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Raman spectroscopy has been widely demonstrated for tissue characterization and disease discrimination, however current implementations with either 785 or 830 nm near-infrared (NIR) excitation have been ineffectual in tissues with intense autofluorescence such as the liver. Here we report the use of a dispersive 1064 nm Raman system using a low-noise Indium-Gallium-Arsenide (InGaAs) array to discriminate highly autofluorescent bulk tissue ex vivo specimens from healthy liver, adenocarcinoma, and hepatocellular carcinoma (N = 5 per group). The resulting spectra have been combined with a multivariate discrimination algorithm, sparse multinomial logistic regression (SMLR), to predict class membership of healthy and diseased tissues, and spectral bands selected for robust classification have been extracted. A quantitative metric called feature importance is defined based on classification outputs and is used to guide the association of spectral features with biological indicators of healthy and diseased liver tissue. Spectral bands with high feature importance for healthy and liver tumor specimens include retinol, heme, biliverdin, or quinones (1595 cm(-1)); lactic acid (838 cm(-1)); collagen (873 cm(-1)); and nucleic acids (1485 cm(-1)). Classification performance in both binary (normal versus tumor, 100% sensitivity and 89% specificity) and three-group cases (classification accuracy: normal 89%, adenocarcinoma 74%, hepatocellular carcinoma 64%) indicates the potential for accurately separating healthy and cancerous tissues and suggests implications for utilizing Raman techniques during surgical guidance in liver resection.
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Raman spectroscopy is an established technique for molecularly specific characterization of tissues. However, even with near-infrared (NIR) excitation, some tissues possess background autofluorescence, which can overwhelm Raman scattering. Here, we report collection of spectra from tissues with strong autofluorescence using a 1064 nm system with a high-throughput dispersive spectrometer and deep-cooled InGaAs array. Spectra collected at 1064 nm were compared with those collected at 785 nm in specimens from human breast, liver, and kidney. The results demonstrate superior performance at 1064 nm in the liver and kidney, where NIR autofluorescence is intense. The results indicate the feasibility of new biomedical applications for Raman spectroscopy at 1064 nm in tissues with strong autofluorescence.
Assuntos
Raios Infravermelhos , Espectrometria de Fluorescência/métodos , Análise Espectral Raman/métodos , Mama/citologia , Estudos de Viabilidade , Humanos , Rim/citologia , Fígado/citologiaRESUMO
Current diagnostics for lysosomal storage disorders such as mucopolysaccharidosis (MPS) rely on evaluation of ex vivo bodily fluids, which has several shortcomings. In this study, we evaluated whether Raman spectroscopy could noninvasively diagnose MPS in a murine model. Via confocal sampling of the murine outer ear, Raman spectra were obtained at multiple depths. Partial least-squares discriminant analysis of the processed Raman spectra showed a 93% sensitivity and 91% specificity for disease. The discriminant algorithm relied on several Raman bands related to glycosaminoglycans (GAGs) that typically accumulate in MPS. These findings indicate the possibility for a new, noninvasive diagnostic tool for MPS.
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We present a Monte Carlo model, which we use to calculate the depth dependent sensitivity or sampling volume of different single fiber and multi-fiber Raman probes. A two-layer skin model is employed to investigate the dependency of the sampling volume on the absorption and reduced scattering coefficients in the near infrared wavelength range (NIR). The shape of the sampling volume is mainly determined by the scattering coefficient and the wavelength dependency of absorption and scattering has only a small effect on the sampling volume of a typical fingerprint spectrum. An increase in the sampling depth in nonmelanoma skin cancer, compared to normal skin, is obtained.
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OBJECTIVE: To report the use of near-infrared spectroscopic monitoring to recognize mesenteric oxygen desaturations in a preterm neonate with necrotizing enterocolitis as well as the demonstration of reassuring mesenteric tissue perfusion in a twin sibling with an uncomplicated course. DESIGN: Case report. SETTING: Neonatal intensive care unit in a tertiary care children's hospital. PATIENTS: A 12-day-old growth-restricted preterm female twin with necrotizing enterocolitis and her twin who did not develop disease. INTERVENTIONS: In the twin with symptoms of necrotizing enterocolitis, reduction in the mesenteric saturations was recorded in the injured bowel tissue as later confirmed during surgery. After resection of the ischemic bowel, mesenteric saturations returned to values comparable to those measured in the healthy twin. Reduced saturations were not observed in the asymptomatic twin. CONCLUSIONS: The use of optical oximetry to monitor mesenteric tissue saturation may provide a measure of bowel perfusion that could enhance clinical management in at-risk preterm neonates.
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Enterocolite Necrosante/fisiopatologia , Enterocolite Necrosante/cirurgia , Doenças do Prematuro , Consumo de Oxigênio/fisiologia , Circulação Esplâncnica/fisiologia , Gêmeos , California , Nutrição Enteral , Feminino , Humanos , Recém-Nascido , Isquemia/fisiopatologia , Isquemia/cirurgia , Oximetria , Gravidez , Complicações na Gravidez , Espectroscopia de Luz Próxima ao InfravermelhoRESUMO
PURPOSE: Raman spectroscopy has been successfully demonstrated as an effective tool for tissue characterization and diagnosis, but nearly all studies have interrogated adult tissues and diseases. In this study, we demonstrate the application of Raman spectroscopy and its background autofluorescence for pediatric Wilms' tumor diagnosis. METHODS: Eight tumors were measured in this study, along with matched normal kidney tissue in 6 cases. Spectral comparisons were drawn, and diagnostic use was assessed using both the Raman spectral features as well as the inherent tissue fluorescence. RESULTS: The fluorescent background spectra were able to discriminate normal kidney from Wilms' tumor with 81% sensitivity and 100% specificity. The Raman spectra obtained 93% sensitivity and 100% specificity. CONCLUSIONS: This pilot study shows that both autofluorescence and Raman spectra provide diagnostic use in discriminating Wilms' tumor from normal kidney. These techniques may be used individually or in tandem to develop a real-time intraoperative screening and diagnostic device.
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Neoplasias Renais/diagnóstico , Espectrometria de Fluorescência/métodos , Análise Espectral Raman/métodos , Tumor de Wilms/diagnóstico , Estudos de Casos e Controles , Pré-Escolar , Detecção Precoce de Câncer/métodos , Feminino , Humanos , Neoplasias Renais/cirurgia , Masculino , Reconhecimento Automatizado de Padrão , Projetos Piloto , Probabilidade , Medição de Risco , Gestão da Segurança , Sensibilidade e Especificidade , Tumor de Wilms/cirurgiaRESUMO
It has been demonstrated that the presence of cancer results in detectable changes to uninvolved tissues, collectively termed cancer field effects (CFE). In this study, we directly assessed the ability of Raman microspectroscopy to detect CFE via in-vitro study of organotypic tissue rafts approximating human skin. Raman spectra were measured from both epidermis and dermis after transfer of the rafts to dishes containing adherent cultures of either normal human fibroblasts or fibrosarcoma (HT1080) cells. Principal components analyses allowed discrimination between the groups with 86% classification accuracy in the epidermis and 94% in the dermis. These results encourage further study to evaluate the Raman capacity for detecting CFE as a possible tool for noninvasive screening for tumor presence.
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BACKGROUND AND OBJECTIVES: Nonmelanoma skin cancers, including basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), are the most common skin cancers, presenting nearly as many cases as all other cancers combined. The current gold-standard for clinical diagnosis of these lesions is histopathologic examination, an invasive, time-consuming procedure. There is thus considerable interest in developing a real-time, automated, noninvasive tool for nonmelanoma skin cancer diagnosis. In this study, we explored the capability of Raman microspectroscopy to provide differential diagnosis of BCC, SCC, inflamed scar tissue, and normal tissue in vivo. STUDY DESIGN: Based on the results of previous in vitro studies, we developed a portable confocal Raman system with a handheld probe for clinical study. Using this portable system, we measured Raman spectra of 21 suspected nonmelanoma skin cancers in 19 patients with matched normal skin spectra. These spectra were input into nonlinear diagnostic algorithms to predict pathological designation. RESULTS: All of the BCC (9/9), SCC (4/4), and inflamed scar tissues (8/8) were correctly predicted by the diagnostic algorithm, and 19 out of 21 normal tissues were correctly classified. This translates into a 100% (21/21) sensitivity and 91% (19/21) specificity for abnormality, with a 95% (40/42) overall classification accuracy. CONCLUSIONS: These findings reveal Raman microspectroscopy to be a viable tool for real-time diagnosis and guidance of nonmelanoma skin cancer resection.
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Carcinoma Basocelular/diagnóstico , Carcinoma de Células Escamosas/diagnóstico , Neoplasias Cutâneas/diagnóstico , Análise Espectral Raman/métodos , HumanosRESUMO
A common goal of most Raman spectroscopists is the acquisition of clear Raman spectra in short integration times. It is paramount, then, that the hardware used in a Raman system be optimized for throughput, signal-to-noise ratio, and resolution. To this end, we performed a systematic comparison of two Raman spectrographs: the widely used Kaiser HoloSpec and a relatively new commercial offering, the Raman Explorer by Headwall Photonics. Both strong and weak Raman scattering samples were measured using various launching conditions. When resolution-matched, the throughputs of both spectrographs were found to be roughly similar over the central range of the fingerprint region (approximately 800 to 1140 cm(-1), using 785 nm illumination), with the Raman Explorer demonstrating a slight throughput advantage outside this range. Other factors are also considered such that end users may better select the optimum spectrograph for their particular application.
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Análise Espectral Raman/métodos , Desenho de Equipamento , Análise de Falha de Equipamento , Tecnologia de Fibra Óptica , Espectrofotometria Infravermelho , Espectrofotometria Ultravioleta , Análise Espectral Raman/instrumentaçãoRESUMO
We investigate the potential of near-infrared Raman microspectroscopy to differentiate between normal and malignant skin lesions. Thirty-nine skin tissue samples consisting of normal, basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma from 39 patients were investigated. Raman spectra were recorded at the surface and at 20-microm intervals below the surface for each sample, down to a depth of at least 100 microm. Data reduction algorithms based on the nonlinear maximum representation and discrimination feature (MRDF) and discriminant algorithms using sparse multinomial logistic regression (SMLR) were developed for classification of the Raman spectra relative to histopathology. The tissue Raman spectra were classified into pathological states with a maximal overall sensitivity and specificity for disease of 100%. These results indicate the potential of using Raman microspectroscopy for skin cancer detection and provide a clear rationale for future clinical studies.
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Diagnóstico por Computador/métodos , Fotometria/métodos , Refratometria/métodos , Neoplasias Cutâneas/diagnóstico , Análise Espectral/métodos , Humanos , Reprodutibilidade dos Testes , Sensibilidade e EspecificidadeRESUMO
Epithelial cancers, including those of the skin and cervix, are the most common type of cancers in humans. Many recent studies have attempted to use Raman spectroscopy to diagnose these cancers. In this paper, Raman spectral markers related to the temporal and spatial effects of cervical and skin cancers are examined through four separate but related studies. Results from a clinical cervix study show that previous disease has a significant effect on the Raman signatures of the cervix, which allow for near 100% classification for discriminating previous disease versus a true normal. A Raman microspectroscopy study showed that Raman can detect changes due to adjacent regions of dysplasia or HPV that cannot be detected histologically, while a clinical skin study showed that Raman spectra may be detecting malignancy associated changes in tissues surrounding nonmelanoma skin cancers. Finally, results of an organotypic raft culture study provided support for both the skin and the in vitro cervix results. These studies add to the growing body of evidence that optical spectroscopy, in this case Raman spectral markers, can be used to detect subtle temporal and spatial effects in tissue near cancerous sites that go otherwise undetected by conventional histology.
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Biomarcadores Tumorais/análise , Transformação Celular Neoplásica/patologia , Neoplasias Epiteliais e Glandulares/diagnóstico , Neoplasias Epiteliais e Glandulares/patologia , Neoplasias Cutâneas/diagnóstico , Neoplasias Cutâneas/patologia , Análise Espectral Raman/métodos , Neoplasias do Colo do Útero/diagnóstico , Neoplasias do Colo do Útero/patologia , Adolescente , Adulto , Idoso , Técnicas de Cultura de Células , Colo do Útero/patologia , Progressão da Doença , Feminino , Humanos , Pessoa de Meia-Idade , Neoplasias Epiteliais e Glandulares/química , Sensibilidade e Especificidade , Pele/patologia , Neoplasias Cutâneas/química , Neoplasias do Colo do Útero/químicaRESUMO
We explore nine different combinations of fluorescence, light scattering, and polarization spectral imaging approaches in the near-infrare spectral region toward the diagnosis of pathologic and normal esophageal lesions. The combinations of all the imaging techniques were evaluated for maximal sensitivity and specificity. The results suggest that this multimodal approach is capable of highly accurate detection of the presence of pathologic tissue.
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One of the challenges of using Raman spectroscopy for biological applications is the inherent fluorescence generated by many biological molecules that underlies the measured spectra. This fluorescence can sometimes be several orders of magnitude more intense than the weak Raman scatter, and its presence must be minimized in order to resolve and analyze the Raman spectrum. Several techniques involving hardware and software have been devised for this purpose; these include the use of wavelength shifting, time gating, frequency-domain filtering, first- and second-order derivatives, and simple curve fitting of the broadband variation with a high-order polynomial. Of these, polynomial fitting has been found to be a simple but effective method. However, this technique typically requires user intervention and thus is time consuming and prone to variability. An automated method for fluorescence subtraction, based on a modification to least-squares polynomial curve fitting, is described. Results indicate that the presented automated method is proficient in fluorescence subtraction, repeatability, and in retention of Raman spectral lineshapes.