A Study of Raman Spectroscopy as a Clinical Diagnostic Tool for the Detection of Lynch Syndrome/Hereditary NonPolyposis Colorectal Cancer (HNPCC)

Lynch syndrome also known as hereditary non-polyposis colorectal cancer (HNPCC) is a highly penetrant hereditary form of colorectal cancer that accounts for approximately 3% of all cases. It is caused by mutations in DNA mismatch repair resulting in accelerated adenoma to carcinoma progression. The current clinical guidelines used to identify Lynch Syndrome (LS) are known to be too stringent resulting in overall underdiagnoses. Raman spectroscopy is a powerful analytical tool used to probe the molecular vibrations of a sample to provide a unique chemical fingerprint. The potential of using Raman as a diagnostic tool for discriminating LS from sporadic adenocarcinoma is explored within this thesis. A number of experimental parameters were initially optimized for use with formalin fixed paraffin embedded colonic tissue (FFPE). This has resulted in the development of a novel cost-effective backing substrate shown to be superior to the conventionally used calcium fluoride (CaF2). This substrate is a form of silanized super mirror stainless steel that was found to have a much lower Raman background, enhanced Raman signal and complete paraffin removal from FFPE tissues. Performance of the novel substrate was compared against CaF2 by acquiring large high resolution Raman maps from FFPE rat and human colonic tissue. All of the major histological features were discerned from steel mounted tissue with the benefit of clear lipid signals without paraffin obstruction. Biochemical signals were comparable to those obtained on CaF2 with no detectable irregularities. By using principal component analysis to reduce the dimensionality of the dataset it was then possible to use linear discriminant analysis to build a classification model for the discrimination of normal colonic tissue (n=10) from two pathological groups: LS (n=10) and sporadic adenocarcinoma (n=10). Using leaveone-map-out cross-validation of the model classifier has shown that LS was predicted with a sensitivity of 63% and a specificity of 89% - values that are competitive with classification techniques applied routinely in clinical practice

Feature extraction for the analysis of colon status from the endoscopic images

BACKGROUND: Extracting features from the colonoscopic images is essential for getting the features, which characterizes the properties of the colon. The features are employed in the computer-assisted diagnosis of colonoscopic images to assist the physician in detecting the colon status. METHODS: Endoscopic images contain rich texture and color information. Novel schemes are developed to extract new texture features from the texture spectra in the chromatic and achromatic domains, and color features for a selected region of interest from each color component histogram of the colonoscopic images. These features are reduced in size using Principal Component Analysis (PCA) and are evaluated using Backpropagation Neural Network (BPNN). RESULTS: Features extracted from endoscopic images were tested to classify the colon status as either normal or abnormal. The classification results obtained show the features' capability for classifying the colon's status. The average classification accuracy, which is using hybrid of the texture and color features with PCA (τ = 1%), is 97.72%. It is higher than the average classification accuracy using only texture (96.96%, τ = 1%) or color (90.52%, τ = 1%) features. CONCLUSION: In conclusion, novel methods for extracting new texture- and color-based features from the colonoscopic images to classify the colon status have been proposed. A new approach using PCA in conjunction with BPNN for evaluating the features has also been proposed. The preliminary test results support the feasibility of the proposed method

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

An Investigation of the Diagnostic Potential of Autofluorescence Lifetime Spectroscopy and Imaging for Label-Free Contrast of Disease

The work presented in this thesis aimed to study the application of fluorescence lifetime spectroscopy (FLS) and fluorescence lifetime imaging microscopy (FLIM) to investigate their potential for diagnostic contrast of diseased tissue with a particular emphasis on autofluorescence (AF) measurements of gastrointestinal (GI) disease. Initially, an ex vivo study utilising confocal FLIM was undertaken with 420 nm excitation to characterise the fluorescence lifetime (FL) images obtained from 71 GI samples from 35 patients. A significant decrease in FL was observed between normal colon and polyps (p = 0.024), and normal colon and inflammatory bowel disease (IBD) (p = 0.015). Confocal FLIM was also performed on 23 bladder samples. A longer, although not significant, FL for cancer was observed, in paired specimens (n = 5) instilled with a photosensitizer. The first in vivo study was a clinical investigation of skin cancer using a fibre-optic FL spectrofluorometer and involved the interrogation of 27 lesions from 25 patients. A significant decrease in the FL of basal cell carcinomas compared to healthy tissue was observed (p = 0.002) with 445 nm excitation. A novel clinically viable FLS fibre-optic probe was then applied ex vivo to measure 60 samples collected from 23 patients. In a paired analysis of neoplastic polyps and normal colon obtained from the same region of the colon in the same patient (n = 12), a significant decrease in FL was observed (p = 0.021) with 435 nm excitation. In contrast, with 375 nm excitation, the mean FL of IBD specimens (n = 4) was found to be longer than that of normal tissue, although not statistically significant. Finally, the FLS system was applied in vivo in 17 patients, with initial data indicating that 435 nm excitation results in AF lifetimes that are broadly consistent with ex vivo studies, although no diagnostically significant differences were observed in the signals obtained in vivo.Open Acces

Multimodal Multispectral Optical Endoscopic Imaging for Biomedical Applications

Optical imaging is an emerging field of clinical diagnostics that can address the growing medical need for early cancer detection and diagnosis. Various human cancers are amenable to better prognosis and patient survival if found and treated during early disease onset. Besides providing wide-field, macroscopic diagnostic information similar to existing clinical imaging techniques, optical imaging modalities have the added advantage of microscopic, high resolution cellular-level imaging from in vivo tissues in real time. This comprehensive imaging approach to cancer detection and the possibility of performing an ‘optical biopsy’ without tissue removal has led to growing interest in the field with numerous techniques under investigation. Three optical techniques are discussed in this thesis, namely multispectral fluorescence imaging (MFI), hyperspectral reflectance imaging (HRI) and fluorescence confocal endomicroscopy (FCE). MFI and HRI are novel endoscopic imaging-based extensions of single point detection techniques, such as laser induced fluorescence spectroscopy and diffuse reflectance spectroscopy. This results in the acquisition of spectral data in an intuitive imaging format that allows for quantitative evaluation of tissue disease states. We demonstrate MFI and HRI on fluorophores, tissue phantoms and ex vivo tissues and present the results as an RGB colour image for more intuitive assessment. This follows dimensionality reduction of the acquired spectral data with a fixed-reference isomap diagnostic algorithm to extract only the most meaningful data parameters. FCE is a probe-based point imaging technique offering confocal detection in vivo with almost histology-grade images. We perform FCE imaging on chemotherapy-treated in vitro human ovarian cancer cells, ex vivo human cancer tissues and photosensitiser-treated in vivo murine tumours to show the enhanced detection capabilities of the technique. Finally, the three modalities are applied in combination to demonstrate an optical viewfinder approach as a possible minimally-invasive imaging method for early cancer detection and diagnosis

Morphological Features of Dysplastic Progression in Epithelium: Quantification of Cytological, Microendoscopic, and Second Harmonic Generation Images

Advances in imaging technology have led to a variety of available clinical and investigational systems. In this collection of studies, we tested the relevance of morphological image feature quantification on several imaging systems and epithelial tissues. Quantification carries the benefit of creating numerical baselines and thresholds of healthy and abnormal tissues, to potentially aid clinicians in determining a diagnosis, as well as providing researchers with standardized, unbiased results for future dissemination and comparison. Morphological image features in proflavine stained oral cells were compared qualitatively to traditional Giemsa stained cells, and then we quantified the nuclear to cytoplasm ratio. We determined that quantification of proflavine stained cells matched our hypothesis, as the nuclei in oral carcinoma cells were significantly larger than healthy oral cells. Proflavine has been used in conjunction with translational fluorescence microendoscopy of the gastrointestinal tract, and we demonstrated the ability of our custom algorithm to accurately (up to 85% sensitivity) extract colorectal crypt area and circularity data, which could minimize the burden of training on clinicians. In addition, we proposed fluorescein as an alternative fluorescent dye, providing comparable crypt area and circularity information. In order to investigate the morphological changes of crypts via the supporting collagen structures, we adapted our quantification algorithm to analyze crypt area, circularity, and an additional shape parameter in second harmonic generation images of label-free freshly resected murine epithelium. Murine models of colorectal cancer (CRC) were imaged at early and late stages of tumor progression, and we noted significant differences between the Control groups and the late cancer stages, with some differences between early and late stages of CRC progression

The role of Raman Spectroscopy in the detection of dysplasia in Barrett’s oesophagus

Introduction The incidence of oesophageal adenocarcinoma is increasing. Although improvements have been seen, the overall 5 year survival rate remains poor, at 15.1%. As with other cancers, the survival rate is highest when the disease is confined to the oesophagus. Barrett’s oesophagus is an acquired condition, characterised by the replacement of the normal distal squamous epithelial lining of the oesophagus with columnar epithelium. Oesophageal adenocarcinoma develops, in most instances, along a pathway of increasing dysplasia in the sections of Barrett’s oesophagus. If dysplasia can be diagnosed accurately, then this would permit treatment prior to the development of adenocarcinoma. Methods Samples of Barrett’s oesophagus with varying degrees of dysplasia and adenocarcinoma were measured with Raman point and mapping spectroscopy. Analysis was performed using Matlab®. Results Samples of squamous epithelia, Barrett’s oesophagus without dysplasia, with low-grade dysplasia, with high-grade dysplasia and oesophageal adenocarcinoma were measured and analysed. 2078 point spectra measurements and 117 map regions were analysed. Raman point spectra measurements and Raman mapping differentiated samples without dysplasia from those with dysplasia, and differentiated samples of low-grade dysplasia from those of high-grade dysplasia and adenocarcinoma. The specificity and sensitivity were, however, low. Conclusion This research has illustrated the ability of Raman spectroscopy to discern samples of Barrett’s oesophagus with low-grade dysplasia from those with higher grades of dysplasia. This capability could be utilised clinically with in- vivo measurements to identify the areas requiring detailed surveillance and biopsies. The majority of patients with Barrett’s oesophagus and low-grade dysplasia will never progress to adenocarcinoma. There is currently no means, either via histopathology or via a biomarker, to identify the minority who will develop high- grade dysplasia or adenocarcinoma. Raman spectroscopy may have the ability to do this and I believe this is the path that this technology should pursue

Distributed computing methodology for training neural networks in an image-guided diagnostic application

Distributed computing is a process through which a set of computers connected by a network is used collectively to solve a single problem. In this paper, we propose a distributed computing methodology for training neural networks for the detection of lesions in colonoscopy. Our approach is based on partitioning the training set across multiple processors using a parallel virtual machine. In this way, interconnected computers of varied architectures can be used for the distributed evaluation of the error function and gradient values, and, thus, training neural networks utilizing various learning methods. The proposed methodology has large granularity and low synchronization, and has been implemented and tested. Our results indicate that the parallel virtual machine implementation of the training algorithms developed leads to considerable speedup, especially when large network architectures and training sets are used

Development of polarization-resolved optical scanning microscopy imaging techniques to study biomolecular organizations

Light, as electromagnetic radiation, conveys energy through space and time via fluctuations in electric and magnetic fields. This thesis explores the interaction of light and biological structures through polarization-resolved imaging techniques. Light microscopy, and polarization analysis enable the examination of biological entities. Biological function often centers on chromatin, the genetic material composed of DNA wrapped around histone proteins within cell nuclei. This structure's chiral nature gives rise to interactions with polarized light. This research encompasses three main aspects. Firstly, an existing multimodal Circular Intensity Differential Scattering (CIDS) and fluorescence microscopy are upgraded into an open configuration to be integrated with other modalities. Secondly, a novel cell classification method employing CIDS and a phasor representation is introduced. Thirdly, polarization analysis of fluorescence emission is employed for pathological investigations. Accordingly, the thesis is organized into three chapters. Chapter 1 lays the theoretical foundation for light propagation and polarization, outlining the Jones and Stokes-Mueller formalisms. The interaction between light and optical elements, transmission, and reflection processes are discussed. Polarized light's ability to reveal image contrast in polarizing microscopes, linear and nonlinear polarization-resolved microscopy, and Mueller matrix microscopy as a comprehensive technique for studying biological structures are detailed. Chapter 2 focuses on CIDS, a label-free light scattering method, including a single point angular spectroscopy mode and scanning microscopy imaging. A significant upgrade of the setup is achieved, incorporating automation, calibration, and statistical analysis routines. An intuitive phasor approach is proposed, enabling image segmentation, cell discrimination, and enhanced interpretation of polarimetric contrast. As a result, image processing programs have been developed to provide automated measurements using polarization-resolved laser scanning microscopy imaging integrated with confocal fluorescence microscopy of cells and chromatin inside cell nuclei, including the use of new types of samples such as progeria cells. Chapter 3 applies a polarization-resolved two-photon excitation fluorescence (2PEF) microscopy to study multicellular cancerous cells. A homemade 2PEF microscope is developed for colon cancer cell analysis. The integration of polarization and fluorescence techniques leads to a comprehensive understanding of the molecular orientation within samples, particularly useful for cancer diagnosis. Overall, this thesis presents an exploration of polarization-resolved imaging techniques for studying biological structures, encompassing theory, experimental enhancements, innovative methodologies, and practical applications