Modelling FTIR spectral sata with Type-I and Type-II fuzzy sets for breast cancer grading

Breast cancer is one of the most frequently occurring cancers amongst women throughout the world. After the diagnosis of the disease, monitoring its progression is important in predicting the chances of long term survival of patients. The Nottingham Prognostic Index (NPI) is one of the most common indices used to categorise the patients into different groups depending upon the severity of the disease. One of the key factors of this index is cancer grade which is determined by pathologists who examine cell samples under a microscope. This manual method has a higher chance of false classification and may lead to incorrect treatment of patients. There is a need to develop automated methods that employ advanced computational methods to help pathologists in making a decision regarding the classification of breast cancer grade. Fourier transform infra-red spectroscopy (FTIR) is one of the relatively new techniques that has been used for diagnosis of various cancer types with advanced computational methods in the literature. In this thesis we examine the use of advanced fuzzy methods with the FTIR spectral data sets to develop a model prototype that can help clinicians with breast cancer grading. Initial work is focussed on using the commonly used clustering algorithms k-means and fuzzy c-means with principal component analysis on different cancer spectral data sets to explore the complexities within them. After that, a novel model based on Type-II fuzzy logic is developed for use on a complex breast cancer FTIR spectral data set that can help clinicians classify breast cancer grades. The data set used for the purpose consists of multiple cases of each grade. We consider two types of uncertainty, one within the spectra of a single case of a grade (intra -case) and other when comparing it with other cases of same grade (inter-case). Features have been extracted in terms of interval data from various peaks and troughs. The interval data from the features has been used to create Type-I fuzzy sets for each case. After that the Type-I fuzzy sets are combined to create zSlices based General Type-II fuzzy sets for each feature for each grade. The created benchmark fuzzy sets are then used as prototypes for classification of unseen spectral data. Type-I fuzzy sets are created for unseen spectral data and then compared against the benchmark prototype Type-II fuzzy sets for each grade using a similarity measure. The best match based on the calculated similarity scores is assigned as the resultant grade. The novel model is tested on an independent spectral data set of oral cancer patients. Results indicate that the model was able to successfully construct prototype fuzzy sets for the data set, and provide in-depth information regarding the complexities of the data set as well as helping in classification of the data

Modelling FTIR spectral sata with Type-I and Type-II fuzzy sets for breast cancer grading

Breast cancer is one of the most frequently occurring cancers amongst women throughout the world. After the diagnosis of the disease, monitoring its progression is important in predicting the chances of long term survival of patients. The Nottingham Prognostic Index (NPI) is one of the most common indices used to categorise the patients into different groups depending upon the severity of the disease. One of the key factors of this index is cancer grade which is determined by pathologists who examine cell samples under a microscope. This manual method has a higher chance of false classification and may lead to incorrect treatment of patients. There is a need to develop automated methods that employ advanced computational methods to help pathologists in making a decision regarding the classification of breast cancer grade. Fourier transform infra-red spectroscopy (FTIR) is one of the relatively new techniques that has been used for diagnosis of various cancer types with advanced computational methods in the literature. In this thesis we examine the use of advanced fuzzy methods with the FTIR spectral data sets to develop a model prototype that can help clinicians with breast cancer grading. Initial work is focussed on using the commonly used clustering algorithms k-means and fuzzy c-means with principal component analysis on different cancer spectral data sets to explore the complexities within them. After that, a novel model based on Type-II fuzzy logic is developed for use on a complex breast cancer FTIR spectral data set that can help clinicians classify breast cancer grades. The data set used for the purpose consists of multiple cases of each grade. We consider two types of uncertainty, one within the spectra of a single case of a grade (intra -case) and other when comparing it with other cases of same grade (inter-case). Features have been extracted in terms of interval data from various peaks and troughs. The interval data from the features has been used to create Type-I fuzzy sets for each case. After that the Type-I fuzzy sets are combined to create zSlices based General Type-II fuzzy sets for each feature for each grade. The created benchmark fuzzy sets are then used as prototypes for classification of unseen spectral data. Type-I fuzzy sets are created for unseen spectral data and then compared against the benchmark prototype Type-II fuzzy sets for each grade using a similarity measure. The best match based on the calculated similarity scores is assigned as the resultant grade. The novel model is tested on an independent spectral data set of oral cancer patients. Results indicate that the model was able to successfully construct prototype fuzzy sets for the data set, and provide in-depth information regarding the complexities of the data set as well as helping in classification of the data

Infrared Spectroscopy of Serum Samples for Disease Diagnostics

The fundamental vibrational modes of biological constituents in the tissues and the complex body fluids coincide with optical frequencies in the infrared region. Therefore, spatially resolved molecular compositions and interaction information within the biological materials can be extracted non-destructively using IR radiation without the use of labels or probes. However, the feasibility of this technique to elucidate constituent molecular compositions and interactions within the diagnostic mediums is not well explored. This study demonstrates an application of infrared (IR) spectroscopy of sera for monitoring inflammatory bowel diseases (IBD) and various cancers. Using samples from experimental mice and human patients, the power of IR spectroscopy in structural studies of proteins and other complex band contours are explored to find spectral signatures. Two experimental models of IBD; interleukin 10 knockouts (IL10-/-) and Dextran Sodium Sulfate (DSS) induced mouse shows diagnostic accuracy with 80-100% sensitivity and specificity values. Importantly, the findings of human IBD patients’ serum also show promising results resembling our proofs-of-concept investigations of mouse models. Maximum values of sensitivity and specificity are 100% and 86%, respectively, in human samples. Similarly, in cancer studies, the EL4 mouse model of non-Hodgkin lymphoma (NHL) and a B16 mouse model of the subcutaneous melanoma are used to extract a snapshot of tumor-associated alteration in the serum. The study of both cancer-bearing mouse models in wild types (WT) and their corresponding control types emphasizes the diagnostic potential of this approach as a screening technique for the NHL and melanoma skin cancer. The breast cancer (BC) -associated protein conformational alteration in the serum samples shows the sensitivity and the specificity of identifying spectral signatures were both 90%. All in all, IR spectroscopy of serum samples accompanied by spectral analysis technique shows some promising results for disease diagnostics. The brief outlook of the fundamentals of the infrared detection technique and their applicability for the development of portable spectroscopy is also provided

A study of the molecular pathology of ductal carcinoma in situ and invasive ductal carcinoma of the breast.

The biological validity of the histopathological classification of ductal carcinoma in situ (DCIS) of the breast was evaluated in this study by correlating the three histopathological grades of DCIS to immunohistochemical expression of Ki67, p53, cerbB-2, markers of poor prognosis in invasive ductal carcinoma (IDC) and also to bcl2 and ER, markers of good prognosis in invasive breast cancer. DCIS grades correlated positively to Ki67, p53, cerbB-2 and negatively to bcl2 and ER, suggesting validity of the classification. The incidence of bax protein expression was determined immunohistochemically in DCIS and IDC. It did not correlate to histopathological grades of DCIS or IDC. The relationships of bax protein to the above mentioned biological markers were also determined in DCIS and IDC. Furthermore, the expression of bax, bcl2, Ki67, ER, p53 and cerbB-2 within DCIS grades was compared with the expression of these markers within IDC grades. The DCIS grades were determined subjectively as well as objectively by means of computer assisted image analysis with significant correlation found between subjective and objective measures. Image analysis was also used to determine percentage of positive cells per case for the nuclear stains (Ki67, ER, p53). Immunohistochemically positive p53 cases were analysed for p53 mutation by polymerase chain reaction (PCR) and subsequent DNA sequencing to compare the incidence of p53 mutation in DCIS to that of IDC. Biochemical changes within tissue may either initiate disease or occur as the result of the disease process and these changes can be studied by both Fourier transform infrared (FTIR) and FT-Raman spectroscopic techniques. FTIR and FT-Raman were employed to distinguish the DCIS and IDC grades. It has the potential to distinguish between DCIS grades, between IDC grades and also between DCIS and IDC as whole groups. The implications of the obtained data for the understanding of the molecular biology of DCIS of the breast and IDC are discussed and future investigations to further elucidate the molecular and cellular mechanisms involved are proposed

Applications of advanced spectroscopic imaging to biological tissues

The objectives of this research were to develop experimental approaches that can be applied to classify different stages of malignancy in routine formalin-fixed and paraffin-embedded tissues and to optimise the imaging approaches using novel implementations. It is hoped that the approach developed in this research may be applied for early cancer diagnostics in clinical settings in the future in order to increase cancer survival rates. Infrared spectroscopic imaging has recently shown to have great potential as a powerful method for the spatial visualization of biological tissues. This spectroscopic technique does not require sample labelling because its chemical specificity allows the differentiation of biocomponents to be achieved based on their chemical structures. Experiments were performed on 3-µm thick prostate and colon tissues that were deposited on 2 mm-calcium fluoride (CaF2) which were subsequently deparaffinised. The samples were measured under IR microscopes, in both transmission and attenuated total reflection (ATR) mode. In transmission, thermo-spectroscopic imaging of the prostate samples was first carried out to investigate the potential of thermography to complement the information obtained from IR spectral. Spectroscopic imaging has made the acquisition of chemical map of a sample possible within a short time span since this approach facilitates the simultaneous acquisition of thousands of spatially resolved infrared spectra. Spectral differences in the lipid region (3000 -2800 cm-1) were identified between cancer and benign regions within prostate tissues. The governing spectral band for classification was anti-symmetric stretching of CH2 (2921 cm-1) from PCA analysis. Nonetheless, the difference in tissue emissivity at room temperature was minimal, thus the contrast in the thermal image is low for intra-tissue classification. Besides, the thermal camera could only capture IR light between 3333-2000 cm-1. To record spectral data between 3900 - 900 cm-1 (mid-IR), Fourier transform infrared (FTIR) spectroscopic imaging was used to classify the different stages of colon disease. An automated processing framework was developed, that could achieve an overall classification accuracy of 92.7%. The processing steps included unsupervised k-means clustering of lipid bands, followed by Random Forest (RF) classification using the ‘fingerprint’ region of the data. The implementation of a correcting lens and the effect of the RMieS-EMSC correction on the tissue spectra were also investigated, which showed that computational RMieS-EMSC correction was more effective at removing spectral artefacts than the correcting lens. Furthermore, the effect of the fluctuations of surrounding humidity where the experiments were carried out was studied by using various supersaturated salt solutions. Significant peak changes of the phosphate band were observed, most notably the peak shift of the anti-symmetric stretching of phosphate bands from 1230 cm-1 to 1238 cm-1 was observed. By regulating and controlling humidity at its lowest, the classification accuracy of the colon specimens was improved without having to resort to alteration on the RF machine learning algorithm. In the ATR mode, additional apertures were introduced to the FTIR microscope, as a novel means of depth profiling the prostate tissue samples by changing the angle of incidence of IR light beam. Despite the successful attempts in capturing the qualitative information on the change of tissue morphology with the depth of penetration (dp), the spectral data were not suitable for further processing with machine learning as dp changes with wavelengths. Apart from the apertures, a ‘large-area’ germanium (Ge) crystal was introduced to enable simultaneous mapping and imaging of the colon tissue samples. Many advantages of this new implementation were observed, which included improvement in signal-to-noise ratio, uniform distribution, and no impression left on the sample. The research done in this thesis set a groundwork for clinical diagnosis and the novel implementations were transferable to studies of other samples.Open Acces

Applications of Raman micro-spectroscopy for cancer diagnostics

Bladder cancer has the highest recurrence rate of any cancer, and as with most solid organ malignancies, early diagnosis, detection, and treatment are imperative for good clinical outcomes. Cystoscopy is the cornerstone of bladder diagnostics for real-time visualization of the bladder mucosa. However, it is an uncomfortable, invasive procedure, and is not without significant risk and potential complications for the patient. Urine cytology is currently the only non-invasive diagnostic tool available for the diagnosis of bladder cancer; this method is highly sensitive for high grade tumours, but has low sensitivity for low grade tumours, which accounts for the majority of cases. Therefore, there exists a clinical need to develop and integrate a non-invasive, accurate technique to assist in the diagnosis of bladder cancer. The combination of Raman micro-spectroscopy and voided urine cytology may provide an ideal platform to replace cystoscopy for bladder cancer diagnostics. By recording Raman spectra from cells obtained from urine cytology, it is possible to analyse the spectral differences associated with the biomolecular continuum of disease progression, as well as being able to classify between different pathological subgroups. Previous studies to date have shown promising results in the application of Raman based urine cytology; however, there appears a high degree of variability across experimental protocols, which is believed to have hindered the advancement of this technique into the clinic. This thesis involves the design and building of a confocal Raman micro-spectrometer to be utilised for the analysis of urine cytology samples, with a key emphasis on the translation of Raman based urine cytology into the clinic. In order to achieve this, a range of traditional protocols and consumables are systematically examined in terms of their compatibility with Raman micro-spectroscopy, as well as comparing the differences between Raman micro-spectroscopy and another form of vibrational spectroscopy for bladder and prostate cancer diagnostics. Although no patient urine cytology samples are used in this thesis, simulated samples are generated using bladder and prostate cell lines along with commercially available synthetic urine. Additional experimentation is provided in order to investigate the impact of hypoxia and exosomal communication on cellular biochemistry

Laser Based Mid-Infrared Spectroscopic Imaging – Exploring a Novel Method for Application in Cancer Diagnosis

A number of biomedical studies have shown that mid-infrared spectroscopic images can provide both morphological and biochemical information that can be used for the diagnosis of cancer. Whilst this technique has shown great potential it has yet to be employed by the medical profession. By replacing the conventional broadband thermal source employed in modern FTIR spectrometers with high-brightness, broadly tuneable laser based sources (QCLs and OPGs) we aim to solve one of the main obstacles to the transfer of this technology to the medical arena; namely poor signal to noise ratios at high spatial resolutions and short image acquisition times. In this thesis we take the first steps towards developing the optimum experimental configuration, the data processing algorithms and the spectroscopic image contrast and enhancement methods needed to utilise these high intensity laser based sources. We show that a QCL system is better suited to providing numerical absorbance values (biochemical information) than an OPG system primarily due to the QCL pulse stability. We also discuss practical protocols for the application of spectroscopic imaging to cancer diagnosis and present our spectroscopic imaging results from our laser based spectroscopic imaging experiments of oesophageal cancer tissue

Vibrational Spectroscopy for Pathology from Biochemical Analysis to Diagnostic Tool

Cervical cancer is the second most common cancer in women worldwide with 80% of cases arising in the developing world. The mortality associated with cervical cancer can be reduced if this disease is detected at the early stages of development or at the pre-malignant state (cervical intra-epithelial neoplasia, CIN). The aim of this study was to investigate the potential of Raman spectroscopy as a diagnostic tool to detect biochemical changes accompanying cervical cancer progression. Raman spectra were acquired from proteins, nucleic acids, lipids and carbohydrates in order to gain an insight into the biochemical composition of cells and tissues. Spectra were also obtained from histological samples of normal, CIN and invasive carcinoma tissue from 40 patients. Multivariate analysis of the spectra was carried out to develop a classification model to discriminate normal from abnormal tissue. The results show that Raman spectroscopy displays a high sensitivity to biochemical changes in tissue during disease progression resulting in an exceptional prediction accuracy when discriminating between normal cervical tissue, invasive carcinoma and cervical intra-epithelial neoplasia (CIN). Raman spectroscopy shows enormous clinical potential as a rapid non invasive diagnostic tool for cervical and other cancers