Real-time Raman spectroscopy for in vivo skin cancer diagnosis

Multispectral near infrared absorption imaging for histology of skin cancer

Multispectral imaging combines the spectral resolution of spectroscopy with the spatial resolution of imaging and is therefore very useful for biomedical applications. Currently, histological diagnostics use mainly stainings with standard dyes (eg, hematoxylin + eosin) to identify tumors. This method is not applicable in vivo and provides low amounts of chemical information. Biomolecules absorb near infrared light (NIR, 800-1700 nm) at different wavelengths, which could be used to fingerprint tissue. Here, we built a NIR multispectral absorption imaging setup to study skin tissue samples. NIR light (900-1500 nm) was used for homogenous wide-field transmission illumination and detected by a cooled InGaAs camera. In this setup, images I(x, y, λ) from dermatological samples (melanoma, nodular basal-cell carcinoma, squamous-cell carcinoma) were acquired to distinguish healthy from diseased tissue regions. In summary, we show the potential of multispectral NIR imaging for cancer diagnostics.

Rapid label-free analysis of Opisthorchis viverrini eggs in fecal specimens using confocal Raman spectroscopy

Opisthorchis viverrini, a human liver fluke, is highly prevalent in Southeast Asia. Definitive diagnosis of infection is usually achieved parasitologically through the discovery of fluke eggs in feces. However, the eggs of O. viverrini are difficult to differentiate morphologically from those of other minute intestinal flukes in fecal samples, even for experienced technicians. The present study developed a label-free optical methodology for analysis of O. viverrini eggs using Raman spectroscopy. Raman features of O. viverrini eggs were reported that can be used as marker bands for the efficient analysis of O. viverrini eggs in fecal specimens. The methodology presented here allows for the rapid detection of O. viverrini egg infection and can be readily and practicably applied in any clinical setting, even those in which a trained parasitologist is not available.

Discrimination of fresh frozen non-tumour and tumour brain tissue using spectrochemical analyses and a classification model

Introduction: In order for brain tumours to be successfully treated, maximal resection is beneficial. A method to detect infiltrative tumour edges intraoperatively, improving on current methods would be clinically useful. Vibrational spectroscopy offers the potential to provide a handheld, reagent-free method for tumour detection.Purpose: This study was designed to determine the ability of both Raman and Fourier-transform infrared (FTIR) spectroscopy towards differentiating between normal brain tissue, glioma or meningioma.Method: Unfixed brain tissue, which had previously only been frozen, comprising normal, glioma or meningioma tissue was placed onto calcium fluoride slides for analysis using Raman and attenuated total reflection (ATR)-FTIR spectroscopy. Matched haematoxylin and eosin slides were used to confirm tumour areas. Analyses were then conducted to generate a classification model.Results: This study demonstrates the ability of both Raman and ATR-FTIR spectroscopy to discriminate tumour from non-tumour fresh frozen brain tissue with 94% and 97.2% of cases correctly classified, with sensitivities of 98.8% and 100%, respectively. This decreases when spectroscopy is used to determine tumour type.Conclusion: The study demonstrates the ability of both Raman and ATR-FTIR spectroscopy to detect tumour tissue from non-tumour brain tissue with a high degree of accuracy. This demonstrates the ability of spectroscopy when targeted for a cancer diagnosis. However, further improvement would be required for a classification model to determine tumour type using this technology, in order to make this tool clinically viable.

Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks

Importance

Convolutional neural networks (CNNs) achieve expert-level accuracy in the diagnosis of pigmented melanocytic lesions. However, the most common types of skin cancer are nonpigmented and nonmelanocytic, and are more difficult to diagnose.

Objective

To compare the accuracy of a CNN-based classifier with that of physicians with different levels of experience.

Design, Setting, and Participants

A CNN-based classification model was trained on 7895 dermoscopic and 5829 close-up images of lesions excised at a primary skin cancer clinic between January 1, 2008, and July 13, 2017, for a combined evaluation of both imaging methods. The combined CNN (cCNN) was tested on a set of 2072 unknown cases and compared with results from 95 human raters who were medical personnel, including 62 board-certified dermatologists, with different experience in dermoscopy.

Main Outcomes and Measures

The proportions of correct specific diagnoses and the accuracy to differentiate between benign and malignant lesions measured as an area under the receiver operating characteristic curve served as main outcome measures.

Results

Among 95 human raters (51.6% female; mean age, 43.4 years; 95% CI, 41.0-45.7 years), the participants were divided into 3 groups (according to years of experience with dermoscopy): beginner raters (<3 years), intermediate raters (3-10 years), or expert raters (>10 years). The area under the receiver operating characteristic curve of the trained cCNN was higher than human ratings (0.742; 95% CI, 0.729-0.755 vs 0.695; 95% CI, 0.676-0.713; P < .001). The specificity was fixed at the mean level of human raters (51.3%), and therefore the sensitivity of the cCNN (80.5%; 95% CI, 79.0%-82.1%) was higher than that of human raters (77.6%; 95% CI, 74.7%-80.5%). The cCNN achieved a higher percentage of correct specific diagnoses compared with human raters (37.6%; 95% CI, 36.6%-38.4% vs 33.5%; 95% CI, 31.5%-35.6%; P = .001) but not compared with experts (37.3%; 95% CI, 35.7%-38.8% vs 40.0%; 95% CI, 37.0%-43.0%; P = .18).

Conclusions and Relevance

Neural networks are able to classify dermoscopic and close-up images of nonpigmented lesions as accurately as human experts in an experimental setting.

A three-dimensional principal component analysis approach for exploratory analysis of hyperspectral data: identification of ovarian cancer samples based on Raman microspectroscopy imaging of blood plasma

Hyperspectral imaging is a powerful tool to obtain both chemical and spatial information of biological systems. However, few algorithms are capable of working with full three-dimensional images, in which reshaping or averaging procedures are often performed to reduce the data complexity. Herein, we propose a new algorithm of three-dimensional principal component analysis (3D-PCA) for exploratory analysis of complete 3D spectrochemical images obtained through Raman microspectroscopy. Blood plasma samples of ten patients (5 healthy controls, 5 diagnosed with ovarian cancer) were analysed by acquiring hyperspectral imaging in the fingerprint region (∼780-1858 cm-1). Results show that 3D-PCA can clearly differentiate both groups based on its scores plot, where higher loadings coefficients were observed in amino acids, lipids and DNA regions. 3D-PCA is a new methodology for exploratory analysis of hyperspectral imaging, providing fast information for class differentiation.

Raman spectroscopic histology using machine learning for nonalcoholic fatty liver disease

Histopathology requires the expertise of specialists to diagnose morphological features of cells and tissues. Raman imaging can provide additional biochemical information to benefit histological disease diagnosis. Using a dietary model of nonalcoholic fatty liver disease in rats, we combine Raman imaging with machine learning and information theory to evaluate cellular-level information in liver tissue samples. After increasing signal-to-noise ratio in the Raman images through superpixel segmentation, we extract biochemically distinct regions within liver tissues, allowing for quantification of characteristic biochemical components such as vitamin A and lipids. Armed with microscopic information about the biochemical composition of the liver tissues, we group tissues having similar composition, providing a descriptor enabling inference of tissue states, contributing valuable information to histological inspection.

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.

Rapid SERS-based recognition of cell secretome on the folic acid-functionalized gold gratings

Nowadays, functionalization of the plasmon-supported nanostructured surface is considered as a powerful tool for tumour cell recognition. In this study, the SERS on a surface plasmon polariton-supported gold grating functionalized with folic acid was used to demonstrate an unpretentious recognition of melanoma-associated fibroblasts. Using cultivation media conditioned by different cells, we were able to detect reproducible differences in the secretome of melanoma-associated and normal control fibroblasts. The homogeneous distribution of plasmon energy along the grating surface was proved to provide excellent SERS signal reproducibility, while, to increase the affinity of (bio)molecules to SERS substrate, folic acid molecules were covalently grafted to the gold gratings. As proof of concept, fibroblasts were cultured in vitro, and culture media from the normal and tumour-associated lines were collected and analysed with our proposed SERS substrates. Identifying individual peaks of the Raman spectra as well as comparing their relative intensities, we showed that the proposed functional SERS platform can recognise the melanoma-associated cells without the need for further statistical spectral evaluation directly. We also demonstrated that the SERS chip created provided a stable SERS signal over a period of 90 days without loss of sensitivity. Graphical abstract.

A roadmap for the clinical implementation of optical-imaging biomarkers

Clinical workflows for the non-invasive detection and characterization of disease states could benefit from optical-imaging biomarkers. In this Perspective, we discuss opportunities and challenges towards the clinical implementation of optical-imaging biomarkers for the early detection of cancer by analysing two case studies: the assessment of skin lesions in primary care, and the surveillance of patients with Barrett's oesophagus in specialist care. We stress the importance of technical and biological validations and clinical-utility assessments, and the need to address implementation bottlenecks. In addition, we define a translational roadmap for the widespread clinical implementation of optical-imaging technologies.

Hydrogen bound water profiles in the skin influenced by optical clearing molecular agents-Quantitative analysis using confocal Raman microscopy

Confocal Raman microscopy has been used to measure depth-dependent profiles of porcine skin ex vivo in the high wavenumber region after application of molecular optical clearing agents (OCAs). Glycerol (70%) and iohexol (100% Omnipaque [300]) water solutions were used as OCAs and topically applied to porcine ear skin for 30 and 60 minutes. Using Gaussian function-based deconvolution, the changes of hydrogen bound water molecule types have been microscopically analyzed down to the depth of 200 μm. Results show that both OCAs induced skin dehydration (reduction of total water), which is 51.3% for glycerol (60 minutes), 33.1% for glycerol (30 minutes), 8.3% for Omnipaque (60 minutes) and 4.4% for Omnipaque (30 minutes), on average for the 40 to 200 μm depths. Among the water types in the skin, the following reduction was observed in concentration of weakly bound (51.1%, 33.2%, 7.5% and 4.6%), strongly bound (50.4%, 33.0%, 7.9% and 3.4%), tightly bound (63.6%, 42.3%, 26.1% and 12.9%) and unbound (55.4%, 28.7%, 10.1% and 5.9%) water types on average for the 40 to 200 μm depths, post application of glycerol (60 minutes), glycerol (30 minutes), Omnipaque (60 minutes) and Omnipaque (30 minutes), respectively. As most concentrated in the skin, weakly and strongly bound water types are preferentially involved in the OCA-induced water flux in the skin, and thus, are responsible for optical clearing efficiency.

Differentiating melanoma and healthy tissues based on elasticity-specific Brillouin microspectroscopy

The main objective of the present study is to evaluate the use of Brillouin microspectroscopy for differentiation of melanoma and normal tissues based on elasticity measurements. Previous studies of malignant melanoma show that the lesion is stiffer than the surrounding healthy tissue. We hypothesize that elasticity-specific Brillouin spectroscopy can be used to distinguish between healthy and cancerous regions of an excised melanoma from a Sinclair miniature swine. Brillouin measurements of non-regressing and regressing melanomas and the surrounding healthy tissues were performed. Based on the Brillouin measurements, the melanomas and healthy tissues can be successfully differentiated. The stiffness of both tumors is found to be significantly greater than the healthy tissues. Notably, we found that the elasticity of regressing melanoma is closer to that of the normal tissue. The results indicate that Brillouin spectroscopy can be utilized as a tool for elasticity-based differentiation between malignant melanoma and surrounding healthy tissue, with potential use for melanoma boundary identification, monitoring tumor progression, or response to treatment.

Spectroscopic identification of benign (follicular adenoma) and cancerous lesions (follicular thyroid carcinoma) in thyroid tissues

Thyroid follicular nodules are quite common in the population, however only a small proportion is malignant. Thyroid cancer differs from adenoma by features of cellular atypia, angioinvasiveness and possibility of metastasis via blood vessels mainly in the lungs and bones. Pathomorphological examination of the postoperative material plays a significant role in the diagnosis of cystic thyroid lesions. De facto, there is no possibility to determine with certainty whether the lesion is benign or malignant before surgery, therefore new methods are being sought to meet clinical needs. The study aimed to investigate if Fourier-transform infrared spectroscopy (FTIR) spectroscopy and Raman spectroscopy combined with multidimensional analysis can be a useful tool in distinguishing between thyroid adenomas and carcinomas. The obtained results indicate quantitative and qualitative alterations within proteins and fats derived from patients' tissues samples. Raman spectroscopy additionally shows significant changes in the amount of tissue collagen due to the pathogenic process. In the spectra of the second FTIR derivative, shifts of vibrations corresponding to the β-sheet and α-helix structure are observed towards the lower rates of wave numbers in the case of neoplastic tissues. Using the leave-one-out cross-validation, sensitivity and specificity calculated with Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA) clearly shows the possibility to distinguish between pathologically changed and normal thyroid tissue as well as differentiate follicular thyroid adenoma (FTA) from widely invasive follicular thyroid carcinoma (WI-FTC) tissues.

Precise in vivo tissue micro-Raman spectroscopy with simultaneous reflectance confocal microscopy monitoring using a single laser

Raman spectroscopy provides molecular finger-printing of biological tissues. To achieve high spatial resolution, confocal Raman spectroscopy (micro-Raman) has been developed. To guide where micro-Raman is acquired, imaging guidance is necessary, especially for high scattering biological tissue. Reflectance confocal microscopy (RCM) has been integrated with micro-Raman. However, existing systems do not allow point-of-interest micro-Raman measurement with simultaneous RCM guidance. Here we demonstrate how a single laser can be used to localize and acquire micro-Raman signals within tissue with simultaneous real-time RCM imaging. Applications of this RCM-guided micro-Raman system for ex vivo samples and in vivo skin measurements are presented.

Current Advancements in Transdermal Biosensing and Targeted Drug Delivery

In this manuscript, recent advancements in the area of minimally-invasive transdermal biosensing and drug delivery are reviewed. The administration of therapeutic entities through the skin is complicated by the stratum corneum layer, which serves as a barrier to entry and retards bioavailability. A variety of strategies have been adopted for the enhancement of transdermal permeation for drug delivery and biosensing of various substances. Physical techniques such as iontophoresis, reverse iontophoresis, electroporation, and microneedles offer (a) electrical amplification for transdermal sensing of biomolecules and (b) transport of amphiphilic drug molecules to the targeted site in a minimally invasive manner. Iontophoretic delivery involves the application of low currents to the skin as well as the migration of polarized and neutral molecules across it. Transdermal biosensing via microneedles has emerged as a novel approach to replace hypodermic needles. In addition, microneedles have facilitated minimally invasive detection of analytes in body fluids. This review considers recent innovations in the structure and performance of transdermal systems.

Integration of a Raman spectroscopy system to a robotic-assisted surgical system for real-time tissue characterization during radical prostatectomy procedures

Surgical excision of the whole prostate through a radical prostatectomy procedure is part of the standard of care for prostate cancer. Positive surgical margins (cancer cells having spread into surrounding nonresected tissue) occur in as many as 1 in 5 cases and strongly correlate with disease recurrence and the requirement of adjuvant treatment. Margin assessment is currently only performed by pathologists hours to days following surgery and the integration of a real-time surgical readout would benefit current prostatectomy procedures. Raman spectroscopy is a promising technology to assess surgical margins: its in vivo use during radical prostatectomy could help insure the extent of resected prostate and cancerous tissue is maximized. We thus present the design and development of a dual excitation Raman spectroscopy system (680- and 785-nm excitations) integrated to the robotic da Vinci surgical platform for in vivo use. Following validation in phantoms, spectroscopic data from 20 whole human prostates immediately following radical prostatectomy are obtained using the system. With this dataset, we are able to distinguish prostate from extra prostatic tissue with an accuracy, sensitivity, and specificity of 91%, 90.5%, and 96%, respectively. Finally, the integrated Raman spectroscopy system is used to collect preliminary spectroscopic data at the surgical margin in vivo in four patients.

Basal cell carcinoma: Contemporary approaches to diagnosis, treatment, and prevention

As the most common human cancer worldwide and continuing to increase in incidence, basal cell carcinoma is associated with significant morbidity and cost. Continued advances in research have refined both our insight and approach to this seemingly ubiquitous disease. This 2-part continuing medical education series provides a comprehensive and contemporary review of basal cell carcinoma. The second article in this series will present both the current standard of care and newly developed approaches to diagnosis, treatment, and prevention of this disease.

Determination of meningioma brain tumour grades using Raman microspectroscopy imaging

Raman microspectroscopy imaging was used to distinguish 90 brain tissue samples into meningiomas Grade I and Grade II.

Raman spectroscopy for cancer detection and cancer surgery guidance: translation to the clinics

Oncological applications of Raman spectroscopy have been contemplated, pursued, and developed at academic level for at least 25 years. Published studies aim to detect pre-malignant lesions, detect cancer in less invasive stages, reduce the number of unnecessary biopsies and guide surgery towards the complete removal of the tumour with adequate tumour resection margins. This review summarizes actual clinical needs in oncology that can be addressed by spontaneous Raman spectroscopy and it provides an overview over the results that have been published between 2007 and 2017. An analysis is made of the current status of translation of these results into clinical practice. Despite many promising results, most of the applications addressed in scientific studies are still far from clinical adoption and commercialization. The main hurdles are identified, which need to be overcome to ensure that in the near future we will see the first Raman spectroscopy-based solutions being used in routine oncologic diagnostic and surgical procedures.

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.

Raman spectroscopy and multivariate analysis for the non invasive diagnosis of clinically inconclusive vulval lichen sclerosus

Vulval lichen sclerosus (LS) is a common inflammatory condition associated with an increased risk of developing vulval carcinoma. Diagnosis is usually clinical although biopsy is necessary if the diagnosis is uncertain or if there is a failure to respond to adequate initial treatment. Raman spectroscopy has the potential to be applied in vivo for near real time objective non-invasive optical diagnosis, avoiding the need for invasive tissue biopsies. The aim of this study was to evaluate the diagnostic performance of Raman spectroscopy for differentiating LS from other vulval conditions in fresh vulval biopsies. Biopsies were analysed from 27 women with suspected LS in whom the attending gynaecologist could not establish the diagnosis on clinical presentation alone. Spectral variance was explored using principal component analysis and in conjunction with the histological diagnoses was used to develop and test a multivariate linear discriminant classification model. This model was validated with leave one sample out cross validation and the diagnostic performance of the technique assessed in comparison with the pathology gold standard. After cross validation the technique was able to correctly differentiate LS from other inflammatory vulval conditions with a sensitivity of 91% and specificity of 80%. This study demonstrates Raman spectroscopy has potential as a technique for in vivo non-invasive diagnosis of vulval skin conditions. Applied in the clinical setting this technique may reduce the need for invasive tissue biopsy. Further in vivo study is needed to assess the ability of Raman spectroscopy to diagnose other vulval conditions before clinical application.

Raman Spectroscopy and Microscopy Applications in Cardiovascular Diseases: From Molecules to Organs

Noninvasive and label-free vibrational spectroscopy and microscopy methods have shown great potential for clinical diagnosis applications. Raman spectroscopy is based on inelastic light scattering due to rotational and vibrational modes of molecular bonds. It has been shown that Raman spectra provide chemical signatures of changes in biological tissues in different diseases, and this technique can be employed in label-free monitoring and clinical diagnosis of several diseases, including cardiovascular studies. However, there are very few literature reviews available to summarize the state of art and future applications of Raman spectroscopy in cardiovascular diseases, particularly cardiac hypertrophy. In addition to conventional clinical approaches such as electrocardiography (ECG), echocardiogram (cardiac ultrasound), positron emission tomography (PET), cardiac computed tomography (CT), and single photon emission computed tomography (SPECT), applications of vibrational spectroscopy and microscopy will provide invaluable information useful for the prevention, diagnosis, and treatment of cardiovascular diseases. Various in vivo and ex vivo investigations can potentially be performed using Raman imaging to study and distinguish pathological and physiological cardiac hypertrophies and understand the mechanisms of other cardiac diseases. Here, we have reviewed the recent literature on Raman spectroscopy to study cardiovascular diseases covering investigations on the molecular, cellular, tissue, and organ level.

Classification of burn injury using Raman spectroscopy and optical coherence tomography: An ex-vivo study on porcine skin

Accurate depth assessment of burn wounds is a critical task to provide the right treatment and care. Currently, laser Doppler imaging is able to provide better accuracy compared to the standard clinical evaluation. However, its clinical applicability is limited by factors like scanning distance, time, and cost. Precise diagnosis of burns requires adequate structural and functional details. In this work, we evaluated the combined potential of two non-invasive optical modalities, optical coherence tomography (OCT) and Raman spectroscopy (RS), to identify degrees of burn wounds (superficial partial-thickness (SPT), deep partial-thickness (DPT), and full-thickness (FT)). OCT provides morphological information, whereas, RS provides biochemical aspects. OCT images and Raman spectra were obtained from burns created on ex-vivo porcine skin. Algorithms were developed to segment skin region and extract textural features from OCT images, and derive spectral wave features from RS. These computed features were fed into machine learning classifiers for categorization of burns. Histological results obtained from trichrome staining were used as ground-truth. The combined performance of RS-OCT reported an overall average accuracy of 85% and ROC-AUC=0.94, in distinguishing the burn wounds. The significant performance on ex vivo skin motivates to assess the feasibility of combined RS-OCT in in vivo models.

Nonscanning large-area Raman imaging for ex vivo/in vivo skin cancer discrimination

Imaging Raman spectroscopy can be used to identify cancerous tissue. Traditionally, a step-by-step scanning of the sample is applied to generate a Raman image, which, however, is too slow for routine examination of patients. By transferring the technique of integral field spectroscopy (IFS) from astronomy to Raman imaging, it becomes possible to record entire Raman images quickly within a single exposure, without the need for a tedious scanning procedure. An IFS-based Raman imaging setup is presented, which is capable of measuring skin ex vivo or in vivo. It is demonstrated how Raman images of healthy and cancerous skin biopsies were recorded and analyzed.

Evaluation of accuracy dependence of Raman spectroscopic models on the ratio of calibration and validation points for non-invasive glucose sensing

Optical monitoring of blood glucose levels for non-invasive diagnosis is a growing area of research. Recent efforts in this direction have been inclined towards reducing the requirement of calibration framework. Here, we are presenting a systematic investigation on the influence of variation in the ratio of calibration and validation points on the prospective predictive accuracy of spectral models. A fiber-optic probe coupled Raman system has been employed for transcutaneous measurements. Limit of agreement analysis between serum and partial least square regression predicted spectroscopic glucose values has been performed for accurate comparison. Findings are suggestive of strong predictive accuracy of spectroscopic models without requiring substantive calibration measurements. Graphical abstract.

Smart systems for determination of drug's solubility

The solubility of drugs is a crucial physicochemical property in the drug discovery or development process and for improving the bioavailability of drugs. There are various methods for evaluating the solubility of drugs including manual measurement methods, mathematical methods, and smart methods. Manual measurement and mathematical methods have some defects which make the smart systems more reliable and important in this field. In this review, various instruments used for the solubility determination, along with the smart systems, have been discussed. Mechanism and applications of each method have been elaborated in detail. Moreover, unique characteristics as well as some limitations of discussed methods are also described.

Combining high wavenumber and fingerprint Raman spectroscopy for the detection of prostate cancer during radical prostatectomy

For prostate cancer (PCa) patients, radical prostatectomy (complete removal of the prostate) is the only curative surgical option. To date, there is no clinical technique allowing for real-time assessment of surgical margins to minimize the extent of residual cancer. Here, we present a tissue interrogation technique using a dual excitation wavelength Raman spectroscopy system capable of sequentially acquiring fingerprint (FP) and high wavenumber (HWN) Raman spectra. Results demonstrate the ability of the system to detect PCa in post-prostatectomy specimens. In total, 477 Raman spectra were collected from 18 human prostate slices. Each area measured with Raman spectroscopy was characterized as either normal or cancer based on histopathological analyses, and each spectrum was classified based on supervised learning using support vector machines (SVMs). Based on receiver operating characteristic (ROC) analysis, FP (area under the curve [AUC] = 0.89) had slightly superior cancer detection capabilities compared with HWN (AUC = 0.86). Optimal performance resulted from combining the spectral information from FP and HWN (AUC = 0.91), suggesting that the use of these two spectral regions may provide complementary molecular information for PCa detection. The use of leave-one-(spectrum)-out (LOO) or leave-one-patient-out (LOPO) cross-validation produced similar classification results when combining FP with HWN. Our findings suggest that the application of machine learning using multiple data points from the same patient does not result in biases necessarily impacting the reliability of the classification models.

Skin cancer detection using non-invasive techniques

Skin cancer is the most common form of cancer and is globally rising. Historically, the diagnosis of skin cancers has depended on various conventional techniques which are of an invasive manner. A variety of commercial diagnostic tools and auxiliary techniques are available to detect skin cancer. This article explains in detail the principles and approaches involved for non-invasive skin cancer diagnostic methods such as photography, dermoscopy, sonography, confocal microscopy, Raman spectroscopy, fluorescence spectroscopy, terahertz spectroscopy, optical coherence tomography, the multispectral imaging technique, thermography, electrical bio-impedance, tape stripping and computer-aided analysis. The characteristics of an ideal screening test are outlined, and the authors pose several points for clinicians and scientists to consider in the evaluation of current and future studies of skin cancer detection and diagnosis. This comprehensive review critically analyses the literature associated with the field and summarises the recent updates along with their merits and demerits.

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.

Discrimination of malignant and normal kidney tissue with short wave infrared dispersive Raman spectroscopy

Renal mass biopsy is still controversial due to imperfect accuracy. Raman spectroscopy (RS) demonstrated promise as an in vivo real-time, nondestructive diagnostic tool in many malignancies. Short wave infrared (SWIR) RS has the potential to improve on previous RS systems for renal mass diagnosis. The aim of this study is to evaluate a SWIR RS system in differentiating normal and malignant renal samples. Measurements were acquired using a benchtop RS system with excitation wavelength at 1064 nm and an InGaAs array detector. Processed spectra were classified with a Bayesian machine learning algorithm, sparse multinomial logistic regression. Sensitivity and receiver operating characteristic curve analyses evaluated the classifier accuracy. Accuracy of the classifier was 92.5% with sensitivity and specificity of 95.8% and 88.8%, respectively. For posterior probability of malignant class assignment, the area under the ROC curve is 0.94 (95% confidence interval: 0.89-0.99, P < .001). SWIR RS accurately differentiated normal and malignant kidney tumors. RS has the potential to be used as a diagnostic tool in kidney cancer.

In-vivo Raman spectroscopy: from basics to applications

For more than two decades, Raman spectroscopy has found widespread use in biological and medical applications. The instrumentation and the statistical evaluation procedures have matured, enabling the lengthy transition from ex-vivo demonstration to in-vivo examinations. This transition goes hand-in-hand with many technological developments and tightly bound requirements for a successful implementation in a clinical environment, which are often difficult to assess for novice scientists in the field. This review outlines the required instrumentation and instrumentation parameters, designs, and developments of fiber optic probes for the in-vivo applications in a clinical setting. It aims at providing an overview of contemporary technology and clinical trials and attempts to identify future developments necessary to bring the emerging technology to the clinical end users. A comprehensive overview of in-vivo applications of fiber optic Raman probes to characterize different tissue and disease types is also given.

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

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

In vivo Raman spectroscopy for biochemical monitoring of the human cervix throughout pregnancy

BACKGROUND

The cervix must undergo significant biochemical remodeling to allow for successful parturition. This process is not fully understood, especially in instances of spontaneous preterm birth. In vivo Raman spectroscopy is an optical technique that can be used to investigate the biochemical composition of tissue longitudinally and noninvasively in human beings, and has been utilized to measure physiology and disease states in a variety of medical applications.

OBJECTIVE

The purpose of this study is to measure in vivo Raman spectra of the cervix throughout pregnancy in women, and to identify biochemical markers that change with the preparation for delivery and postpartum repair.

STUDY DESIGN

In all, 68 healthy pregnant women were recruited. Raman spectra were measured from the cervix of each patient monthly in the first and second trimesters, weekly in the third trimester, and at the 6-week postpartum visit. Raman spectra were measured using an in vivo Raman system with an optical fiber probe to excite the tissue with 785 nm light. A spectral model was developed to highlight spectral regions that undergo the most changes throughout pregnancy, which were subsequently used for identifying Raman peaks for further analysis. These peaks were analyzed longitudinally to determine if they underwent significant changes over the course of pregnancy (P < .05). Finally, 6 individual components that comprise key biochemical constituents of the human cervix were measured to extract their contributions in spectral changes throughout pregnancy using a linear combination method. Patient factors including body mass index and parity were included as variables in these analyses.

RESULTS

Raman peaks indicative of extracellular matrix proteins (1248 and 1254 cm^-1) significantly decreased (P < .05), while peaks corresponding to blood (1233 and 1563 cm^-1) significantly increased (P < .0005) in a linear manner throughout pregnancy. In the postpartum cervix, significant increases in peaks corresponding to actin (1003, 1339, and 1657 cm^-1) and cholesterol (1447 cm^-1) were observed when compared to late gestation, while signatures from blood significantly decreased. Postpartum actin signals were significantly higher than early pregnancy, whereas extracellular matrix proteins and water signals were significantly lower than early weeks of gestation. Parity had a significant effect on blood and extracellular matrix protein signals, with nulliparous patients having significant increases in blood signals throughout pregnancy, and higher extracellular matrix protein signals in early pregnancy compared to patients with prior pregnancies. Body mass index significantly affected actin signal contribution, with low body mass index patients showing decreasing actin contribution throughout pregnancy and high body mass index patients demonstrating increasing actin signals.

CONCLUSION

Raman spectroscopy was successfully used to biochemically monitor cervical remodeling in pregnant women during prenatal visits. This foundational study has demonstrated sensitivity to known biochemical dynamics that occur during cervical remodeling, and identified patient variables that have significant effects on Raman spectra throughout pregnancy. Raman spectroscopy has the potential to improve our understanding of cervical maturation, and be used as a noninvasive preterm birth risk assessment tool to reduce the incidence, morbidity, and mortality caused by preterm birth.

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.

Error analysis of the spectral shift for partial least squares models in Raman spectroscopy

Raman spectroscopy paired with the partial least squares (PLS) method is commonly used for quantitative or qualitative analysis of complex samples. However, spectral shift induced by different Raman spectroscopy, different environment or different measured time will decrease the accuracy of the PLS model. In this work, the processing algorithms that improve the accuracy by removing the noise, background and varying sources of other spectral interference were first reviewed. The error induced by the spectral shift was analyzed and the formulas of the error were derived. The formulas were then used to calculate the theoretical error in the example of discriminating human and nonhuman blood. A comparison of the actual errors obtained from the mathematical method and experiment with the theoretical value demonstrated the effectiveness of the equation. The compensation for nonhuman blood according to the average error demonstrated the improvement of the accuracy. Finally, the non-uniform sampling of the Raman shift by charge-coupled device (CCD) was considered in the error equation. An accurate error equation was obtained. This work could help improve the stability of PLS models in the case of the spectral shift of the spectrometer in Raman spectroscopy.

Autofluorescence and white light imaging-guided endoscopic Raman and diffuse reflectance spectroscopy for in vivo nasopharyngeal cancer detection

Nasopharyngeal cancer (NPC) is an endemic with high incidence in Southern China and Southeast Asia countries. Screening for NPC under conventional white light imaging (WLI) nasopharyngoscope examination remains a great clinical challenge due to its poor sensitivity. Here, we developed an integrated 4-modality endoscopy system combining WLI, autofluorescence imaging (AFI), diffuse reflectance spectroscopy and Raman spectroscopy technologies for in vivo endoscopic cancer detection for the first time. A pilot clinical test of the system for NPC detection was conducted, in which 283 in vivo Raman and diffuse reflectance spectral data sets from 30 NPC patients and 30 healthy subjects were acquired under the guidance of AFI and WLI. Both high diagnostic sensitivity (98.6%) and high specificity (95.1%) for differentiating cancer from normal tissue sites were achieved using this system combined with principal component analysis-linear discriminant analysis diagnostic algorithm, demonstrating great potential for improving real-time, in vivo diagnosis of NPC at endoscopy.

Raman spectroscopy for assessment of bone resection margins in mandibulectomy for oral cavity squamous cell carcinoma

OBJECTIVES

The aim of this study was to investigate the potential of Raman spectroscopy for detection of oral cavity squamous cell carcinoma (OCSCC) in bone resection surfaces during mandibulectomy.

MATERIALS & METHODS

Raman mapping experiments were performed on fresh mandible resection specimens from patients treated with mandibulectomy for OCSCC. A tumour detection algorithm was created based on water concentration and the high-wavenumber range (2800 cm^-1-3050 cm^-1) of the Raman spectra.

RESULTS

Twenty-six ex vivo Raman mapping experiments were performed on 26 fresh mandible resection specimens obtained from 22 patients. The algorithm was applied on an independent test set and showed an accuracy of 95%, a sensitivity of 95%, and a specificity of 87%.

CONCLUSION

These results form the basis for further development of a Raman spectroscopy tool as an objective method for intraoperative assessment of bone resection margins.

Rapid diagnostics of liver steatosis by Raman spectroscopyviafiber optic probe: a pilot study

Raman spectroscopyviafiber optic probes enables assessment of the liver condition and rapid quantification of liver steatosis, thus, this technique has the potential as a diagnostic tool.

Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms

Currently, diagnosis of skin diseases is based primarily on the visual pattern recognition skills and expertise of the physician observing the lesion. Even though dermatologists are trained to recognize patterns of morphology, it is still a subjective visual assessment. Tools for automated pattern recognition can provide objective information to support clinical decision-making. Noninvasive skin imaging techniques provide complementary information to the clinician. In recent years, optical coherence tomography (OCT) has become a powerful skin imaging technique. According to specific functional needs, skin architecture varies across different parts of the body, as do the textural characteristics in OCT images. There is, therefore, a critical need to systematically analyze OCT images from different body sites, to identify their significant qualitative and quantitative differences. Sixty-three optical and textural features extracted from OCT images of healthy and diseased skin are analyzed and, in conjunction with decision-theoretic approaches, used to create computational models of the diseases. We demonstrate that these models provide objective information to the clinician to assist in the diagnosis of abnormalities of cutaneous microstructure, and hence, aid in the determination of treatment. Specifically, we demonstrate the performance of this methodology on differentiating basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) from healthy tissue.

Raman Spectroscopy Applied to Parathyroid Tissues: A New Diagnostic Tool to Discriminate Normal Tissue from Adenoma

Primary hyperparathyroidism is an endocrine disorder characterized by autonomous production of parathyroid hormone. Patients with the symptomatic disease should be referred for parathyroidectomy. However, the distinction between the pathological condition and the benign one is very challenging in the surgical setting; therefore, accurate recognition is important to ensure success during minimally invasive surgery. At present, all intraoperative techniques significantly increase surgical time and, consequently, cost. In this proof-of-concept study, Raman microscopy was used to differentiate between healthy parathyroid tissue and parathyroid adenoma from 18 patients. The data showed different spectroscopic features for the two main tissue types of healthy and adenoma. Moreover, the parathyroid adenoma subtypes (chief cells and oxyphil cells) were characterized by their own Raman spectra. The partial least-squares discriminant analysis (PLS-DA) model built to discriminate healthy from adenomatous parathyroid tissue was able to correctly classify all samples in the calibration and validation data sets, providing 100% prediction accuracy. The PLS-DA model built to discriminate chief cell adenoma from oxyphil cell adenoma allowed us to correctly classify >99% of the spectra during calibration and cross-validation and to correctly predict 100% of oxyphil and 99.8% of chief cells in the external validation data set. The results clearly demonstrate the great potential of Raman spectroscopy. The final goal would be development of a Raman portable fiber probe device for intraoperative optical biopsy, both to improve the surgical success rate and reduce surgical cost.

Improving diagnostic sensitivity of combined dermoscopy and reflectance confocal microscopy imaging through double reader concordance evaluation in telemedicine settings: A retrospective study of 1000 equivocal cases

BACKGROUND

Reflectance confocal microscopy (RCM) is an imaging device that permits non-invasive visualization of cellular morphology and has been shown to improve diagnostic accuracy of dermoscopically equivocal cutaneous lesions. The application of double reader concordance evaluation of dermoscopy-RCM image sets in retrospective settings and its potential application to telemedicine evaluation has not been tested in a large study population.

OBJECTIVE

To improve diagnostic sensitivity of RCM image diagnosis using a double reader concordance evaluation approach; to reduce mismanagement of equivocal cutaneous lesions in retrospective consultation and telemedicine settings.

METHODS

1000 combined dermoscopy-RCM image sets were evaluated in blind by 10 readers with advanced training and internship in dermoscopy and RCM evaluation. We compared sensitivity and specificity of single reader evaluation versus double reader concordance evaluation as well as the effect of diagnostic confidence on lesion management in a retrospective setting.

RESULTS

Single reader evaluation resulted in an overall sensitivity of 95.2% and specificity of 76.3%, with misdiagnosis of 8 melanomas, 4 basal cell carcinomas and 2 squamous cell carcinomas. Combined double reader evaluation resulted in an overall sensitivity of 98.3% and specificity of 65.5%, with misdiagnosis of 1 in-situ melanoma and 2 basal cell carcinomas.

CONCLUSION

Evaluation of dermoscopy-RCM image sets of cutaneous lesions by single reader evaluation in retrospective settings is limited by sensitivity levels that may result in potential mismanagement of malignant lesions. Double reader blind concordance evaluation may improve the sensitivity of diagnosis and management safety. The use of a second check can be implemented in telemedicine settings where expert consultation and second opinions may be required.