There is a real need for improvements in cancer detection. Significant problems are encountered when utilising the gold standard of excisional biopsy combined with histopathology. This can include missed lesions, perforation and high levels of inter- and intra-observer discrepancies. The clinical requirements for an objective, non-invasive real time probe for accurate and repeatable measurement of tissue pathological state are overwhelming. This study has evaluated the potential for Raman spectroscopy to achieve this goal. The technique measures the molecular specific inelastic scattering of laser light within tissue, thus enabling the analysis of biochemical changes that precede and accompany disease processes. Initial work has been carried out to optimise a commercially available Raman microspectrometer for tissue measurements; to target potential malignancies with a clinical need for diagnostic improvements (oesophagus. colon, breast, andd prostate) and to build and test spectral libraries and prediction algorithms for tissue types and pathologies. This study has followed rigorous sample collection protocols and histopathological analysis using a board of expert pathologists. Only the data from samples with full agreement of a homogeneous pathology have been used to construct a training data set of Raman spectra. Measurements of tissue specimens from the full spectrum of different pathological groups found in each tissue have been made. Diagnostic predictive models have been constructed and optimised using multivariate analysis techniques. They have been tested using cross-validation or leave-one-out and demonstrated high levels of discrimination between pathology groups (greater than 90% sensitivity and specificity for all tissues). However larger sample numbers are required for further evaluation. The discussions outline the likely work required for successful implementation of in vivo Raman detection of early malignancies.

The use of near-infrared Raman spectroscopy to interrogate epithelial tissue biochemistry and hence distinguish between normal and abnormal tissues was investigated. Six different epithelial tissues from the larynx, tonsil, oesophagus, stomach, bladder and prostate were measured. Spectral diagnostic models were constructed using multivariate statistical analysis of the spectra to classify samples of epithelial cancers and pre-cancers. Tissues were selected for clinical significance and to include those which develop into carcinoma from squamous, transitional or columnar epithelial cells. Rigorous histopathological protocols were followed and mixed pathology tissue samples were discarded from the study. Principal component fed linear discriminant models demonstrated excellent group separation, when tested by crossvalidation. Larynx samples, with squamous epithelial tissue, were separated into three distinct groups with sensitivities ranging from 86 to 90% and specificities from 87 to 95%. Bladder specimens, containing transitional epithelial tissue, were separated into five distinct groups with sensitivities of between 78 and 98% and specificities between 96 and 99%. Oesophagus tissue can contain both squamous and columnar cell carcinomas. A three group model discriminated the columnar cell pathological groups with sensitivities of 84-97% and specificities of 93-99%, and an eight group model combining both columnar and squamous tissues in the oesophagus was able to discriminate pathologies with sensitivities of 73-100% and specificities of 92-100%. It is likely that any overlap between pathology group predictions will have been due to a combination of the difficulty in histologically distinguishing between pre-cancerous states and the fact that there is no biochemical boundary from one pathological group to the next, i.e. there is believed to be a continuum of progression from the normal to the diseased state.

Raman spectroscopy (RS) is an optical technique that provides an objective method of pathological diagnosis based on the molecular composition of tissue. Studies have shown that the technique can accurately identify and grade prostatic adenocarcinoma (CaP) in vitro. This study aimed to determine whether RS was able to differentiate between CaP cell lines of varying degrees of biological aggressiveness. Raman spectra were measured from two well-differentiated, androgen-sensitive cell lines (LNCaP and PCa 2b) and two poorly differentiated, androgen-insensitive cell lines (DU145 and PC 3). Principal component analysis was used to study the molecular differences that exist between cell lines and, in conjunction with linear discriminant analysis, was applied to 200 spectra to construct a diagnostic algorithm capable of differentiating between the different cell lines. The algorithm was able to identify the cell line of each individual cell with an overall sensitivity of 98% and a specificity of 99%. The results further demonstrate the ability of RS to differentiate between CaP samples of varying biological aggressiveness. RS shows promise for application in the diagnosis and grading of CaP in clinical practise as well as providing molecular information on CaP samples in a research setting.

No abstract

Raman spectroscopy is an optical technique, which provides a measure of the molecular composition of tissue. Raman spectra were recorded in vitro from both benign and malignant prostate biopsies, and used to construct a diagnostic algorithm. The algorithm was able to correctly identify each pathological group studied with an overall accuracy of 89%. The technique shows promise as a method for objectively grading prostate cancer.