailfold, finger, and forearm because, in these sites, the penetration depth of the excitation light can reach the stratum corneum (10-20 µm) and the ISF. Studies found that RS is a good optical technique to measure glucose noninvasively by comparing glucose levels obtained by RS with those from invasive methods such as glucose meters with testing strips during an oral glucose tolerance test (OGTT).

CONCLUSIONS

New alternatives for noninvasive glucose measurements that overcome the disadvantages of current devices is necessary, and RS is a possible solution. However, more research is needed to evaluate the stability, accuracy, costs, and acceptance.

Non-invasive 3D imaging of human melanocytic lesions by combined ultrasound and photoacoustic tomography: a pilot study

The accurate determination of the size and depth of infiltration is critical to the treatment and excision of melanoma and other skin cancers. However, current techniques, such as skin biopsy and histological examination, pose invasiveness, time-consumption, and have limitations in measuring at the deepest level. Non-invasive imaging techniques like dermoscopy and confocal microscopy also present limitations in accurately capturing contrast and depth information for various skin types and lesion locations. Thus, there is a pressing need for non-invasive devices capable of obtaining high-resolution 3D images of skin lesions. In this study, we introduce a novel device that combines 18 MHz ultrasound and photoacoustic tomography into a single unit, enabling the acquisition of colocalized 3D images of skin lesions. We performed in vivo measurements on 25 suspicious human skin nevi that were promptly excised following measurements. The combined ultrasound/photoacoustic tomography imaging technique exhibited a strong correlation with histological Breslow thickness between 0.2 and 3 mm, achieving a coefficient of determination (R[Formula: see text]) of 0.93, which is superior to the coefficients from the individual modalities. The results procured in our study underscore the potential of combined ultrasound and photoacoustic tomography as a promising non-invasive 3D imaging approach for evaluating human nevi and other skin lesions. Furthermore, the system allows for integration of other optical modalities such as optical coherence tomography, microscopy, or Raman spectroscopy in future applications.

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.

Molecular Fingerprint Detection Using Raman and Infrared Spectroscopy Technologies for Cancer Detection: A Progress Review

Molecular vibrations play a crucial role in physical chemistry and biochemistry, and Raman and infrared spectroscopy are the two most used techniques for vibrational spectroscopy. These techniques provide unique fingerprints of the molecules in a sample, which can be used to identify the chemical bonds, functional groups, and structures of the molecules. In this review article, recent research and development activities for molecular fingerprint detection using Raman and infrared spectroscopy are discussed, with a focus on identifying specific biomolecules and studying the chemical composition of biological samples for cancer diagnosis applications. The working principle and instrumentation of each technique are also discussed for a better understanding of the analytical versatility of vibrational spectroscopy. Raman spectroscopy is an invaluable tool for studying molecules and their interactions, and its use is likely to continue to grow in the future. Research has demonstrated that Raman spectroscopy is capable of accurately diagnosing various types of cancer, making it a valuable alternative to traditional diagnostic methods such as endoscopy. Infrared spectroscopy can provide complementary information to Raman spectroscopy and detect a wide range of biomolecules at low concentrations, even in complex biological samples. The article concludes with a comparison of the techniques and insights into future directions.

Surface-enhanced Raman scattering (SERS) by gold nanoparticle characterizes dermal thickening by collagen in bleomycin-treated skin ex vivo

PURPOSE

Current skin imaging modalities, including optical, electron, and confocal microscopy, mostly require tissue fixations that could damage proteins and biological molecules. Live tissue or cell imaging such as ultrasonography and optical coherent microscope may not adequately measure the dynamic spectroscopical changes. Raman spectroscopy has been adopted for skin imaging in vivo, mostly for skin cancer imaging. However, whether the epidermal and dermal thickening in skin could be measured and distinguished by conventional Ramen spectroscopy or the surface-enhanced Raman scattering (SERS), a rapid and label-free method for noninvasive measurement remains unknown.

METHODS

Human skin sections from patients of atopic dermatitis and keloid, which represent epidermal and dermal thickening, respectively, were measured by conventional Ramen spectroscopy. In mice, skin sections from imiquimod (IMQ)- and bleomycin (BLE)-treated mice, which reflect the epidermal and dermal thickening, respectively, were measured by SERS, that incorporates gold nanoparticles to generate surface plasma and enhance Raman signals.

RESULTS

Conventional Ramen spectroscopy failed to consistently show the Raman shift in human samples among the different groups. SERS successfully revealed a prominent peak around 1300 cm-1 in the IMQ-treated skin; and two significant peaks around 1100 and 1300 cm-1 in BLE-treated group. Further quantitative analysis showed 1100 cm-1 peak was significantly accentuated in the BLE-treated skin than that in control skin. SERS identified in vitro a similar 1100 cm-1 peak in solutions of collagen, the major dermal biological molecules.

CONCLUSION

SERS distinguishes the epidermal or dermal thickening in mouse skin with rapid and label-free measures. A prominent 1100 cm-1 SERS peak in the BLE-treated skin may result from collagen. SERS might help precision diagnosis in the future.

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.

Deep learning on reflectance confocal microscopy improves Raman spectral diagnosis of basal cell carcinoma

SIGNIFICANCE

Raman spectroscopy (RS) provides an automated approach for assisting Mohs micrographic surgery for skin cancer diagnosis; however, the specificity of RS is limited by the high spectral similarity between tumors and normal tissues structures. Reflectance confocal microscopy (RCM) provides morphological and cytological details by which many features of epidermis and hair follicles can be readily identified. Combining RS with deep-learning-aided RCM has the potential to improve the diagnostic accuracy of RS in an automated fashion, without requiring additional input from the clinician.

AIM

The aim of this study is to improve the specificity of RS for detecting basal cell carcinoma (BCC) using an artificial neural network trained on RCM images to identify false positive normal skin structures (hair follicles and epidermis).

APPROACH

Our approach was to build a two-step classification model. In the first step, a Raman biophysical model that was used in prior work classified BCC tumors from normal tissue structures with high sensitivity. In the second step, 191 RCM images were collected from the same site as the Raman data and served as inputs for two ResNet50 networks. The networks selected the hair structure and epidermis images, respectively, within all images corresponding to the positive predictions of the Raman biophysical model with high specificity. The specificity of the BCC biophysical model was improved by moving the Raman spectra corresponding to these selected images from false positive to true negative.

RESULTS

Deep-learning trained on RCM images removed 52% of false positive predictions from the Raman biophysical model result while maintaining a sensitivity of 100%. The specificity was improved from 84.2% using Raman spectra alone to 92.4% by integrating Raman spectra with RCM images.

CONCLUSIONS

Combining RS with deep-learning-aided RCM imaging is a promising tool for guiding tumor resection surgery.

Preliminary study for the application of Raman spectroscopy for the identification of Leishmania infected dogs

Raman spectroscopy is a rapid qualitative and quantitative technique that allows the simultaneous determination of several components in biological fluids. This methodology concerns an alternative technique to distinguish between non-healthy and healthy subjects. Leishmaniasis is a zoonosis of world interest, the most important agent is L. infantum. Dogs are the principal reservoirs affected by a broad spectrum of clinical features. During a clinical exam, blood samples were collected in tubes without anticoagulants, from twenty two dogs. One aliquot was used for serological test for Leishmaniasis, one aliquot was subjected to the Raman spectroscopic analysis. Animals were divided into two groups of equal subjects, Leishmania group (LG) constituted by infected dogs, and control group (CG) constituted by healthy dogs. The acquired spectra were different in the region 1200-1370 cm^-1, in which it is possible to distinguish the amide III vibration (~ 1300 cm^-1). In LG, an evident shift to the shortwave region is observed in spectral frequencies of the band centered at ~ 1250 cm^-1. Our results distinguished between LD group and CG. Further studies are necessary to exclude the effect of metabolic modification due to disease on the recorded spectra changes and to consolidate the achievability of Raman spectroscopy as rapid and less expensive diagnosis of Leishmaniasis.

Matrix Effectors in the Pathogenesis of Keratinocyte-Derived Carcinomas

Basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC), referred to as keratinocyte carcinomas, are skin cancer with the highest incidence. BCCs, rarely metastasize; whereas, though generally not characterized by high lethality, approximately 2–4% of primary cSCCs metastasize with patients exhibiting poor prognosis. The extracellular matrix (ECM) serves as a scaffold that provides structural and biological support to cells in all human tissues. The main components of the ECM, including fibrillar proteins, proteoglycans (PGs), glycosaminoglycans (GAGs), and adhesion proteins such as fibronectin, are secreted by the cells in a tissue-specific manner, critical for the proper function of each organ. The skin compartmentalization to the epidermis and dermis compartments is based on a basement membrane (BM), a highly specialized network of ECM proteins that separate and unify the two compartments. The stiffness and assembly of BM and tensile forces affect tumor progenitors' invasion at the stratified epithelium's stromal border. Likewise, the mechanical properties of the stroma, e.g., stiffness, are directly correlated to the pathogenesis of the keratinocyte carcinomas. Since the ECM is a pool for various growth factors, cytokines, and chemokines, its' intense remodeling in the aberrant cancer tissue milieu affects biological functions, such as angiogenesis, adhesion, proliferation, or cell motility by regulating specific signaling pathways. This review discusses the structural and functional modulations of the keratinocyte carcinoma microenvironment. Furthermore, we debate how ECM remodeling affects the pathogenesis of these skin cancers.

High-Precision Intelligent Cancer Diagnosis Method: 2D Raman Figures Combined with Deep Learning

Raman spectroscopy, as a label-free detection technology, has been widely used in tumor diagnosis. However, most tumor diagnosis procedures utilize multivariate statistical analysis methods for classification, which poses a major bottleneck toward achieving high accuracy. Here, we propose a concept called the two-dimensional (2D) Raman figure combined with convolutional neural network (CNN) to improve the accuracy. Two-dimensional Raman figures can be obtained from four transformation methods: spectral recurrence plot (SRP), spectral Gramian angular field (SGAF), spectral short-time Fourier transform (SSTFT), and spectral Markov transition field (SMTF). Two-dimensional CNN models all yield more than 95% accuracy, which is higher than the PCA-LDA method and the Raman-spectrum-CNN method, indicating that 2D Raman figure inputs combined with CNN may be one reason for gaining excellent performances. Among 2D-CNN models, the main difference is the conversion, where SRP is based on the structure of wavenumber series with the best performances (98.9% accuracy, 99.5% sensitivity, 98.3% specificity), followed by SGAF on the wavenumber series, SSTFT on wavenumber and intensity information, and SMTF on wavenumber position information. The inclusion of external information in the conversion may be another reason for improvement in the accuracy. The excellent capability shows huge potential for tumor diagnosis via 2D Raman figures and may be applied in other spectroscopy analytical fields.

Optimization of measurement mode and sample processing for FTIR microspectroscopy in skin cancer research

The use of Fourier Transform Infrared (FTIR) microspectroscopy to study cancerous cells and tissues has gained popularity due to its ability to provide spatially resolved information at the molecular level. Transmission and transflection are the commonly used measurement modes for FTIR microspectroscopy, and the tissue samples measured in these modes are often paraffinized or deparaffinized. Previous studies have shown that variability in the spectra acquired using different measurement modes and sample processing methods affect the result of the analysis. However, there is no protocol that standardizes the mode of measurement and sample processing method to achieve the best classification result. This study compares the spectra of primary (IPC-298) and metastatic (SK-MEL-30) melanoma cell lines acquired in both transmission and transflection modes using paraffinized and deparaffinized samples to determine the optimal combination for accurate classification. Significant differences were observed in the spectra of the same cell line measured in different modes and with or without deparaffinization. The PLS-DA model built for the classification of two cell lines showed high accuracy in each case, suggesting that both modes and sample processing alternatives are suitable for differentiating cultured cell samples using supervised multivariate analysis. The biochemical information contained in the cells capable of discriminating two melanoma cell lines is present regardless of mode or sample type used. However, the paraffinized samples measured in transflection mode provided the best classification.

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.

Optical coherence tomography-guided confocal Raman microspectroscopy for rapid measurements in tissues

We report a joint system with both confocal Raman spectroscopy (CRS) and optical coherence tomography (OCT) modules capable of quickly addressing the region of interest in a tissue for targeted Raman measurements from OCT. By using an electrically tunable lens in the Raman module, the focus of the module can be adjusted to address any specific depth indicated in an OCT image in a few milliseconds. We demonstrate the performance of the joint system in the depth dependent measurements of an ex vivo swine tissue and in vivo human skin. This system can be useful in measuring samples embedded with small targets, for example, to identify tumors in skin in vivo and assessment of tumor margins, in which OCT can be used to perform initial real-time screening with high throughput based on morphological features to identify suspicious targets then CRS is guided to address the targets in real time and fully characterize their biochemical fingerprints for confirmation.

Correlation and Difference Between Raman Spectral Characteristic and Feature Evaluation for Leukocytes and Tumor Cells

Tumor detection supported by Raman spectroscopy is becoming increasingly popular, yet the relevance of spectral variation and feature selection retains unclear. Here we determined the correlation and difference between spectral characteristic and feature evaluation for leukocytes and tumor cells. Some peaks were found to show noticeable spectral differences, and their intensity distributions were investigated, finding using log-normal distribution to describe Raman intensity pattern may be more appropriate. Further the importance of all Raman features was calculated, where some other peak features occupied the top status. By surveying the intensity variation and feature evaluation for those peaks, we concluded the peak with the highest importance does not correspond to the peak location with the most noticeable intensity difference in spectra. Moreover, the peak intensity ratio of I₁₅₁₇/I₇₁₉ associated with protein to nucleic acid level presented the maximum separation, thus, it can be recognized as a special indicator to develop an alternative cancer detection. It is inspiring to introduce advanced statistical models into bio-spectroscopic fields but those intrinsic spectral variations rather than classification performance should be valued. Our explorations can provide possibilities to reveal the essences within tumor carcinogenesis based on Raman spectroscopy, further overwhelming the obstacles during the translation into clinical applications.

Finding reduced Raman spectroscopy fingerprint of skin samples for melanoma diagnosis through machine learning

Early-stage detection of cutaneous melanoma can vastly increase the chances of cure. Excision biopsy followed by histological examination is considered the gold standard for diagnosing the disease, but requires long high-cost processing time, and may be biased, as it involves qualitative assessment by a professional. In this paper, we present a new machine learning approach using raw data for skin Raman spectra as input. The approach is highly efficient for classifying benign versus malignant skin lesions (AUC 0.98, 95% CI 0.97-0.99). Furthermore, we present a high-performance model (AUC 0.97, 95% CI 0.95-0.98) using a miniaturized spectral range (896-1039 cm^-1), thus demonstrating that only a single fragment of the biological fingerprint Raman region is needed for producing an accurate diagnosis. These findings could favor the future development of a cheaper and dedicated Raman spectrometer for fast and accurate cancer diagnosis.

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.

Raman Spectroscopy Technique: A Non-Invasive Tool in Celiac Disease Diagnosis

Celiac disease (CD) is diagnosed by a combination of specific serology and typical duodenal lesions. The histological confirmation of CD, mandatory in the majority of patients with suspected CD, is based on invasive and poorly tolerated procedures, such as upper gastrointestinal endoscopy. In this study we propose an alternative and non-invasive methodology able to confirm the diagnosis of CD based on the analysis of serum samples using the Raman spectroscopy technique. Three different bands centered at 1650, 1450 and 1003 cm^-1 have been considered and the A₁₄₅₀/A₁₀₀₃ and A₁₆₅₀/A₁₀₀₃ ratios have been computed to discriminate between CD and non-CD subjects. The reliability of the methodology was validated by statistical analysis using receiver operating characteristic (ROC) curves. The Youden index was also determined to obtain optimal cut-off points. The obtained results highlighted that the proposed methodology was able to distinguish between CD and non-CD subjects with 98% accuracy. The optimal cut-off points revealed, for both the A₁₄₅₀/A₁₀₀₃ and A₁₆₅₀/A₁₀₀₃ ratios, high values of sensitivity and specificity (>95.0% and >92.0% respectively), confirming that Raman spectroscopy may be considered a valid alternative to duodenal biopsy and demonstrates spectral changes in the secondary structures of the protein network.

Assessment of Transdermal Delivery of Topical Compounds in Skin Scarring Using a Novel Combined Approach of Raman Spectroscopy and High-Performance Liquid Chromatography

Objective: The goal of any topical formulation is efficient transdermal delivery of its active components. However, delivery of compounds can be problematic with penetration through tough layers of fibrotic dermal scar tissue. Approach: We propose a new approach combining high-performance liquid chromatography (HPLC) and Raman spectroscopy (RS) using a topical of unknown composition against a well-known antiscar topical (as control). Results: Positive detection of compounds within the treatment topical using both techniques was validated with mass spectrometry. RS detected conformational structural changes; the 1,655/1,446 cm^-1 ratio estimating collagen content significantly decreased (p < 0.05) over weeks 4, 12, and 16 compared with day 0. The amide I band, known to represent collagen and protein in skin, shifted from 1,667 to 1,656 cm^-1, which may represent a change from β-sheets in elastin to α-helices in collagen. Confirmatory elastin immunohistochemistry decreased compared with day 0, conversely the collagen I/III ratio increased in the same samples by week 12 (p < 0.05, and p < 0.0001, respectively), in keeping with normal scar formation. Optical coherence tomography attenuation coefficient representing collagen deposition was significantly decreased at week 4 compared with day 0 and increased at week 16 (p < 0.05). Innovation: This study provides a platform for further research on the simultaneous evaluation of the effects of compounds in cutaneous scarring by RS and HPLC, and identifies a role for RS in the therapeutic evaluation and theranostic management of skin scarring. Conclusions: RS can provide noninvasive information on the effects of topicals on scar pathogenesis and structural composition, validated by other analytical techniques.

Application of Raman Spectroscopy for the Evaluation of Metabolomic Dynamic Analysis in Athletic Horses

Raman spectroscopy is a rapid qualitative and quantitative technique that allows the simultaneous determination of several components, both biomolecules both chemical compounds, in the biological fluids to assess the metabolic status. In this study, the serum composition was evaluated in regularly trained athletic horses using Raman spectroscopy to identify biomarkers of sports performance. Five clinically healthy and regularly trained Italian Saddle horses were subjected to a standardized obstacle course (350 m/minute; eleven 1.25 high jumps) preceded by a warm-up. On the collected sera, at rest, immediately after exercise, 30 minutes, and 1 hour after the end of the exercise Raman measurements were performed using a diode laser with the excitation wavelength of 785 nm. The analysis of the obtained spectra allowed the identification of peaks and bands different in position and intensity among the experimental conditions. The acquired spectra, obtained from horse sera collected during the experimental protocol, were visually similar, except for the large band detected in the 1,250-1,800 cm^-1 range. The spectral intensity of the Raman spectrum decreased after training and 30 minutes after the end of exercise respect to the before exercise value, to come to the basal value after 60 minutes the end of the exercise. In conclusion, we can claim the ability of Raman spectroscopy to reveal the metabolic status of horses after physical exercise.

Assessment of Raman Spectroscopy for Reducing Unnecessary Biopsies for Melanoma Screening

A key challenge in melanoma diagnosis is the large number of unnecessary biopsies on benign nevi, which requires significant amounts of time and money. To reduce unnecessary biopsies while still accurately detecting melanoma lesions, we propose using Raman spectroscopy as a non-invasive, fast, and inexpensive method for generating a “second opinion” for lesions being considered for biopsy. We collected in vivo Raman spectral data in the clinical skin screening setting from 52 patients, including 53 pigmented lesions and 7 melanomas. All lesions underwent biopsies based on clinical evaluation. Principal component analysis and logistic regression models with leave one lesion out cross validation were applied to classify melanoma and pigmented lesions for biopsy recommendations. Our model achieved an area under the receiver operating characteristic (ROC) curve (AUROC) of 0.903 and a specificity of 58.5% at perfect sensitivity. The number needed to treat for melanoma could have been decreased from 8.6 (60/7) to 4.1 (29/7). This study in a clinical skin screening setting shows the potential of Raman spectroscopy for reducing unnecessary skin biopsies with in vivo Raman data and is a significant step toward the application of Raman spectroscopy for melanoma screening in the clinic.

Superpixel Raman spectroscopy for rapid skin cancer margin assessment

Spontaneous Raman micro-spectroscopy has been demonstrated great potential in delineating tumor margins; however, it is limited by slow acquisition speed. We describe a superpixel acquisition approach that can expedite acquisition between ~×100 and ×10 000, as compared to point-by-point scanning by trading off spatial resolution. We present the first demonstration of superpixel acquisition on rapid discrimination of basal cell carcinoma tumor from eight patients undergoing Mohs micrographic surgery. Results have been demonstrated high discriminant power for tumor vs normal skin based on the biochemical differences between nucleus, collagen, keratin and ceramide. We further perform raster-scanned superpixel Raman imaging on positive and negative margin samples. Our results indicate superpixel acquisition can facilitate the use of Raman microspectroscopy as a rapid and specific tool for tumor margin assessment.

Towards development of a novel universal medical diagnostic method: Raman spectroscopy and machine learning

This review summarizes recent progress made using Raman spectroscopy and machine learning for potential universal medical diagnostic applications.

Normal-subtracted preprocessing of Raman spectra aiming to discriminate skin actinic keratosis and neoplasias from benign lesions and normal skin tissues

The differences in the biochemistry of normal and cancerous tissue could be better exploited by Raman spectroscopy when the spectral information from normal tissue is subtracted from the abnormal tissues. In this study, we evaluated the use of the normal-subtracted spectra to evidence the biochemical differences in the pre-cancerous and cancerous skin tissues compared with normal skin, and to discriminate the groups with altered tissues with respect to the normal sites. Raman spectra from skin tissues [normal (Normal), benign (dermatitis-BEN), basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and actinic keratosis (KER)] were obtained in vivo (Silveira et al., 2015, doi: https://doi.org/10.1002/lsm.22318) and used to develop the spectral model. The mean spectrum of the normal sites (circumjacent to each lesion) from each subject was calculated and subtracted from each individual spectrum of that particular subject independently of the group (Normal, BEN, BCC, SCC, KERAT). The mean spectra of each altered group and the mean spectra of the differences were firstly evaluated in terms of biochemical contribution or differentiation comparing the normal site. Then, the normal-subtracted spectra were submitted to discriminant models based on partial least squares and principal components regression (PLS-DA and PCR-DA), and the discrimination were compared with the model using non-subtracted spectra. Results showed that the peaks of nucleic acids, lipids (triolein) and proteins (elastin and collagens I, III, and IV) were significantly different in the lesions, higher for the pre- and neoplastic lesions compared with normal and benign. The PLS-DA showed that the groups could be discriminated with 90.3% accuracy when the mean-subtracted spectra were used, contrasting with 75.1% accuracy when the non-subtracted spectra were used. Also, when discriminating non-neoplastic tissue (Normal + BEN) from pre- and neoplastic sites (BCC + SCC + KERAT), the accuracy increases to 92.5% for the normal-subtracted compared with 85.3% for the non-subtracted. The subtraction of the mean normal spectrum from the subject obtained circumjacent to each lesion could significantly increase the diagnostic capability of the Raman-based discrimination algorithm.

Physiological model using diffuse reflectance spectroscopy for nonmelanoma skin cancer diagnosis

Diffuse reflectance spectroscopy (DRS) is a noninvasive, fast, and low-cost technology with potential to assist cancer diagnosis. The goal of this study was to test the capability of our physiological model, a computational Monte Carlo lookup table inverse model, for nonmelanoma skin cancer diagnosis. We applied this model on a clinical DRS dataset to extract scattering parameters, blood volume fraction, oxygen saturation and vessel radius. We found that the model was able to capture physiological information relevant to skin cancer. We used the extracted parameters to classify (basal cell carcinoma [BCC], squamous cell carcinoma [SCC]) vs actinic keratosis (AK) and (BCC, SCC, AK) vs normal. The area under the receiver operating characteristic curve achieved by the classifiers trained on the parameters extracted using the physiological model is comparable to that of classifiers trained on features extracted via Principal Component Analysis. Our findings suggest that DRS can reveal physiologic characteristics of skin and this physiologic model offers greater flexibility for diagnosing skin cancer than a pure statistical analysis. Physiological parameters extracted from diffuse reflectance spectra data for nonmelanoma skin cancer diagnosis.

Rapid Discrimination of Malaria- and Dengue-Infected Patients Sera Using Raman Spectroscopy

Malaria and dengue have overlapping clinical symptoms and are prevalent in the same geographic region (tropical and subtropical), hence precise diagnosis is challenging. The high mortality rate associated with both malaria and dengue could be attributed to "false", "delayed", or "missed" diagnosis. The present study thus aims to stratify malaria and dengue using Raman spectroscopy (RS). In total, 130 human sera were analyzed for model development and double-blinded testing. Principal components linear discriminant analysis (PC-LDA) of acquired RS-spectra could classify malaria and dengue with a minor overlap of 16.7%. Receiver operating characteristic (ROC) analysis of test samples showed sensitivity/specificity of 0.9529 for malaria vs healthy controls (HC) and 0.9584 for dengue vs HC. The Raman findings were complemented by mass spectroscopy (MS)-based metabolite analysis of 8 individuals, each from malaria, dengue, and HC. Several of the metabolites, including amino acids, cell-free DNA, creatinine, and bilirubin, assigned for the predominant RS-bands were also identified by MS and showed similar trends. Our data clearly indicates that RS-based serum analysis using a microprobe has immense potential for early, accurate, and automated detection and discrimination of malaria and dengue, and in the future, it could be extrapolated in field-settings combined with hand-held RS. Further, this approach might be extended to diagnose other closely related infections with similar clinical manifestations.

Portable spectroscopic system for in vivo skin neoplasms diagnostics by Raman and autofluorescence analysis

The present paper studies the applicability of a portable cost-effective spectroscopic system for the optical screening of skin tumors. in vivo studies of Raman scattering and autofluorescence (AF) of skin tumors with the 785 nm excitation laser in the near-infrared region included malignant melanoma, basal cell carcinoma and various types of benign neoplasms. The efficiency of the portable system was evaluated by comparison with a highly sensitive spectroscopic system and with the diagnosis accuracy of a human oncologist. Partial least square analysis of Raman and AF spectra was performed; specificity and sensitivity of various skin oncological pathologies detection varied from 78.9% to 100%. Hundred percent accuracy of benign and malignant skin tumors differentiation is possible only with a combined analysis of Raman and AF signals.

Biophysical basis of skin cancer margin assessment using Raman spectroscopy

Achieving adequate margins during tumor margin resection is critical to minimize the recurrence rate and maximize positive patient outcomes during skin cancer surgery. Although Mohs micrographic surgery is by far the most effective method to treat nonmelanoma skin cancer, it can be limited by its inherent required infrastructure, including time-consuming and expensive on-site histopathology. Previous studies have demonstrated that Raman spectroscopy can accurately detect basal cell carcinoma (BCC) from surrounding normal tissue; however, the biophysical basis of the detection remained unclear. Therefore, we aim to explore the relevant Raman biomarkers to guide BCC margin resection. Raman imaging was performed on skin tissue samples from 30 patients undergoing Mohs surgery. High correlations were found between the histopathology and Raman images for BCC and primary normal structures (including epidermis, dermis, inflamed dermis, hair follicle, hair shaft, sebaceous gland and fat). A previously developed model was used to extract the biochemical changes associated with malignancy. Our results showed that BCC had a significantly different concentration of nucleus, keratin, collagen, triolein and ceramide compared to normal structures. The nucleus accounted for most of the discriminant power (90% sensitivity, 92% specificity - balanced approach). Our findings suggest that Raman spectroscopy is a promising surgical guidance tool for identifying tumors in the resection margins.