A novel image analysis approach to characterise the effects of dietary components on intestinal morphology and immune system in Atlantic salmon

The intestinal tract of salmonids provides a dynamic interface that not only mediates nutrient uptake but also functions as the first line of defence against ingested pathogens. Exposure of the immune system to beneficial microorganisms and different dietary immunostimulants via the intestine has been shown to prime the immune system and help in the development of immune competence. Furthermore, the morphology and function of teleostean intestines are known to respond to feed components and to ingested and resident bacterial communities. Histological appraisal is still generally considered to be the gold standard for sensitive assessment of the effects of such dietary modulation. The aim of the present study was to improve understanding of salmonid intestinal function, structure and dynamics and to use the knowledge gained to develop a model for analysis, which would allow intestinal health to be assessed with respect to different intestinal communities and feed components. Virtual histology, the process of assessing digital images of histological slides, is gaining momentum as an approach to supplement traditional histological evaluation methodologies and at the same time, image analysis of digitised histological sections provides a practical means for quantifiable assessment of structural and functional changes in tissues, being both objective and reproducible. This project focused on the development of a rapid, practical analytical methodology based on advanced image analysis, that was able to measure and characterise a range of features of the intestinal histology of Atlantic salmon in a quantitative manner. In the first research chapter, the development of a novel histological assessment system based upon advanced image analysis was described, this being developed with the help of a soybean feed model known to induce enteropathy in Atlantic salmon. This tool targeted the evaluation of the extent of morphological changes occurring in the distal intestine of Atlantic salmon following dietary modulation. The final analytical methodology arrived at, could be conducted with minimal user-interaction, allowing rapid and objective assessment of 12 continuous variables per histological frame analysed. The processing time required for each histological frame was roughly 20-25 min, which greatly improved the efficiency of conducting such a quantitative assessment with respect to the time taken for a subjective semi-quantitative alternative approach. Significant agreement between the fully automated and the manual morphometric image segmentation was achieved, however, the strength of this quantitative approach was enhanced by the employment of interactive procedures, which enabled the operator / observer to rectify preceding automated segmentation steps, and account for the specimen’s variations. Results indicated that image analysis provided a viable alternative to a pathologist’s manual scoring, being more practical and time-efficient. In the second research chapter, feeding Atlantic salmon a high inclusion level of unrefined SBM (25 %) produced an inflammatory response in the distal intestine as previously described by other authors. The model feed trial successfully generated differentiable states, although these were not, for the most part, systemically differentiable through the majority of standard immunological procedures used, being only detectable morphologically. Quantitation of morphometric parameters associated with histological sections using the newly developed image analysis tool successfully allowed identification of major morphological changes. Image analysis was thus shown to provide a powerful tool for describing the histomorphological structure of Atlantic salmon distal intestine. In turn, the semi-automated image analysis methods were able to distinguish normal intestinal mucosa from those affected by enteritis. While individual parameters were less discriminatory, use of multivariate techniques allowed better discrimination of states and is likely to prove the most productive approach in further studies. Work described in the third research chapter sought to validate the semi-automated image analysis system to establish that it was measuring the parameters it was purported to be measuring, and to provide reassurance that it could reliably measure pre-determined features. This study, using the same sections for semi-quantitative and quantitative analyses, demonstrated that the quantitative indices performed well when compared to analogous semi-quantitative descriptive parameters of assessment for enteritis prognosis. The excellent reproducibility and accuracy performance levels indicated that the image analysis system was a useful and reliable morphometric method for the quantification of SB-induced enteritis in salmon. Other characteristics such as rapidity, simplicity and adaptability favour this method for image analysis, and are particularly useful where less experienced interpreters are performing the analysis. The work described in the fourth research chapter characterised changes in the morphology of the intestinal epithelial cells occurring as a result of dietary modulation and aspects of inflammatory infiltration, using a selected panel of enzyme and IHC markers. To accomplish this, image analysis techniques were used to evaluate and systematically optimise a quantitative immunolabelling assessment protocol. Digital computer-assisted quantification of labelling for cell proliferation and regeneration; programmed cell death or apoptosis; EGCs and t-cell like infiltrates; mobilisation of stress-related protein regenerative processes and facilitation of nutrient uptake and ion transport provided encouraging results. Through the description of the intestinal cellular responses at a molecular level, such IHC expression profiling further characterised the inflammatory reaction generated by the enteropathic diet. In addition, a number of potential diagnostic parameters were described for fish intestinal health e.g. the relative levels of antigenicity and the spatial distribution of antigens in tissues. Work described in the final research chapter focused on detailed characterisation of intestinal MCs / EGCs in order to try to elucidate their functional role in the intestinal immune responses. Through an understanding of their distribution, composition and ultrastructure, the intention was to better characterise these cells and their functional properties. The general morphology, histochemical characteristics and tissue distribution of these cells were explored in detail using histochemical, IHC and immunogold staining / labelling, visualised using light, confocal and TEM microscopy. Despite these extensive investigations, their physiological function and the content of their granules still remain somewhat obscure, although a role as immunodulatory cells reacting to various exogeneous signals through a finely regulated process and comparable to that causing the degranulation of mammalian MCs is suggested. The histochemical staining properties demonstrated for salmonid MCs / EGCs seem to resemble those of mammalian mucosal mast cells, with both acidophilic and basophilic components in their granules, and a granule content containing neuromodulator / neurotransmitter-peptides such as serotonin, met-enkephalin and substance-p. Consequently, distinguishable bio-chromogenic markers have been identified that are of utility in generating a discriminatory profile for image analysis of such cells

A deep-learning approach to aid in diagnosing Barrett’s oesophagus related dysplasia

Barrett's oesophagus is the only known precursor to oesophagus carcinoma. Histologically, it is defined as a condition of columnar cells replacing the standard squamous lining. Those altered cells are prone to cytological and architectural abnormalities, known as dysplasia. The dysplastic degree varies from low to high grade and can evolve into invasive carcinoma or adenocarcinoma. Thus, detecting high-grade and intramucosal carcinoma during the surveillance of Barrett's oesophagus patients is vital so they can be treated by surgical resection. Unfortunately, the achieved interobserver agreement for grading dysplasia among pathologists is only fair to moderate. Nowadays, grading Barrett's dysplasia is limited to visual examination by pathologists for glass or virtual slides. This work aims to diagnose different grades of dysplasia in Barrett’s oesophagus, particularly high-grade dysplasia, from virtual histopathological slides of oesophagus tissue. In the first approach, virtual slides were analysed at a low magnification to detect regions of interest and predict the grade of dysplasia based on the analysis of the virtual slides at 10X magnification. Transfer learning was employed to partially fine-tune two deep-learning networks using healthy and Barrett’s oesophagus tissue. Then, the two networks were connected. The proposed model achieved 0.57 sensitivity, 0.79 specificity and moderate agreement with a pathologist. On the contrary, the second approach processed the slides at a higher magnification (40X magnification). It adapted novelty detection and local outlier factor alongside transfer learning to solve the multiple instances learning problem. It increased the performance of the diagnosis to 0.84 sensitivity and 0.92 specificity, and the interobserver agreement reached a substantial level. Finally, the last approach mimics the pathologists’ procedure to diagnose dysplasia, relying on both magnifications. Thus, their behaviours during the assessment were analysed. As a result, it was found that employing a multi-scale approach to detect dysplastic tissue using a low magnification level (10X magnification) and grade dysplasia at a higher level (40X magnification). The proposed computer-aided diagnosis system was built using networks from the first two approaches. It scored 0.90 sensitivity, 0.94 specificity and a substantial agreement with the pathologist and a moderate agreement with the other expert

Detection and classification of gastrointestinal cancer and other pathologies through quantitative analysis of optical coherence tomography data and goniophotometry

The changes in light interaction between healthy and diseased tissues have been investigated as a potential diagnostic application. Here we attempt to differentiate between healthy and pathological gastrointestinal tissues using quantitative analysis of optical coherence tomography (OCT) data and goniophotometry. A goniophotometer was constructed and calibrated using titanium oxide and microsphere phantoms. Measurements were carried out on human gastrointestinal tissue sections collected using the methodology described below. The anisotropy factor g was extracted from the scattering curves by fitting the Henyey-Greenstein function. Measurements on human samples were in the forward scattering range with g 0.6-0.7, in agreement with the literature. Optical coherence tomography imaging was carried out on gastrointestinal tissues collected from patients undergoing elective surgery or endoscopy at St. Mary’s Hospital, London. In total 146 patients were included. Data was processed using gradient analysis of signal attenuation and morphological analysis with kNN classification. Results were correlated with histological diagnoses. Gradient analysis results were statistically significant across most categories, showing particularly good differences in the gradient distributions between healthy and diseased oesophageal tissues. Morphological analysis and kNN classification produced sensitivity and specificity values for healthy oesophagus and cancer in surgical specimens reaching 100% / 97.87% and 99.99% / 99.91% respectively and high accuracy in detecting Barrett's oesophagus in endoscopic specimens, with sensitivity and specificity values of 99.80% and 99.02%. Results in rectal tissue where also noteworthy, with detection of dysplasia reaching a sensitivity and specificity of 99.55% / 96.01%. Despite limitations in our work, we have shown that the detection of gastrointestinal pathologies using quantitative analysis of OCT data is a promising technique with good ex vivo results. Transferring the methodology to the in vivo domain holds a lot of potential as a future quick and reliable diagnostic technique.Open Acces

Quantitative chemical imaging: A top-down systems pathology approach to predict colon cancer patient survival

Colon cancer is the second deadliest cancer, affecting the quality of life in older patients. Prognosis is useful in developing an informed disease management strategy, which can improve mortality as well as patient comfort. Morphometric assessment provides diagnosis, grade, and stage information. However, it is subjective, requires multi-step sample processing, and annotations by pathologists. In addition, morphometric techniques offer minimal molecular information that can be crucial in determining prognosis. The interaction of the tumor with its surrounding stroma, comprised of several biomolecular factors and cells is a critical determinant of the behavior of the disease. To evaluate this interaction objectively, we need biomolecular profiling in spatially specific context. In this work, we achieved this by analyzing tissue microarrays using infrared spectroscopic imaging. We developed supervised classification algorithms that were used to reliably segment colon tissue into histological components, including differentiation of normal and desmoplastic stroma. Thus, infrared spectroscopic imaging enabled us to map the stromal changes around the tumor. This supervised classification achieved >0.90 area under the curve of the receiver operating characteristic curve for pixel level classification. Using these maps, we sought to define evaluation criteria to assess the segmented colon images to determine prognosis. We measured the interaction of tumor with the surrounding stroma containing activated fibroblast in the form of mathematical functions that took into account the structure of tumor and the prevalence of reactive stroma. Using these functions, we found that the interaction effect of large tumor size in the presence of a high density of activated fibroblasts provided patients with worse outcome. The overall 6-year probability of survival in patient groups that were classified as “low-risk” was 0.73 whereas in patients that were “high-risk” was 0.54 at p-value <0.0003. Remarkably, the risk score defined in this work was independent of patient risk assessed by stage and grade of the tumor. Thus, objective evaluation of prognosis, which adds to the current clinical regimen, was achieved by a completely automated analysis of unstained patient tissue to determine the risk of 6-year death. In this work, we demonstrate that quantitative chemical imaging using infrared spectroscopic imaging is an effective method to measure tumor-tumor microenvironment interactions. As a top-down systems pathology approach, our work integrated morphometry based spatial constraints and biochemistry based stromal changes to identify markers that gave us mechanistic insights into the tumor behavior. Our work shows that while the tumor microenvironment changes are prognostic, an interaction model that takes into account both the extent of microenvironment modifications, as well as the tumor morphology, is a better predictor of prognosis. Finally, we also developed automated tumor grade determination using deep learning based infrared image analysis. Thus, the computational models developed in this work provide an objective, processing-free and automated way to predict tumor behavior

The application of artificial intelligence and image analysis to novel prognostic classification systems of colorectal cancer

Summary: pp. IV-V

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

MALDI imaging mass spectrometry in clinical proteomics research of gastric cancer tissues

In the presented thesis, matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry was used for the proteomic analysis of gastric cancer tissue samples, with the aims of 1) identifying proteins that predict disease outcome of patients with intestinal-type gastric cancer after surgical resection, and 2) generating a proteomic classifier that determines HER2-status in order to aid in therapy decision with regard to trastuzumab (Herceptin) administration. In the first study, a seven-protein signature was found to be associated with an unfavorable overall survival independent of major clinical covariates after analyzing 63 intestinal-type primary resected gastric cancer samples by MALDI imaging. Of these seven proteins, three could be identified as CRIP1, HNP-1, and S100-A6, and validated immunohistochemically on tissue microarrays of an independent validation cohort (n=118). While HNP-1 and S100-A6 were found to further subdivide early (UICC-I) and late stage (UICC-II-III) patients into different prognostic groups, CRIP1, a protein previously unknown in gastric cancer, was confirmed as a novel and independent prognostic factor for all patients in the validation cohort. The protein pattern described here serves as a new independent indicator of patient survival complementing the previously known clinical parameters in terms of prognostic relevance. In the second study, we hypothesized that MALDI imaging mass spectrometry may be useful for generating a classifier that may determine HER2-status in gastric cancer. This assumption was based on previous results where HER2-status could be reliably predicted in breast cancer patients. Here, 59 gastric cryo tissue samples were analyzed by MALDI imaging and the obtained proteomic profiles were used to create HER2 prediction models using different classification algorithms. Astonishingly, the breast cancer proteomic classifier from the previous study was able to correctly predict HER2-status in gastric cancers with a sensitivity of 65% and a specificity of 92%. In order to create a universal classifier for HER2-status, breast and non-breast cancer samples were combined, which increased sensitivity to 78%; specificity was 88%. This study provides evidence that HER2-status can be identified on a proteomic level across different cancer types suggesting that HER2 overexpression may constitute a widely spread molecular event independent of the tumor entity.Im Rahmen dieser Doktorarbeit wurden zwei Arbeiten publiziert, in denen die bildgebende Massenspektrometrie als zentrale Methode zur proteomischen Analyse von Magenkarzinomgeweben eingesetzt wurde. Dabei wurden folgende Ziele verfolgt: 1. die Identifizierung prognostischer Proteinmarker für Patienten mit intestinalem Magenkarzinom, und 2. die Generierung eines proteomischen Klassifikators zur Bestimmung des HER2-Status zur Entscheidungshilfe für eine Behandlung mit Trastuzumab (Herzeptin). In der ersten Studie wurde eine Signatur bestehend aus sieben Proteinsignalen gefunden, deren Überexpression unabhängig von anderen klinischen Parametern ein schlechtes Gesamtüberleben der Patienten indizieren. Hierzu wurden 63 Gewebeproben von Patienten mit Magenkarzinom intestinalen Typs mittels MALDI Imaging analysiert. Drei der sieben Proteinsignale konnten als CRIP1, HNP-1 und S100-A6 identifiziert werden. Diese wurden anschließend an einem unabhängigen Patientenkollektiv (n=118) immunhistochemisch anhand von Tissue Microarrays validiert. Dabei zeigte sich, dass die beiden Proteine HNP-1 und S100-A6 bestehende klinische Gruppen nach ihrem Risiko weiter aufstratifizieren konnten; HNP-1 Magenkarzinompatienten im frühen Stadium (UICC I) und S100-A6 Patienten im fortgeschrittenen Stadium (UICC II-III). Darüber hinaus konnte CRIP1 als unabhängiger prognostischer Faktor für alle Patienten des Validierungskollektives bestätigt werden. Perspektivisch könnte die hier beschriebene Proteinsignatur vorhandene klinische Parameter als neuer und unabhängiger Indikator für das Überleben von Magenkrebspatienten ergänzen. In der zweiten Studie wurden Proteinexpressionsmuster benutzt, um den HER2-Status in Magenkrebsgeweben vorauszusagen; denn seit kurzem ist der epidermale Wachstumsfaktor-Rezeptor HER2 eine wichtige tumorbiologische Zielstruktur bei der Behandlung von Magenkrebspatienten mit dem therapeutischen Antikörper Trastuzumab. In einer vorherigen Studie konnten wir die Machbarkeit der HER2-Status-Bestimmung durch MALDI Imaging erfolgreich anhand von Brustkrebsproben demonstrieren. Unter der Annahme, dass der HER2-Überexpression – unabhängig vom Tumortyp – charakteristische molekulare Veränderungen zugrunde liegen, wurde untersucht, ob eine Bestimmung des HER2-Status in Magenkrebspatienten mit Hilfe von Proteinexpressionsmustern aus Brustkrebspatienten erfolgen kann. Hierzu wurden, zusätzlich zu den bereits vorhandenen 48 Brustkrebsgeweben, 59 Magenkrebsfälle mittels MALDI Imaging analysiert und verschiedene HER2-Klassifikationsmodelle erstellt und verglichen. Der HER2-Status in Magenkrebsfällen konnte mit einem Mammakarzinom-spezifischen Profil mit einer Sensitivität von 65% und einer Spezifität von 92% bestimmt werden. Zusätzlich wurden die Expressionsprofile aller vorhandenen Tumorarten zusammengeführt, um einen universellen HER2-Klassifikator zu erstellen. Dies führte zu einer verbesserten Vorhersagequalität (Sensitivität: 78%, Spezifität: 88%). Dass sich der HER2-Status über verschiedene Tumorentitäten hinweg auf proteomischer Ebene bestimmen lässt, legt nahe, dass die Überexpression von HER2 ein unabhängiges molekulares Ereignis darstellt, ungeachtet der Herkunft des Tumors. Zudem unterstreichen die Ergebnisse das diagnostische Potential der bildgebenden Massenspektrometrie zur schnellen und zuverlässigen Bestimmung von tumorbiologischen Zielstrukturen, wie HER2

Assessment of histopathological methods of evaluating response to neoadjuvant therapy in oesophageal and gastric adenocarcinoma

Upper gastrointestinal tract (GIT) cancers usually receive neoadjuvant therapy prior to surgery. The histological assessment of this response and if this can be predicted on the pre-treatment biopsy are the subject of this thesis. The first study assessed the inter- and intra-observer variation amongst pathologists in evaluating the degree of regression using the Mandard scoring system. The results showed that the reproducibility of this system was only fair to moderate in both cases of inter- and intra-observer testing. The second study examined the levels of expression of selected tumour markers before and after neoadjuvant chemotherapy. These included markers monitoring apoptosis (p53 and bcl-2), proliferation (Ki-67), angio- and lymphangio-genesis (VEGF, CD-31 and LYVE-1). The levels of expression in these markers were measured in the pre-treatment biopsies, to monitor if they could predict the response to neoadjuvant therapy. It was found that when the panel of chosen markers being used together, delivered a much higher power of prediction rather than adopting only one marker, where the collective power of prediction was 80.6%, whereas individually, the power of prediction ranged between 24.6% (VEGF) and 60.7% (Ki-67). The third study explored the use of digital image analysis in assessing the response to neoadjuvant therapy. It was found that while this technique paralleled the Mandard scoring system, it delivered a more objective and reproducible assessment. On the basis of these results I suggest that image analysis should be used to assess tumour regression especially in the context of clinical trials. In this retrospective study it has been shown that the pre-treatment biopsy can predict the degree of regression

Machine Learning/Deep Learning in Medical Image Processing

Many recent studies on medical image processing have involved the use of machine learning (ML) and deep learning (DL). This special issue, “Machine Learning/Deep Learning in Medical Image Processing”, has been launched to provide an opportunity for researchers in the area of medical image processing to highlight recent developments made in their fields with ML/DL. Seven excellent papers that cover a wide variety of medical/clinical aspects are selected in this special issue