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

N-Linked glycosylation and near-infrared spectroscopy in the diagnosis of prostate cancer

Background: Performing a prostate biopsy is the most robust and reliable way to diagnose prostate cancer (PCa), and to determine the disease grading. As little to no biochemical markers for prostate tissue exist, we explored the possibilities of tissue N-glycosylation and near-infrared spectroscopy (NIR) in PCa diagnosis. Methods: Tissue specimens from 100 patients (benign prostate hyperplasia (BPH), n = 50; and PCa, n = 50) were obtained. The fresh-frozen tissue was dispersed and a tissue N-glycosylation profile was determined. Consequently, the formalin-fixed paraffin-embedded slides were analyzed using NIR spectroscopy. A comparison was made between the benign and malignant tissue, and between the various Gleason scores. Results: A difference was observed for the tissue of N-glycosylation between the benign and malignant tissue. These differences were located in the fycosylation ratios and the total amount of bi- and tetra-antennary structures (all p < 0.0001). These differences were also present between various Gleason scores. In addition, the NIR spectra revealed changes between the benign and malignant tissue in several regions. Moreover, spectral ranges of 1055-1065 nm and 1450-1460 nm were significantly different between the Gleason scores (p = 0.0042 and p = 0.0195). Conclusions: We have demonstrated biochemical changes in the N-glycan profile of prostate tissue, which allows for the distinction between malignant and benign tissue, as well as between various Gleason scores. These changes can be correlated to the changes observed in the NIR spectra. This could possibly further improve the histological assessment of PCa diagnosis, although further method validation is needed

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

Raman spectroscopy and diffuse reflectance spectroscopy for diagnosis of human cancer and acanthosis nigricans

Cancer and diabetes are common chronic diseases in today\u27s world causing numerous deaths in adults as well as children. Most common types of cancers in adults include prostate, lung, breast, colorectal and head and neck squamous cell carcinoma, while among children; leukemia, and brain and central nervous system cancers are quite common. In each of these cases, early detection of the cancer or disease dramatically increases the chances of successful treatment. In recent years, there has been much interest in using Raman spectroscopy and diffuse reflectance spectroscopy as analytical optical spectroscopic methods for early diagnosis of diseases. Raman spectroscopy can be used to measure changes in the bio-molecular composition of a tissue specimen, and diffuse reflectance spectroscopy can measure chromophores of the skin. In this research, archived (formalin-fixed paraffin processed) tissues of head and neck squamous cell carcinoma, prostate, and pediatric tumors have been investigated using Raman spectroscopy. We have utilized statistical methods such as principal component analysis (PCA) and discriminant function analysis (DFA) to analyze the spectral output and distinguish between normal and cancerous tissues. The results show cancerous tissues can be successfully distinguished from normal tissues in three cancer types in ex vivo. However, due to loss of biochemical in the tissue processing (paraffinizing and deparaffinizing procedure), the prediction ability of the archived tissues are less compared to frozen tissues as observed in the pediatric tumor investigation. We also investigated the diagnostic capability of diffuse reflectance spectroscopy and colorimetry on a skin disease, acanthosis nigricans in vivo. The aim is to quantify and characterize the skin color change associated with acanthosis nigricans skin disease in insulin-resistant obese individuals. We observe both the instruments can be utilized to detect acanthosis nigricans with more than 87% sensitivity and 94% specificity when combined with advanced chemometric methods

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

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

Development of innovative analytical methods based on spectroscopic techniques and multivariate statistical analysis for quality control in the food and pharmaceutical fields.

The increasing demand on quality assurance and ever more stringent regulations in food and pharmaceutical fields are promoting the need for analytical techniques enabling to provide reliable and accurate results. However, traditional analytical methods are labor-intensive, time-consuming, expensive and they usually require skilled personnel for performing the analysis. For these reasons, in the last decades, quality control protocols based on the employment of spectroscopic methods have been developed for many different application fields, including pharmaceutical and food ones. Vibrational spectroscopic techniques can be an adequate alternative for acquiring both chemical and physical information related to homogenous and heterogenous matrices of interest. Moreover, the significant development of powerful data-driven methodologies allowed to develop algorithms for the optimal extraction and processing of the complex spectroscopic signals allowing to apply combined approaches for quantitative and qualitative purposes. The present Doctoral Thesis has been focused on the development of ad-hoc analytical strategies based on the application of spectroscopic techniques coupled with multivariate data analysis approaches for providing alternative analytical protocols for quality control in food and pharmaceutical sectors. Regarding applications in food sector, excitation-emission Fluorescence Spectroscopy, Near Infrared Spectroscopy (NIRS) and NIR Hyperspectral Imaging (HSI) have been tested for solving analytical issues of independent case-studies. Unsupervised approaches based on Principal Component Analysis (PCA) and Parallel Factor Analysis (PARAFAC) have been applied on fluorescence data for characterizing green tea samples, while quantitative predictive approaches as Partial Least Squares regression have been used to correlate NIR spectra with quality parameters of extra-virgin olive oil samples. HSI was applied to study dynamic chemical processes which occur during cheese ripening with the aim to map chemical and sensory changes over time. The rapid technical progress in terms of spectroscopic instrumentations has led to have more flexible portable systems suitable for performing measurements directly in the field or in a manufacturing plant. Within this scenario, NIR spectroscopy proved to be one of the most powerful Process Analytical Technologies (PAT) for monitoring and controlling complex manufacturing processes. In this thesis, two applications based on the implementation of miniaturized NIR sensors have been performed for the real-time powder blending monitoring of pharmaceutical and food formulation, respectively. The main challenges in blending monitoring are related to the assessment of the homogeneity of multicomponent formulations, which is crucial to ensure the safety and effectiveness of a solid pharmaceutical formulation or the quality of a food product. In the third chapter of this thesis, tailor made qualitative chemometric strategies for obtaining a global understanding of blending processes and to optimize the endpoint detection are presented

A study of Raman spectroscopy for the early detection and characterization of prostate cancer using blood plasma and prostate tissue biopsy.

Prostate cancer (PC) is the most common cancer in men after non-melanoma skin cancer in the United Kingdom (Cancer Research UK, 2019). Current diagnostic methods (PSA, DRE, MRI & prostate biopsy) have limitations as these are unable to distinguish between low-risk cancers that do not need active treatment from cancers which are more likely to progress. In addition, prostate biopsy is invasive with potential side effects. There is an urgent need to identify new biomarkers for early diagnosis and prognostication in PC. Raman spectroscopy (RS) is an optical technique that utilises molecular-specific, inelastic scattering of light photons to interrogate biological samples. When laser light is incident on a biological sample, the photons from the laser light can interact with the intramolecular bonds present within the sample. The Raman spectrum is a direct function of the molecular composition of the tissue, providing a molecular fingerprint of the phenotypic expression of the cells and tissues, which can give good objective information regarding the pathological state of the biological sample under interrogation. We applied a technique of drop coating deposition Raman (DCDR) spectroscopy using both blood plasma and sera to see if a more accurate prediction of the presence and progression of prostate cancer could be achieved than PSA which would allow for blood sample triage of patients into at risk groups. 100 participants were recruited for this study (100 blood plasma and 100 serum samples). Secondly, 79 prostate tissue samples (from the same cohort) were interrogated with the aid of Raman micro-spectroscopy to ascertain if Raman spectroscopy can provide molecular fingerprint that can be utilised for real time in vivo analysis. Multivariate analysis of support vector machine (SVM) learning and linear discriminant analysis (LDA) were utilised differently to test the performance accuracy of the discriminant model for distinguishing between benign and malignant mean plasma spectra. SVM gave a better performance accuracy than LDA with sensitivity and specificity of 96% and 97% respectively and an area under the curve (AUC) of 0.98 as opposed to sensitivity and specificity of 51% and 80% respectively with AUC of 0.74 using LDA. Slightly lower performance accuracy was also observed when blood serum mean spectra analysis was compared with blood plasma mean spectra analysis for both machine learning algorithms (SVM & LDA). Tissue spectral analysis on the other hand recorded an overall accuracy of 80.8% and AUC of 0.82 with the SVM algorithm compared to performance accuracy of 75% and AUC of 0.77 with LDA algorithm (better performance noted with the SVM algorithm). The small sample size of 79 prostate biopsy tissues was responsible for the low sensitivity and specificity. Therefore, the tissues were insufficient to describe all the variances in each group as well as the variability of the gold standard technique. Conclusion: Raman spectroscopy could be a potentially useful technique in the management of Prostate Cancer in areas such as tissue diagnosis, assessment of surgical margin after radical prostatectomy, detection of metastasis, Prostate Cancer screening as well as monitoring and prognosticating patients with Prostate Cancer. However, more needs to be done to validate the approaches outlined in this thesis using prospective collection of new samples to test the classification models independently with sufficient statistical power. At this stage only the fluid-based models are likely to be large enough for this validation process

Recent Advances and the Potential for Clinical Use of Autofluorescence Detection of Extra-Ophthalmic Tissues

The autofluorescence (AF) characteristics of endogenous fluorophores allow the label-free assessment and visualization of cells and tissues of the human body. While AF imaging (AFI) is well-established in ophthalmology, its clinical applications are steadily expanding to other disciplines. This review summarizes clinical advances of AF techniques published during the past decade. A systematic search of the MEDLINE database and Cochrane Library databases was performed to identify clinical AF studies in extra-ophthalmic tissues. In total, 1097 articles were identified, of which 113 from internal medicine, surgery, oral medicine, and dermatology were reviewed. While comparable technological standards exist in diabetology and cardiology, in all other disciplines, comparability between studies is limited due to the number of differing AF techniques and non-standardized imaging and data analysis. Clear evidence was found for skin AF as a surrogate for blood glucose homeostasis or cardiovascular risk grading. In thyroid surgery, foremost, less experienced surgeons may benefit from the AF-guided intraoperative separation of parathyroid from thyroid tissue. There is a growing interest in AF techniques in clinical disciplines, and promising advances have been made during the past decade. However, further research and development are mandatory to overcome the existing limitations and to maximize the clinical benefits