Paul Crow

Bio: Paul Crow is an academic researcher from Cranfield University. The author has contributed to research in topics: Raman spectroscopy & Prostate. The author has an hindex of 9, co-authored 12 publications receiving 1846 citations.

Raman spectroscopy for identification of epithelial cancers

Abstract: 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, and 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.

Near‐infrared Raman spectroscopy for the classification of epithelial pre‐cancers and cancers

Abstract: 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 cross-validation. 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. Copyright © 2002 John Wiley & Sons, Ltd.

The use of Raman spectroscopy to differentiate between different prostatic adenocarcinoma cell lines

Abstract: 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.

The use of Raman spectroscopy to identify and grade prostatic adenocarcinoma in vitro

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.

Raman spectroscopy of biological tissues

Abstract: This article reviews some of the recent advances in Raman spectroscopy, in areas related to natural tissues and cell biology. It summarizes some of the most widely used peak frequencies and their assignments. The aim of this study is to prepare a database of molecular fingerprints, which will help researchers in defining the chemical structure of the biological tissues introducing most of the important peaks present in the natural tissues. In spite of applying different methods, there seems to be a considerable similarity in defining the peaks of identical areas of the spectra. As a result, it is believed that preparing a unique collection of the frequencies encountered in Raman spectroscopic studies can lead to significant improvements both in the quantity and quality of spectral data and their outcomes. This article is the first review of its kind to provide a precise database on the most important Raman characteristic peak frequencies for researchers aiming to analyze natural tissues by Raman ...

Fourier Transform Infrared (FTIR) Spectroscopy of Biological Tissues

Abstract: This article reviews some of the recent advances on FTIR spectroscopy in areas related to natural tissues and cell biology. It is the second review publication resulting from a detailed study on the applications of spectroscopic methods in biological studies and summarizes some of the most widely used peak frequencies and their assignments. The aim of these studies is to prepare a database of molecular fingerprints, which will help researchers in defining the chemical structure of the biological tissues introducing most of the important peaks present in the natural tissues. In spite of applying different methods, there seems to be a considerable similarity in defining the peaks of identical areas of the FTIR spectra. As a result, it is believed that preparing a unique collection of the frequencies encountered in FTIR spectroscopic studies can lead to significant improvements both in the quantity and quality of research and their outcomes. This article is the first review of its kind that provides...

Using Raman spectroscopy to characterize biological materials

Abstract: Raman spectroscopy can be used to measure the chemical composition of a sample, which can in turn be used to extract biological information. Many materials have characteristic Raman spectra, which means that Raman spectroscopy has proven to be an effective analytical approach in geology, semiconductor, materials and polymer science fields. The application of Raman spectroscopy and microscopy within biology is rapidly increasing because it can provide chemical and compositional information, but it does not typically suffer from interference from water molecules. Analysis does not conventionally require extensive sample preparation; biochemical and structural information can usually be obtained without labeling. In this protocol, we aim to standardize and bring together multiple experimental approaches from key leaders in the field for obtaining Raman spectra using a microspectrometer. As examples of the range of biological samples that can be analyzed, we provide instructions for acquiring Raman spectra, maps and images for fresh plant tissue, formalin-fixed and fresh frozen mammalian tissue, fixed cells and biofluids. We explore a robust approach for sample preparation, instrumentation, acquisition parameters and data processing. By using this approach, we expect that a typical Raman experiment can be performed by a nonspecialist user to generate high-quality data for biological materials analysis.

Raman spectroscopy of proteins: a review

Abstract: In this work, 26 proteins of different structure, function and properties are investigated by Raman spectroscopy with 488, 532 and 1064 nm laser lines. The excitation lines were chosen in NIR and Vis range as the most common and to show the difference due to normal and resonance effect, sometimes accompanied by the fluorescence. The selected proteins were divided, according to the Structural Classification of Proteins, into four classes according to their secondary structure, i.e. α-helical (α), β-sheet (β), mixed structures (α/β, α + β, s) and others. For all compounds, FT-Raman and two Vis spectra are presented along with the detailed band assignment. To the best of our knowledge, this is the first review showing the potential of Raman spectroscopy for the measurement and analysis of such a large collection of individual proteins. This work can serve as a comprehensive vibrational spectra library, based on our and previous Raman measurements. Copyright © 2013 John Wiley & Sons, Ltd.

Raman spectroscopy for identification of epithelial cancers

Abstract: 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, and 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.