Optical coherence tomography (OCT) is rapidly becoming the method of choice for assessing arterial wall pathology in vivo. Atherosclerotic plaques can be diagnosed with high accuracy, including measurement of the thickness of fibrous caps, enabling an assessment of the risk of rupture. While the OCT image presents morphological information in highly resolved detail, it relies on interpretation of the images by trained readers for the identification of vessel wall components and tissue type. We present a framework to enable systematic and automatic classification of atherosclerotic plaque constituents, based on the optical attenuation coefficient mu(t) of the tissue. OCT images of 65 coronary artery segments in vitro, obtained from 14 vessels harvested at autopsy, are analyzed and correlated with histology. Vessel wall components can be distinguished based on their optical properties: necrotic core and macrophage infiltration exhibit strong attenuation, mu(t)>or=10 mm(-1), while calcific and fibrous tissue have a lower mu(t) approximately 2-5mm(-1). The algorithm is successfully applied to OCT patient data, demonstrating that the analysis can be used in a clinical setting and assist diagnostics of vessel wall pathology.

Pancreatic endocrine neoplasms (PEN), otherwise known as pancreatic neuroendocrine tumour, islet cell tumour, and APUDoma (amine precursor uptake and decarboxylationoma) are potentially malignant. However, the rate of malignancy in these tumours is difficult to predict. Fine needle aspiration (FNA) is a safe and very useful method for the investigation of pancreatic tumours, in particular with the modern imaging and guiding techniques such as computed tomography (CT) and endoscopic ultrasonography. We report an extremely rare case of a black pigmented pancreatic endocrine tumour, primarily diagnosed by fine needle aspiration cytology (FNAC), the diagnosis of which was confirmed by histochemical, immunohistochemical and electron microscopic (EM) investigation of the resection specimen.

As the possibilities in the treatment of cancer continue to evolve, its early detection and correct diagnosis are becoming increasingly important. From the early detection of cancer to the guidance of oncosurgical procedures new sensitive in vivo diagnostic tools are much needed. Many studies report the Raman spectroscopic detection of malignant and premalignant tissues in different sites of the body with high sensitivities. The great appeal of this technique lies in its potential for in vivo clinical implementation. We present an overview of the in vitro and in vivo work on the oncological application of Raman spectroscopy and discuss its potential as a new tool in the clinico‐oncological practice. Opportunities for integration of Raman spectroscopy in oncological cure and care as a real‐time guidance tool during diagnostic (i.e. biopsy) and therapeutic (surgical resection) modalities as well as technical shortcomings are discussed from a clinician's point of view. (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

textabstractAs the possibilities in the treatment of diseases continue to evolve, early detection and correct diagnosis of pathological changes have become increasingly important. Pathology literally means the study (logos) of suffering (pathos), and it is essentially the discipline that bridges basic science and clinical practice by studying the structural and functional changes in diseased cells, tissues and organs. The focus of diagnostic pathology is to label the morphological and molecular changes in cells and tissues so that the appropriate therapeutic action can be taken. Histopathology is still the “gold standard” of assessing abnormal changes in tissues. In order to make a pathological diagnosis, it is required that changes in the tissues available for evaluation are representative of the disease. Tissue samples are usually obtained by biopsy procedures. Because usually only limited real-time information is available regarding the nature of the tissues to be sampled, sampling errors tend to occur. Another risk induced by lack of real-time information about the nature of diseased tissues, as compared to their normal environment, is incomplete surgical removal of tumor tissue. Therefore, the identification of biochemical characteristics of tissues which are indicative of a particular abnormality or disease and subsequent incorporation of such characteristics in sampling procedures will improve diagnostic accuracy. The same accuracy may assist surgeons in more precise targeting and removing of lesions.

Accurate targeting of diseased and healthy tissue has significantly been improved by MRI/CT-based navigation systems. Recently, intraoperative MRI navigation systems have proven to be powerful tools for the guidance of the neurosurgical operations. However, the widespread use of such systems is held back by the costs, the time consumption during operation, and the need for MR-compatible surgical devices. Raman spectroscopy is a nondestructive optical technique that enables real-time tissue identification and classification and has proved to be a powerful diagnostic tool in a large number of studies. In the present report, we have investigated the possibility of distinguishing different brain structures by using a single fiber-optic probe to collect Raman scattered light in the high-wavenumber region of the spectrum. For the Raman measurements, 7 pig brains were sliced in the coronal plain and Raman spectra were obtained of 11-19 anatomical structures. Adjacent brain structures could be distinguished based on their Raman spectra, reflecting the differences in their biochemical composition and illustrating the potential Raman spectroscopy holds as a guidance tool during neurosurgical procedures.

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.

In vivo Raman spectroscopy, using fiber-optic probes is hindered by the intense background signal, which is generated in the fused-silica fibers, in the fingerprint region of the Raman spectrum (approximately 0-2000 cm(-1)). Optical filtering is necessary to obtain tissue spectra of sufficient quality. The complexity of fiber-optic probes for fingerprint Raman spectroscopy, in combination with size constraints and flexibility requirements for in vivo use have been a major obstacle in the development of in vivo diagnostic tools based on Raman spectroscopy. A setup for remote Raman spectroscopic tissue characterization in the high-wavenumber region ( approximately 2400-3800 cm(-1)) is presented. It makes use of a single, unfiltered, optical fiber for guiding laser light to the sample and for collecting scattered light and guiding it back to a spectrometer. Such a simple configuration is possible because the fused-silica core and cladding of the fiber present almost no Raman background signal at these wavenumbers. Several commercially available optical fibers were tested with respect to Raman signal background, to determine their suitability for in vivo Raman spectroscopy measurements in the high-wavenumber region. Different fiber core, cladding, and coating materials were tested. Silica core-silica clad fibers, with an acrylate coating and a black nylon jacket, proved to be one of the best candidates. In vitro measurements on brain tissue of a 6-month-old pig were obtained with a remote high-wavenumber Raman setup. They illustrate the low background signal generated in the setup and the signal quality obtained with a collection time of 1 s.

Raman spectroscopy is a powerful diagnostic tool, enabling tissue identification and classification. Mostly, the so-called fingerprint (approximately 400-1800 cm(-1)) spectral region is used. In vivo application often requires small flexible fiber-optic probes, and is hindered by the intense Raman signal that is generated in the fused silica core of the fiber. This necessitates filtering of laser light, which is guided to the tissue, and of the scattered light collected from the tissue, leading to complex and expensive designs. Fused silica has no Raman signal in the high wave number region (2400-3800 cm(-1)). This enables the use of a single unfiltered fiber to guide laser light to the tissue and to collect scattered light in this spectral region. We show, by means of a comparison of in vitro Raman microspectroscopic maps of thin tissue sections (brain tumors, bladder), measured both in the high wave number region and in the fingerprint region, that essentially the same diagnostic information is obtained in the two wave number regions. This suggests that for many clinical applications the technological hurdle of designing and constructing suitable fiber-optic probes may be eliminated by using the high wave number region and a simple piece of standard optical fiber.

Characterization of the biochemical composition of normal bronchial tissue is a prerequisite for understanding the biochemical changes that accompany histological changes during lung cancer development. In this study, 12 Raman microspectroscopic mapping experiments are performed on frozen sections of normal bronchial tissue. Pseudocolor Raman images are constructed using principal component analysis and K-means cluster analysis. Subsequent comparison of Raman images with histologic evaluation of stained sections enables the identification of the morphologic origin (e.g., bronchial mucus, epithelium, fibrocollagenous stroma, smooth muscle, glandular tissue, and cartilage) of the spectral features. Raman spectra collected from the basal side of epithelium consistently show higher DNA contributions and lower lipid contributions when compared with superficial epithelium spectra. Spectra of bronchial mucus reveal a strong signal contribution of lipids, predominantly triolein. These spectra are almost identical to the spectra obtained from submucosal glands, which suggests that the bronchial mucus is mainly composed of gland secretions. Different parts of fibrocollagenous tissue are distinguished by differences in spectral contributions from collagen and actin/myosin. Cartilage is identified by spectral contributions of glycosaminoglycans and collagen. As demonstrated here, in situ analysis of the molecular composition of histologic structures by Raman microspectroscopic mapping creates powerful opportunities for increasing our fundamental understanding of tissue organization and function. Moreover, it provides a firm basis for further in vitro and in vivo investigations of the biochemical changes that accompany pathologic transformation of tissue.