Raman spectroscopy to discriminate laryngeal squamous cell carcinoma from non-cancerous surrounding tissue.

As for many solid cancers, laryngeal cancer is treated surgically, and adequate resection margins are critical for survival. Raman spectroscopy has the capacity to accurately differentiate between cancer and non-cancerous tissue based on their molecular composition, which has been proven in previous work. The aim of this study is to investigate whether Raman spectroscopy can be used to discriminate laryngeal cancer from surrounding non-cancerous tissue. Patients surgically treated for laryngeal cancer were included. Raman mapping experiments were performed ex vivo on resection specimens and correlated to histopathology. Water concentration analysis and CH-stretching region analysis were performed in the high wavenumber range of 2500-4000 cm-1. Thirty-four mapping experiments on 22 resection specimens were used for analysis. Both laryngeal cancer and all non-cancerous tissue structures showed high water concentrations of around 75%. Discriminative information was only found to be present in the CH-stretching region of the Raman spectra of the larynx (discriminative power of 0.87). High wavenumber region Raman spectroscopy can discriminate laryngeal cancer from non-cancerous tissue structures. Contrary to the findings for oral cavity cancer, water concentration is not a discriminating factor for laryngeal cancer.

Automated multimodal spectral histopathology for quantitative diagnosis of residual tumour during basal cell carcinoma surgery.

Multimodal spectral histopathology (MSH), an optical technique combining tissue auto-fluorescence (AF) imaging and Raman micro-spectroscopy (RMS), was previously proposed for detection of residual basal cell carcinoma (BCC) at the surface of surgically-resected skin tissue. Here we report the development of a fully-automated prototype instrument based on MSH designed to be used in the clinic and operated by a non-specialist spectroscopy user. The algorithms for the AF image processing and Raman spectroscopy classification had been first optimised on a manually-operated laboratory instrument and then validated on the automated prototype using skin samples from independent patients. We present results on a range of skin samples excised during Mohs micrographic surgery, and demonstrate consistent diagnosis obtained in repeat test measurement, in agreement with the reference histopathology diagnosis. We also show that the prototype instrument can be operated by clinical users (a skin surgeon and a core medical trainee, after only 1-8 hours of training) to obtain consistent results in agreement with histopathology. The development of the new automated prototype and demonstration of inter-instrument transferability of the diagnosis models are important steps on the clinical translation path: it allows the testing of the MSH technology in a relevant clinical environment in order to evaluate its performance on a sufficiently large number of patients.

Towards Raman-based epidemiological typing of Pseudomonas aeruginosa.

Raman spectra of bacteria can be used as highly specific fingerprints, enabling discrimination at strain level. Pseudomonas aeruginosa strains can be strongly pigmented, making it difficult to obtain high quality spectra of such isolates due to high fluorescent spectral backgrounds. Furthermore, the spectra that could be measured with acceptable quality often showed large spectral variations limiting the reproducibility required for strain level discrimination. P. aeruginosa produces a characteristic yellowish green fluorescent pigment, called pyoverdin. Applying a washing procedure to reduce the amount of fluorescent pigment, enabled the highly pigmented isolates to be measured with sufficient spectral quality. Isolation of the pigment/pyoverdin spectral features, together with spectral scaling methods improved reproducibility. It will be important to analyze the range of the spectral variations that can occur and ensure the correction of all of these factors to obtain the highest reproducibility required for strain level typing.

A novel approach to correct variations in Raman spectra due to photo-bleachable cellular components.

Bacterial typing by Raman spectroscopy is based on small spectral differences that exist between strains, due to differences in their overall molecular composition. These strain-specific spectral differences can be obscured by sources of non-specific signal variance. One such source is the signal contribution of microbial pigments that can vary strongly in intensity. Examples of such pigments are carotenoids in Staphylococcus aureus, and other pigments in Pseudomonas aeruginosa and Mycobacterium lentiflavum. The variance in the intensity of these pigments greatly overshadows strain-specific differences, and therefore lowers spectral reproducibility and causes misclassification of microbial strains. Here a method is presented to determine the spectral signature of pigments of which the relative signal contribution decreases under laser irradiation; so-called photo-bleachable pigments. These signatures are used to eliminate signal variance caused by these pigments by means of the extended multiplicative scatter correction algorithm and spectral interferent subtraction. Application of this method increases the reproducibility of the spectra of microorganisms that contain such pigments to the extent that reproducible identification of samples at strain level is achieved.

Detection of meningioma in dura mater by Raman spectroscopy.

Radical tumor resection is the treatment of choice for patients suffering from meningioma. However, recurrence of these tumors is a problem. Tumor recurrences are attributed to residual nests of meningioma within the regional dura. Therefore, complete removal of all tumor-infiltrated dura is important. Meningioma and normal dura were studied by Raman microspectroscopy to assess the possibility of developing an in vivo Raman method for guidance of meningioma resections. Pseudocolor Raman maps were constructed of cryosections of dura and meningioma, obtained from 20 patients. Comparison of these maps with histopathology enabled assignment of the spectra to either meningioma or dura. Large differences exist between the Raman spectra of dura and meningioma, because of the high collagen content of dura and the increased lipid content of tumors. A classification model for dura and tumor tissue based on linear discriminant analysis of Raman spectra yielded an accuracy of 100%. A first attempt was made to determine the minimum amount of meningioma in dura that is detectable by Raman spectroscopy. It is concluded that Raman spectra enable meningioma to be distinguished from dura, which makes Raman spectroscopy a viable candidate for guidance of surgical resection of meningioma.