Discrimination of basal cell carcinoma and melanoma from normal skin biopsies in vitro through Raman spectroscopy and principal component analysis

Raman spectral band imaging for the diagnostics and classification of canine and feline cutaneous tumors

This study introduces Raman imaging technique for diagnosing skin cancer in veterinary oncology patients (dogs and cats). Initially, Raman spectral bands (with specificity to certain molecular structures and functional groups) were identified in formalin-fixed samples of mast cell tumors and soft tissue sarcomas, obtained through routine veterinary biopsy submissions. Then, a custom-built Raman macro-imaging system featuring an intensified CCD camera (iXon Ultra 888, Andor, UK), tunable narrow-band Semrock (USA) optical filter compartment was used to map the spectral features at 1437 cm-1 and 1655 cm-1 in ex vivo tissue. This approach enabled wide-field (cm2), rapid (within seconds), and safe (< 400 mW/cm2) imaging conditions, supporting accurate diagnosis of tissue state. The findings indicate that machine learning classifiers - particularly support vector machine (SVM) and decision tree (DT) - effectively distinguished between soft tissue sarcoma, mastocytoma and benign tissues using Raman spectral band imaging data. Additionally, combining Raman macro-imaging with residual near-infrared (NIR) autofluorescence as a bimodal imaging technique enhanced diagnostic performance, reaching 85 - 95% in accuracy, sensitivity, specificity, and precision - even with a single spectral band (1437 cm-1 or 1655 cm-1). In conclusion, the proposed bi-modal imaging is a pioneering method for veterinary oncology science, offering to improve the diagnostic accuracy of malignant tumors.

A statistical machine learning approach linking molecular conformational changes to altered mechanical characteristics of skin due to thermal injury

This article develops statistical machine learning models to predict the mechanical properties of skin tissue subjected to thermal injury based on the Raman spectra associated with conformational changes of the molecules in the burned tissue. Ex vivo porcine skin tissue samples were exposed to controlled burn conditions at 200 °F for five different durations: (i) 10s, (ii) 20s, (iii) 30s, (iv) 40s, and (v) 50s. For each burn condition, Raman spectra of wavenumbers 500-2000 cm^-1 were measured from the tissue samples, and tensile testing on the same samples yielded their material properties, including, ultimate tensile strain, ultimate tensile stress, and toughness. Partial least squares regression models were established such that the Raman spectra, describing conformational changes in the tissue, could accurately predict ultimate tensile stress, toughness, and ultimate tensile strain of the burned skin tissues with R² values of 0.8, 0.8, and 0.7, respectively, using leave-two-out cross validation scheme. An independent assessment of the resultant models showed that amino acids, proteins & lipids, and amide III components of skin tissue significantly influence the prediction of the properties of the burned skin tissue. In contrast, amide I has a lesser but still noticeable effect. These results are consistent with similar observations found in the literature on the mechanical characterization of burned skin tissue.

Combining Raman spectroscopy and machine learning to assist early diagnosis of gastric cancer

Gastric cancers, with gastric adenocarcinoma (GAC) as the most common histological type, cause quite a few of deaths. In order to improve the survival rate after GAC treatment, it is important to develop a method for early detection and therapy support of GAC. Raman spectroscopy is a potential tool for probing cancer cell due to its real-time and non-destructive measurements without any additional reagents. In this study, we use Raman spectroscopy to examine GAC samples, and distinguish cancerous gastric mucosa from normal gastric mucosa. Average Raman spectra of two groups show differences at 750 cm^-1, 1004 cm^-1, 1449 cm^-1, 1089-1128 cm^-1, 1311-1367 cm^-1 and 1585-1665 cm^-1, These peaks were assigned to cytochrome c, phenylalanine, phospholipid, collagen, lipid, and unsaturated fatty acid respectively. Furthermore, we build a SENet-LSTM model to realize the automatic classification of cancerous gastric mucosa and normal gastric mucosa, with all preprocessed Raman spectra in the range of 400-1800 cm^-1 as input. An accuracy 96.20% was achieved. Besides, by using masking method, we found the Raman spectral features which determine the classification and explore the explainability of the classification model. The results are consistent with the conclusions obtained from the average spectrum. All results indicate it is potential for pre-cancerous screening to combine Raman spectroscopy and machine learning.

Raman microspectroscopy and machine learning for use in identifying radiation-induced lung toxicity

OBJECTIVE

In this work, we explore and develop a method that uses Raman spectroscopy to measure and differentiate radiation induced toxicity in murine lungs with the goal of setting the foundation for a predictive disease model.

METHODS

Analysis of Raman tissue data is achieved through a combination of techniques. We first distinguish between tissue measurements and air pockets in the lung by using group and basis restricted non-negative matrix factorization. We then analyze the tissue spectra using sparse multinomial logistic regression to discriminate between fibrotic gradings. Model validation is achieved by splitting the data into a training set containing 70% of the data and a test set with the remaining 30%; classification accuracy is used as the performance metric. We also explore several other potential classification tasks wherein the response considered is the grade of pneumonitis and fibrosis sickness.

RESULTS

A classification accuracy of 91.6% is achieved on the test set of fibrotic gradings, illustrating the ability of Raman measurements to detect differing levels of fibrotic disease among the murine lungs. It is also shown via further modeling that coarser consideration of fibrotic grading via binning (ie. 'Low', 'Medium', 'High') does not degrade performance. Finally, we consider preliminary models for pneumonitis discrimination using the same methodologies.

Direct imaging of intracellular RNA, DNA, and liquid-liquid phase separated membraneless organelles with Raman microspectroscopy

Methodologies for direct intracellular imaging of RNA and DNA are necessary for the advancement of bioimaging. Here we show direct label-free imaging of RNA and DNA in single cells by isolating their accurate Raman spectra. Raman images of DNA from interphase cells show intact nucleus, while those from mitotic cells reveal condensed chromosome. The condensed chromosome images are accurate enough to assign the stage of mitotic cell division (e.g., metaphase). Raman spectral features indicate B-DNA double helical conformational form in all the cell lines investigated here. The Raman images of RNAs, on the other hand, reveal liquid-liquid phase separated (LLPS) membraneless organelles in interphase cells, which disappears during mitosis. Further, the Raman spectrum of proteins from the intracellular LLPS organelles indicates slight enrichment of amyloid-like secondary structural features. Vibrational imaging of intracellular DNA and RNA simultaneously would open myriad of opportunities for examining functional biochemical aspects of cells and organelles.

Development of a SERS based cancer diagnosis approach employing cryosectioned thyroid tissue samples on PDMS

An efficient SERS based novel analytical approach named Cryosectioned-PDMS was developed systematically and evaluated applying on 64 thyroid biopsy samples. To utilize thyroid biopsy samples, a 20-μl volume of h-AgNPs suspension was dropped on a 5-μm thick cryosectioned biopsy specimen placed on the PDMS coated glass slide. The SERS spectra from a 10 × 10 points array acquired by mapping 22.5 μm × 22.5 μm sized area from suspended dried droplets placed on the tissue surface. The probability of correctly predicted performance for diagnosis of malignant, benign and healthy tissues was resulted in the accuracy of 100 % for the spectral bands at 667, 724, 920, 960, 1052, 1096, 1315 and 1457 cm^-1 using PCA-fed LDA machine learning. The Cryosectioned-PDMS biophotonic approach with PCA-LDA predictive model demonstrated that the vibrational signatures can accurately recognize the fingerprint of cancer pathology from a healthy one with a simple and fast sample preparation methodology.

Diagnosing molecular subtypes of breast cancer by means of Raman spectroscopy

OBJECTIVES

Raman spectroscopy has been used to discriminate human breast cancer and its different tumor molecular subtypes (luminal A, luminal B, HER2, and triple-negative) from normal tissue in surgical specimens.

MATERIALS AND METHODS

Breast cancer and normal tissue samples from 31 patients were obtained by surgical resection and submitted for histopathology. Before anatomopathological processing, the samples had been submitted to Raman spectroscopy (830 nm, 25 mW excitation laser parameters). In total, 424 Raman spectra were obtained. Principal component analysis (PCA) was used in an exploratory analysis to unveil the compositional differences between the tumors and normal tissues. Discriminant models were developed to distinguish the different cancer subtypes by means of partial least squares (PLS) regression.

RESULTS

PCA vectors showed spectral features referred to the biochemical constitution of breast tissues, such as lipids, proteins, amino acids, and carotenoids, where lipids were decreased and proteins were increased in breast tumors. Despite the small spectral differences between the different subtypes of tumor and normal tissues, the discriminant model based on PLS was able to discriminate the spectra of the breast tumors from normal tissues with an accuracy of 97.3%, between luminal and nonluminal subtypes with an accuracy of 89.9%, between nontriple-negative and triple-negative with an accuracy of 94.7%, and each molecular subtype with an accuracy of 73.0%.

CONCLUSION

PCA could reveal the compositional difference between tumors and normal tissues, and PLS could discriminate the Raman spectra of breast tissues regarding the molecular subtypes of cancer, being a useful tool for cancer diagnosis.

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

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.

Is Computer-Assisted Tissue Image Analysis the Future in Minimally Invasive Surgery? A Review on the Current Status of Its Applications

Purpose: Computer-assisted tissue image analysis (CATIA) enables an optical biopsy of human tissue during minimally invasive surgery and endoscopy. Thus far, it has been implemented in gastrointestinal, endometrial, and dermatologic examinations that use computational analysis and image texture feature systems. We review and evaluate the impact of in vivo optical biopsies performed by tissue image analysis on the surgeon’s diagnostic ability and sampling precision and investigate how operation complications could be minimized. Methods: We performed a literature search in PubMed, IEEE, Xplore, Elsevier, and Google Scholar, which yielded 28 relevant articles. Our literature review summarizes the available data on CATIA of human tissues and explores the possibilities of computer-assisted early disease diagnoses, including cancer. Results: Hysteroscopic image texture analysis of the endometrium successfully distinguished benign from malignant conditions up to 91% of the time. In dermatologic studies, the accuracy of distinguishing nevi melanoma from benign disease fluctuated from 73% to 81%. Skin biopsies of basal cell carcinoma and melanoma exhibited an accuracy of 92.4%, sensitivity of 99.1%, and specificity of 93.3% and distinguished nonmelanoma and normal lesions from benign precancerous lesions with 91.9% and 82.8% accuracy, respectively. Gastrointestinal and endometrial examinations are still at the experimental phase. Conclusions: CATIA is a promising application for distinguishing normal from abnormal tissues during endoscopic procedures and minimally invasive surgeries. However, the efficacy of computer-assisted diagnostics in distinguishing benign from malignant states is still not well documented. Prospective and randomized studies are needed before CATIA is implemented in clinical practice.

New diagnostic and imaging technologies in dermatology

INTRODUCTION

Diagnosis of dermatological disorders is primarily based on clinical examination in combination with histopathology. However, clinical findings alone may not be sufficient for accurate diagnosis and cutaneous biopsies are being associated with morbidity.

OBJECTIVE

The objective of this article is to review the newer technologies along with their applications, limitation and future prospectus.

METHODOLOGY

Comprehensive literature search was performed using electronic online databases "PubMed" and "Google Scholar". Articles published in English language were considered for the review.

RESULTS

In order to improve and/or widen the armamentarium in dermatologic disease diagnosis and therapy, newer emerging technologies are being made available which aid in diagnosis and management. New emerging technologies include confocal microscopy, digital photographic imaging, optical coherence tomography, high frequency ultrasonography, and artificial intelligence. There have been advancements in the dermoscopes.

CONCLUSION

Significant progress is seen in the diagnostic methods and imaging technologies in dermatology, each having its advantages and limitations. Artificial intelligence/machine-based learning software may have a great scope to influence the dermatological practice.

Blind source separation of molecular components of the human skin in vivo: non-negative matrix factorization of Raman microspectroscopy data

Determination of the molecular composition of the skin is crucial for numerous tasks in medicine, pharmacology, dermatology and cosmetology. Confocal Raman microspectroscopy is a sensitive method for the evaluation of molecular depth profiles in the skin in vivo. Since the Raman spectra of most of the skin constituents significantly superimpose, a spectral decomposition by a set of predefined library components is usually performed to disentangle their contributions. However, the incorrect choice of the number and type of components or differences between the spectra of the basic components measured in vitro and in vivo can lead to incorrect results of the decomposition procedure. Here, we investigate an alternative data-driven approach based on a non-negative matrix factorization (NNMF) algorithm of depth-resolved Raman spectra of skin that does not require a priori information of spectral data for the analysis. Using the model and experimentally measured depth-resolved Raman spectra of the upper epidermis in vivo, we show that NNMF provides depth profiles of endogenous molecular components and exogenous agents penetrating through the upper epidermis for the spectra and concentration. Moreover, we demonstrate that this approach is capable of providing new information on the molecular profiles of the skin.

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.

In vivo diagnosis of skin cancer with a portable Raman spectroscopic device

In this study, we performed in vivo diagnosis of skin cancer based on implementation of a portable low-cost spectroscopy setup combining analysis of Raman and autofluorescence spectra in the near-infrared region (800-915 nm). We studied 617 cases of skin neoplasms (615 patients, 70 melanomas, 122 basal cell carcinomas, 12 squamous cell carcinomas and 413 benign tumors) in vivo with a portable setup. The studies considered the patients examined by GPs in local clinics and directed to a specialized Oncology Dispensary with suspected skin cancer. Each sample was histologically examined after excisional biopsy. The spectra were classified with a projection on latent structures and discriminant analysis. To check the classification models stability, a 10-fold cross-validation was performed. We obtained ROC AUCs of 0.75 (0.71-0.79; 95% CI), 0.69 (0.63-0.76; 95% CI) and 0.81 (0.74-0.87; 95% CI) for classification of a) malignant and benign tumors, b) melanomas and pigmented tumors and c) melanomas and seborrhoeic keratosis, respectively. The positive and negative predictive values ranged from 20% to 52% and from 73% to 99%, respectively. The biopsy ratio varied from 0.92:1 to 4.08:1 (at sensitivity levels from 90% to 99%). The accuracy of automatic analysis with the proposed system is higher than the accuracy of GPs and trainees, and is comparable or less to the accuracy of trained dermatologists. The proposed approach may be combined with other optical techniques of skin lesion analysis, such as dermoscopy- and spectroscopy-based computer-assisted diagnosis systems to increase accuracy of neoplasms classification.

Melanins as Sustainable Resources for Advanced Biotechnological Applications

Melanins are a class of biopolymers that are widespread in nature and have diverse origins, chemical compositions, and functions. Their chemical, electrical, optical, and paramagnetic properties offer opportunities for applications in materials science, particularly for medical and technical uses. This review focuses on the application of analytical techniques to study melanins in multidisciplinary contexts with a view to their use as sustainable resources for advanced biotechnological applications, and how these may facilitate the achievement of the United Nations Sustainable Development Goals.

Optical clearing agent increases effectiveness of photodynamic therapy in a mouse model of cutaneous melanoma: an analysis by Raman microspectroscopy

Melanoma is the most aggressive type of skin cancer and a relevant health problem due to its poor treatment response with high morbidity and mortality rates. This study, aimed to investigate the tissue changes of an improved photodynamic therapy (PDT) response when combined with optical clearing agent (OCA) in the treatment of cutaneous melanoma in mice. Photodithazine (PDZ) was administered intraperitoneally and a solution of OCA was topically applied before PDT irradiation. Due to a resultant refractive index matching, OCA-treated tumors are more optically homogenous, improving the PDT response. Raman analysis revealed, when combined with OCA, the PDT response was more homogenous down to 725 µm-depth in thickness.

Machine learning for composition analysis of ssDNA using chemical enhancement in SERS

Surface-enhanced Raman spectroscopy (SERS) is an attractive method for bio-chemical sensing due to its potential for single molecule sensitivity and the prospect of DNA composition analysis. In this manuscript we leverage metal specific chemical enhancement effect to detect differences in SERS spectra of 200-base length single-stranded DNA (ssDNA) molecules adsorbed on gold or silver nanorod substrates, and then develop and train a linear regression as well as neural network models to predict the composition of ssDNA. Our results indicate that employing substrates of different metals that host a given adsorbed molecule leads to distinct SERS spectra, allowing to probe metal-molecule interactions under distinct chemical enhancement regimes. Leveraging this difference and combining spectra from different metals as an input for PCA (Principal Component Analysis) and NN (Neural Network) models, allows to significantly lower the detection errors compared to manual feature-choosing analysis as well as compared to the case where data from single metal is used. Furthermore, we show that NN model provides superior performance in the presence of complex noise and data dispersion factors that affect SERS signals collected from metal substrates fabricated on different days.

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.

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.

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.

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.

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.

Emerging imaging technologies in dermatology: Part II: Applications and limitations

Clinical examination is critical for the diagnosis and identification of response to treatment. It is fortunate that technologies are continuing to evolve, enabling augmentation of classical clinical examination with noninvasive imaging modalities. This article discusses emerging technologies with a focus on digital photographic imaging, confocal microscopy, optical coherence tomography, and high-frequency ultrasound, as well as several additional developing modalities. The most readily adopted technologies to date include total-body digital photography and dermoscopy, with some practitioners beginning to use confocal microscopy. In this article, applications and limitations are addressed. For a detailed discussion of the principles involved in these technologies, please refer to the first part of this review article.

Emerging imaging technologies in dermatology: Part I: Basic principles

Dermatologists rely primarily on clinical examination in combination with histopathology to diagnose conditions; however, clinical examination alone might not be sufficient for accurate diagnosis and skin biopsies have associated morbidity. With continued technological advancement, there are emerging ancillary imaging technologies available to dermatologists to aid in diagnosis and management. This 2-part review article will discuss these emerging technologies including: digital photographic imaging, confocal microscopy, optical coherence tomography, and high-frequency ultrasound, as well as several additional modalities in development. In this first installment, the authors describe the breadth of technologies available and the science behind them. Then, in the second article, the authors discuss the applications and limitations of these technologies and future directions.

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.

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.

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.

Raman biophysical markers in skin cancer diagnosis

Raman spectroscopy (RS) has demonstrated great potential for in vivo cancer screening; however, the biophysical changes that occur for specific diagnoses remain unclear. We recently developed an inverse biophysical skin cancer model to address this issue. Here, we presented the first demonstration of in vivo melanoma and nonmelanoma skin cancer (NMSC) detection based on this model. We fit the model to our previous clinical dataset and extracted the concentration of eight Raman active components in 100 lesions in 65 patients diagnosed with malignant melanoma (MM), dysplastic nevi (DN), basal cell carcinoma, squamous cell carcinoma, and actinic keratosis. We then used logistic regression and leave-one-lesion-out cross validation to determine the diagnostically relevant model components. Our results showed that the biophysical model captures the diagnostic power of the previously used statistical classification model while also providing the skin's biophysical composition. In addition, collagen and triolein were the most relevant biomarkers to represent the spectral variances between MM and DN, and between NMSC and normal tissue. Our work demonstrates the ability of RS to reveal the biophysical basis for accurate diagnosis of different skin cancers, which may eventually lead to a reduction in the number of unnecessary excisional skin biopsies performed.

Chemical imaging of aggressive basal cell carcinoma using time-of-flight secondary ion mass spectrometry

A set of basal cell carcinoma samples, removed by Mohs micrographic surgery and pathologically identified as having an aggressive subtype, have been analyzed using time-of-flight secondary ion mass spectrometry (SIMS). The SIMS analysis employed a gas cluster ion beam (GCIB) to increase the sensitivity of the technique for the detection of intact lipid species. The GCIB also allowed these intact molecular signals to be maintained while surface contamination and delocalized chemicals were removed from the upper tissue surface. Distinct mass spectral signals were detected from different regions of the tissue (epidermis, dermis, hair follicles, sebaceous glands, scar tissue, and cancerous tissue) allowing mass spectral pathology to be performed. The cancerous regions of the tissue showed a particular increase in sphingomyelin signals that were detected in both positive and negative ion mode along with increased specific phosphatidylserine and phosphatidylinositol signals observed in negative ion mode. Samples containing mixed more and less aggressive tumor regions showed increased phosphatidylcholine lipid content in the less aggressive areas similar to a punch biopsy sample of a nonaggressive nodular lesion.

Classification of cancer cell lines using matrix-assisted laser desorption/ionization time‑of‑flight mass spectrometry and statistical analysis

Over the past decade, matrix-assisted laser desorption/ionization time‑of‑flight mass spectrometry (MALDI‑TOF MS) has been established as a valuable platform for microbial identification, and it is also frequently applied in biology and clinical studies to identify new markers expressed in pathological conditions. The aim of the present study was to assess the potential of using this approach for the classification of cancer cell lines as a quantifiable method for the proteomic profiling of cellular organelles. Intact protein extracts isolated from different tumor cell lines (human and murine) were analyzed using MALDI‑TOF MS and the obtained mass lists were processed using principle component analysis (PCA) within Bruker Biotyper® software. Furthermore, reference spectra were created for each cell line and were used for classification. Based on the intact protein profiles, we were able to differentiate and classify six cancer cell lines: two murine melanoma (B16‑F0 and B164A5), one human melanoma (A375), two human breast carcinoma (MCF7 and MDA‑MB‑231) and one human liver carcinoma (HepG2). The cell lines were classified according to cancer type and the species they originated from, as well as by their metastatic potential, offering the possibility to differentiate non‑invasive from invasive cells. The obtained results pave the way for developing a broad‑based strategy for the identification and classification of cancer cells.

Raman active components of skin cancer

Raman spectroscopy (RS) has shown great potential in noninvasive cancer screening. Statistically based algorithms, such as principal component analysis, are commonly employed to provide tissue classification; however, they are difficult to relate to the chemical and morphological basis of the spectroscopic features and underlying disease. As a result, we propose the first Raman biophysical model applied to in vivo skin cancer screening data. We expand upon previous models by utilizing in situ skin constituents as the building blocks, and validate the model using previous clinical screening data collected from a Raman optical fiber probe. We built an 830nm confocal Raman microscope integrated with a confocal laser-scanning microscope. Raman imaging was performed on skin sections spanning various disease states, and multivariate curve resolution (MCR) analysis was used to resolve the Raman spectra of individual in situ skin constituents. The basis spectra of the most relevant skin constituents were combined linearly to fit in vivo human skin spectra. Our results suggest collagen, elastin, keratin, cell nucleus, triolein, ceramide, melanin and water are the most important model components. We make available for download (see supplemental information) a database of Raman spectra for these eight components for others to use as a reference. Our model reveals the biochemical and structural makeup of normal, nonmelanoma and melanoma skin cancers, and precancers and paves the way for future development of this approach to noninvasive skin cancer diagnosis.

Label-Free Molecular Imaging of Biological Cells and Tissues by Linear and Nonlinear Raman Spectroscopic Approaches

Raman spectroscopy is an emerging technique in bioanalysis and imaging of biomaterials owing to its unique capability of generating spectroscopic fingerprints. Imaging cells and tissues by Raman microspectroscopy represents a nondestructive and label-free approach. All components of cells or tissues contribute to the Raman signals, giving rise to complex spectral signatures. Resonance Raman scattering and surface-enhanced Raman scattering can be used to enhance the signals and reduce the spectral complexity. Raman-active labels can be introduced to increase specificity and multimodality. In addition, nonlinear coherent Raman scattering methods offer higher sensitivities, which enable the rapid imaging of larger sampling areas. Finally, fiber-based imaging techniques pave the way towards in vivo applications of Raman spectroscopy. This Review summarizes the basic principles behind medical Raman imaging and its progress since 2012.

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.

Effect of low-level laser therapy in an experimental model of osteoarthritis in rats evaluated through Raman spectroscopy

OBJECTIVE

This work aimed to investigate the biochemical changes associated with low-level laser therapy (LLLT) using 660 and 780 nm, on a well-established experimental model of osteoarthritis (OA) in the knees of rats with induced collagenase, using histomorphometry and Raman spectroscopy.

MATERIALS AND METHODS

Thirty-six Wistar rats were divided into four groups: control (GCON, n=9), collagenase without treatment (GCOL, n=9), collagenase with LLLT 660 nm treatment (G660, n=8), and collagenase with LLLT 780 nm treatment (G780, n=10). LLLT protocol was: 30 mW power output, 10 sec irradiation time, 0.04 cm(2) spot size, 0.3 J energy, 0.75 W/cm(2) irradiance, and 7.5 J/cm(2) fluence per session per day, during 14 days. Then, knees were withdrawn and submitted to histomorphometry and Raman spectroscopy analysis. Principal components analysis (PCA) and Mahalanobis distance were employed to characterize the spectral findings.

RESULTS

Histomorphometry revealed a significant increase in the amount of collagen III for the group irradiated with 660 nm. The Raman bands at 1247, 1273, and 1453 cm(-1) (from principal component score PC2), attributed to collagen type II, and 1460 cm(-1) (from PC3), attributed to collagen type III, suggested that the LLLT causes acceleration in cellular activity, especially on the cells that repair cartilage, accelerating the breakdown of cartilage destroyed by collagenase and stimulating the fibroblast to synthesize repairing collagen III.

CONCLUSIONS

LLLT accelerated the initial breakdown of cartilage destroyed by collagenase and stimulated the fibroblast to synthesize the repairing collagen III, suggesting a beneficial effect of LLLT on OA.

Could the bone mineral density (T-score) be correlated with the Raman spectral features of keratin from women's nails and be used to predict osteoporosis?

Osteoporosis is a disease with great importance in current public health due to the associated risk of fracture; therefore, a rapid and accurate diagnosis becomes increasingly important. Recent literature has described a possible relationship between the changes in the organic phase of bone and the changes in nail keratin measured through Raman spectroscopy, aiming at the development of a standard for measuring bone quality and fracture risk both rapid and accurately. This work evaluated the correlation between the bone mineral density (BMD) scores of women with and without osteoporotic disease with the changes in the Raman spectra of the nail keratin, by assessing the intensity of the peak at 510 cm(-1) (S-S bridge) and the scores of principal component analysis (PCA), correlated with the values of BMD measured at the lumbar and hip. Raman spectra of ex vivo fingernails of 213 women were obtained by means of a dispersive Raman spectrometer (830 nm, 300 mW, in the spectral range between 400 and 1,800 cm(-1)). Peak intensities at ∼510 cm(-1) (assigned to the keratin S-S bridge) were measured, and the scores of first principal component loading vectors were calculated. Results showed no differences in the mean Raman spectra of nails of groups with and without osteoporosis. No correlation was found between the BMD scores and both the intensities of the 510 cm(-1) peak and the scores of the first four principal component vectors. Results suggest that BMD and fracture risk could not be assessed by the nail keratin features.

Developing an Instrument-Independent Algorithm for Raman Spectroscopy: A Case of Cancer Detection

One of the problems in the use of Raman spectroscopy for cancer detection in clinical application is the variety of Raman instruments, producing different spectra for the same sample, due to the nature of the measurement system. This prevents the measured spectra from different systems to be compared against one another without appropriate tools and techniques. Therefore, for each instrument one needs to spend considerable amount of time to prepare a set of reference data based on which the future measurements to be interpreted. For early diagnosis of cancer by Raman spectroscopy, there is a need for an algorithm by which such diagnosis can be made by any type of Raman instrument giving rise to the same findings. In the present study we have investigated the detection of breast cancer in three classes of breast samples (normal, benign and cancer) using three different Raman instruments (Almega, Bruker and R3000) to develop an algorithm that, irrespective of the type of Raman instrument, can be applied to the spectra to extract the features necessary to arrive at the same diagnosis. In doing so, we employed different pre-processing methods to eliminate the instrument-dependent effects on the spectra enabling us to fuse such spectra obtained from different instruments. Then, we classified the data using support vector machine (SVM) and multi-layer perception (MLP) to assess the degree to which the employed methods have been able to detect cancer. The results of the study showed that the range and resolution matching using spline interpolation, and noise and fluorescence elimination using wavelet and SNV normalizations were the most sensitive and accurate procedures for eliminating the instrumental specification-based effects and fusing the data from different instruments.

Discriminating neoplastic and normal brain tissues in vitro through Raman spectroscopy: a principal components analysis classification model

BACKGROUND AND OBJECTIVE

Because of their aggressiveness, brain tumors can lead to death within a short time after diagnosis. Optical techniques such as Raman spectroscopy may be a technique of choice for in situ tumor diagnosis, with potential use in determining tumor margins during surgery because of its ability to identify biochemical changes between normal and tumor brain tissues quickly and without tissue destruction.

METHODS

In this work, fragments of brain tumor (glioblastoma, medulloblastoma, and meningioma) and normal tissues (cerebellum and meninges) were obtained from excisional intracranial surgery and from autopsies, respectively. Raman spectra (dispersive spectrometer, 830 nm 350 mW, 50 sec accumulation, total 172 spectra) were obtained in vitro on these fragments. It has been developed as a model to discriminate between the spectra of normal tissue and tumors based on the scores of principal component analysis (PCA) and Euclidean distance.

RESULTS

ANOVA indicated that the scores of PC2 and PC3 show differences between normal and tumor groups (p<0.05) which could be employed in a discrimination model. PC2 was able to discriminate glioblastoma from the other tumors and from normal tissues, showing featured peaks of lipids/phospholipids and cholesterol. PC3 discriminated medulloblastoma and meningioma from normal tissues, with the most intense spectral features of proteins. PC3 also discriminated normal tissues (meninges and cerebellum) by the presence of cholesterol peaks. Results indicated a sensitivity and specificity of 97.4% and 100%, respectively, for this in vitro diagnosis of brain tumor.

CONCLUSIONS

The PCA/Euclidean distance model was effective in differentiating tumor from normal spectra, regardless of the type of tissue (meninges or cerebellum).

Probing lymphoma infiltration in spleen of AKR/J mice chronically exposed to electromagnetic fields for risk assessment--toward noninvasive modeling

Using the AKR/J mouse model, the potential of Raman spectroscopy for monitoring lymphoma in predisposed subjects is demonstrated by discriminating lymphoma infiltration in spleens; the relevance of different excitation profiles is shown. Under green excitation with optimal fluorescence bleaching, stronger DNA bands, intensity variations at amide-III and phenylalanine bands, and the behavior of the 1606/1639 cm(-1) doublet correlate with tumorigenesis. Under red excitation, Raman fingerprints with multivariate models help to discriminate AKR/J-mouse histological subtypes: Lymphoblastic lymphoma (LB) is found significantly distant from both separated lymphocytic lymphoma (LL) and healthy spleen; this agrees with histology since LB has well differentiated large lymphoma cells, while LL, with smaller cells similar to normal lymphocytes, usually cannot be discriminated from normal tissue without histoimmunoassays.