Metabolomics is the methodical scientific study of biochemical processes associated with the metabolome—which comprises the entire collection of metabolites in any biological entity. Metabolome changes occur as a result of modifications in the genome and proteome, and are, therefore, directly related to cellular phenotype. Thus, metabolomic analysis is capable of providing a snapshot of cellular physiology. Untargeted metabolomics is an impartial, all-inclusive approach for detecting as many metabolites as possible without a priori knowledge of their identity. Hence, it is a valuable exploratory tool capable of providing extensive chemical information for discovery and hypothesis-generation regarding biochemical processes. A history of metabolomics and advances in the field corresponding to improved analytical technologies are described in Chapter 1 of this dissertation. Additionally, Chapter 1 introduces the analytical workflows involved in untargeted metabolomics research to provide a foundation for Chapters 2 – 5. Part I of this dissertation which encompasses Chapters 2 – 3 describes the utilization of mass spectrometry (MS)-based untargeted metabolomic analysis to acquire new insight into cancer detection. There is a knowledge deficit regarding the biochemical processes of the origin and proliferative molecular mechanisms of many types of cancer which has also led to a shortage of sensitive and specific biomarkers. Chapter 2 describes the development of an in vitro diagnostic multivariate index assay (IVDMIA) for prostate cancer (PCa) prediction based on ultra performance liquid chromatography-mass spectrometry (UPLC-MS) metabolic profiling of blood serum samples from 64 PCa patients and 50 healthy individuals. A panel of 40 metabolic spectral features was found to be differential with 92.1% sensitivity, 94.3% specificity, and 93.0% accuracy. The performance of the IVDMIA was higher than the prevalent prostate-specific antigen blood test, thus, highlighting that a combination of multiple discriminant features yields higher predictive power for PCa detection than the univariate analysis of a single marker. Chapter 3 describes two approaches that were taken to investigate metabolic patterns for early detection of ovarian cancer (OC). First, Dicer-Pten double knockout (DKO) mice that phenocopy many of the features of metastatic high-grade serous carcinoma (HGSC) observed in women were studied. Using UPLC-MS, serum samples from 14 early-stage tumor DKO mice and 11 controls were analyzed. Iterative multivariate classification selected 18 metabolites that, when considered as a panel, yielded 100% accuracy, sensitivity, and specificity for early-stage HGSC detection. In the second approach, serum metabolic phenotypes of an early-stage OC pilot patient cohort were characterized. Serum samples were collected from 24 early-stage OC patients and 40 healthy women, and subsequently analyzed using UPLC-MS. Multivariate statistical analysis employing support vector machine learning methods and recursive feature elimination selected a panel of metabolites that differentiated between age-matched samples with 100% cross-validated accuracy, sensitivity, and specificity. This small pilot study demonstrated that metabolic phenotypes may be useful for detecting early-stage OC and, thus, supports conducting larger, more comprehensive studies. Many challenges exist in the field of untargeted metabolomics. Part II of this dissertation which encompasses Chapters 4 – 5 focuses on two specific challenges. While metabolomic data may be used to generate hypothesis concerning biological processes, determining causal relationships within metabolic networks with only metabolomic data is impractical. Proteins play major roles in these networks; therefore, pairing metabolomic information with that acquired from proteomics gives a more comprehensive snapshot of perturbations to metabolic pathways. Chapter 4 describes the integration of MS- and NMR-based metabolomics with proteomics analyses to investigate the role of chemically mediated ecological interactions between Karenia brevis and two diatom competitors, Asterionellopsis glacialis and Thalassiosira pseudonana. This integrated systems biology approach showed that K. brevis allelopathy distinctively perturbed the metabolisms of these two competitors. A. glacialis had a more robust metabolic response to K. brevis allelopathy which may be a result of its repeated exposure to K. brevis blooms in the Gulf of Mexico. However, K. brevis allelopathy disrupted energy metabolism and obstructed cellular protection mechanisms including altering cell membrane components, inhibiting osmoregulation, and increasing oxidative stress in T. pseudonana. This work represents the first instance of metabolites and proteins measured simultaneously to understand the effects of allelopathy or in fact any form of competition. Chromatography is traditionally coupled to MS for untargeted metabolomics studies. While coupling chromatography to MS greatly enhances metabolome analysis due to the orthogonality of the techniques, the lengthy analysis times pose challenges for large metabolomics studies. Consequently, there is still a need for developing higher throughput MS approaches. A rapid metabolic fingerprinting method that utilizes a new transmission mode direct analysis in real time (TM-DART) ambient sampling technique is presented in Chapter 5. The optimization of TM-DART parameters directly affecting metabolite desorption and ionization, such as sample position and ionizing gas desorption temperature, was critical in achieving high sensitivity and detecting a broad mass range of metabolites. In terms of reproducibility, TM-DART compared favorably with traditional probe mode DART analysis, with coefficients of variation as low as 16%. TM-DART MS proved to be a powerful analytical technique for rapid metabolome analysis of human blood sera and was adapted for exhaled breath condensate (EBC) analysis. To determine the feasibility of utilizing TM-DART for metabolomics investigations, TM-DART was interfaced with traveling wave ion mobility spectrometry (TWIMS) time-of-flight (TOF) MS for the analysis of EBC samples from cystic fibrosis patients and healthy controls. TM-DART-TWIMS-TOF MS was able to successfully detect cystic fibrosis in this small sample cohort, thereby, demonstrating it can be employed for probing metabolome changes. Finally, in Chapter 6, a perspective on the presented work is provided along with goals on which future studies may focus.

Near field imaging using microwave in medical applications has gain much attention recently as various researches show its high ability and accuracy in illuminating object comparing to the well-known screening tools such as Magnetic Resonance Imaging (MRI), digital mammography, ultrasound etc. This has encourage and motivate scientists continue to exploit the potential of microwave imaging so that a better and more powerful sensing tools can be developed. This thesis documents the development of antenna design for microwave imaging application such as breast cancer detection. The application is similar to the concept of Ground Penetrating Radar (GPR) but operating at higher frequency band. In these systems a short pulse is transmitted from an antenna to the medium and the backscattered response is investigated for diagnose. In order to accommodate such a short pulse, a very wideband antenna with a minimal internal reflection is required. Printed monopole and planar metal plate antenna is implemented to achieve the necessary operating wide bandwidth. The development of new compact printed planar metal plate ultra wide bandwidth antenna is presented. A generalized parametric study is carried out using two well-known software packages to achieve optimum antenna performance. The Prototype antennas are tested and analysed experimentally, in which a reasonable agreement was achieved with the simulations. The antennas present an excellent relative wide bandwidth of 67% with acceptable range of power gain between 3.5 to 7 dBi. A new compact size air-dielectric microstrip patch-antenna designs proposed for breast cancer detection are presented. The antennas consist of a radiating patch mounted on two vertical plates, fed by coaxial cable. The antennas show a wide bandwidth that were verified by the simulations and also confirmed experimentally. The prototype antennas show excellent performance in terms the input impedance and radiation performance over the target range bandwidth from 4 GHz to 8 GHz. A mono-static model with a homogeneous dielectric box having similar properties to human tissue is used to study the interaction of the antenna with tissue. The numerical results in terms the matching required of new optimised antennas were promising. An experimental setup of sensor array for early-stage breast-cancer detection is developed. The arrangement of two elements separated by short distance that confined equivalent medium of breast tissues were modelled and implemented. The operation performances due to several orientations of the antennas locations were performed to determine the sensitivity limits with and without small size equivalent cancer cells model. In addition, a resistively loaded bow tie antenna, intended for applications in breast cancer detection, is adaptively modified through modelling and genetic optimisation is presented. The required wideband operating characteristic is achieved through manipulating the resistive loading of the antenna structure, the number of wires, and their angular separation within the equivalent wire assembly. The results show an acceptable impedance bandwidth of 100.75 %, with a VSWR < 2, over the interval from 3.3 GHz to 10.0 GHz. Feasibility studies were made on the antenna sensitivity for operation in a tissue equivalent dielectric medium. The simulated and measured results are all in close agreement.

Radionuclide molecular imaging is an emerging multidisciplinary technique that is used in modern medicine to visualise diseases at cellular and molecular levels. This thesis is based on five papers (I-V) and focuses on the development of site-specific radiolabelled recombinant anti-HER2 Affibody molecules and preclinical evaluations in vitro and in vivo of the labelled conjugates. This work is part of a preclinical development of an Affibody molecule-based tracer for molecular imaging of HER2 expressing tumours. Papers I and II report the evaluation of the Affibody molecule ZHER2:2395-C, site-specifically labelled with the radiometals 111In (for SPECT) and 57Co (as a surrogate for 55Co, suitable for PET applications) using a thiol reactive DOTA derivative as a chelator. Both conjugates demonstrated very suitable biodistribution properties, enabling high contrast imaging just a few hours after injection. Papers III and IV report the development and optimization of a technique for site-specific labelling of ZHER2:2395-C with 99mTc using an N3S chelating peptide sequence. 99mTc-ZHER2:2395-C demonstrated high and specific tumour uptake and rapid clearance of non-bound tracer from the blood, resulting in high tumour-to-non-tumour ratios shortly after injection, enabling high contrast imaging. In addition, in the study described in paper IV, freeze-dried kits previously developed for 99mTc-labelling were optimised, resulting in the development of a kit in which all the reagents and protein needed for labelling of ZHER2:2395-C with 99mTc were contained in a single vial. Paper V reports the evaluation of an anti-HER2 Affibody molecule, ABY-025, with a fundamentally re-engineered scaffold. Despite the profound re-engineering, the biodistribution pattern of 111In-ABY-025 was very similar to that of two variants of the parental molecule. It seems reasonable to believe that these results will also be applicable to Affibody molecules towards other targets. Hopefully, this work will also be helpful in the development of other small proteinaceous tracers.

Oral cancer is the sixth most common cancer worldwide and has been marked by high morbidity and poor survival rates that have changed little over the past few decades. Beyond prevention, early detection is the most crucial determinant for successful treatment and survival of cancer. Yet current methodologies for cancer diagnosis based upon pathological examination alone are insufficient for detecting early tumor progression and molecular transformation. Development of new diagnostic tools incorporating tumor biomarkers could enhance early detection by providing molecular-level insight into the biochemical and cellular changes associated with oral carcinogenesis. The work presented in this doctoral dissertation aims to address this clinical need through the development of new automated cellular analysis methods, incorporating lab-on-a-chip sensor techniques, for examination of molecular and morphological biomarkers associated with oral carcinogenesis. Using the epidermal growth factor receptor (EGFR) as a proof-of-principle biomarker, the sensor system demonstrated capacity to support rapid biomarker analysis in less than one-tenth the time of traditional methods and effectively characterized EGFR biomarker over-expression in oral tumor-derived cell lines. Successful extension from in vitro tumor cell lines to clinically relevant exfoliative brush cytology was demonstrated, providing a non-invasive method for sampling abnormal oral epithelium. Incorporation of exfoliative cytology further helped to define the important assay and imaging parameters necessary for dual molecular and morphological analysis in adherent epithelium. Next, this new sensor assay and method was applied in a small pilot study in order to secure an initial understanding of the diagnostic utility of such biosensor systems in clinical settings. Four cellular features were identified as useful indicators of cancerous or pre-cancerous conditions including, the nuclear area and diameter, nuclear-to-cytoplasm ratio, and EGFR biomarker expression. Further examination using linear regression and ROC curve analysis identified the morphological features as the best predictors of disease while a combination of all features may be ideal for classification of OSCC and pre-malignancy with high sensitivity and specificity. Further testing in a larger sample size is necessary to validate this regression model and the LOC sensor technique, but shows strong promise as a new diagnostic tool for early detection of oral cancer. / text

Orale Plattenepithelkarzinome stellen seit Jahrzehnten eine globale Herausforderung im Gesundheitswesen dar. In dieser Studie wird mit dem Optical Stretcher ein neuer diagnostischer Ansatz in der Krebserkennung der Mundhöhle untersucht und im Rahmen einer klinischen Pilotstudie evaluiert. Dabei steht die Beurteilung der viskoelastischen Eigenschaften von oralen Epithelzellen im Vordergrund. Eine entscheidende Rolle spielt hierbei vor allem das Zytoskelett einer Zelle, welches aus unterschiedlichen Faserstrukturen ein komplexes, dynamisches Gerüst bildet und für die Strukturgebung sowie für die mechanischen Eigenschaften der unterschiedlichen Zelltypen verantwortlich ist. In dieser Arbeit wurden diesbezüglich einzelne Zellen im Optical Stretcher ohne direkten mechanischen Kontakt durch zwei gegenüberliegende Laserstrahlen verformt. Dabei wurde die relative Deformation als Längenänderung entlang der Laserachse von gedehnter zu ungedehnter Zelle definiert. Die relative Deformation dient als Vergleichsparameter und unterliegt verschiedenen Einflussfaktoren. Schließlich erlauben das Maß und die Art der Deformation, welche individuell für jede Zelle sind, Rückschlüsse auf ihr biologisches Verhalten. In Kombination mit statistischen Auswertungsalgorithmen war es möglich, signifikante Unterschiede hinsichtlich der relativen Dehnung zwischen benignen und malignen oralen Zellen darzustellen. Die Ergebnisse zeigen, dass der Optical Stretcher in der Lage ist, bereits minimale Veränderungen zwischen den verschiedenen zytoskelettalen Zuständen einer Zelle zu detektieren und somit wird sich die Dehnungsfähigkeit einer Zelle zukünftig als sensibler Zellmarker zur Dignitätsbestimmung etablieren.

Our aim was to determine the characteristics, treatments and outcomes of patients with primary spinal glioblastomas (GB) or gliosarcomas (GS) reported in literature until March 2015. PubMed and Web of Science were searched for peer-reviewed articles pertaining to cases of glioblastomas / gliosarcomas with primary spinal origin, using predefined search terms. Furthermore we performed hand searches tracking the references from the selected papers. Eighty-two articles published between 1938 and March 2015 were eligible. They reported on 157 patients. Median age at diagnosis was 22 years. The proportion of patients who received adjuvant chemo- or radiotherapy clearly increased from the time before 1980 until present. Median overall survival from diagnosis was 8.0 ± 0.9 months. On univariate analysis age influenced overall survival, whereas tumor location, gender and the extent of initial resection did not. Outcomes did not differ between children (< 18 years) and adults. However, the patients who were treated after 1980 achieved longer survival times than the patients treated before. On multivariable analysis only age (< 60 years) and the time period of treatment (>1980) were confirmed as positive independent prognostic factors. In conclusion, primary spinal GB / GS mainly affect younger patients and are associated with a dismal prognosis. However, most likely due to the increasing use of adjuvant treatment, modest therapeutic progress has been achieved over recent decades. The characteristics and treatments of primary spinal glioblastomas should be entered into a central registry in order to gain more information about the ideal treatment approach in the future.

Background and Purpose: In 2007, the WHO classification of brain tumors was extended by three new entities of glioneuronal tumors: papillary glioneuronal tumor (PGNT), rosette-forming glioneuronal tumor of the fourth ventricle (RGNT) and glioneuronal tumor with neuropil-like islands (GNTNI). Focusing on clinical characteristics and outcome, the authors performed a comprehensive individual patient data (IPD) meta-analysis of the cases reported in literature until December 2012. Methods: PubMed, Embase and Web of Science were searched for peer-reviewed articles reporting on PGNT, RGNT, and GNTNI using predefined keywords. Results: 95 publications reported on 182 patients (PGNT, 71; GNTNI, 26; RGNT, 85). Median age at diagnosis was 23 years (range 4–75) for PGNT, 27 years (range 6–79) for RGNT, and 40 years (range 2–65) for GNTNI. Ninety-seven percent of PGNT and 69% of GNTNI were located in the supratentorial region, 23% of GNTNI were in the spinal cord, and 80% of RGNT were localized in the posterior fossa. Complete resection was reported in 52 PGNT (73%), 36 RGNT (42%), and 7 GNTNI (27%) patients. Eight PGNT, 3 RGNT, and 12 GNTNI patients were treated with chemo- and/or radiotherapy as the primary postoperative treatment. Follow-up data were available for 132 cases. After a median follow-up time of 1.5 years (range 0.2–25) across all patients, 1.5-year progression-free survival rates were 52±12% for GNTNI, 86±5% for PGNT, and 100% for RGNT. The 1.5-year overall-survival were 95±5%, 98±2%, and 100%, respectively. Conclusions: The clinical understanding of the three new entities of glioneuronal tumors, PGNT, RGNT and GNTNI, is currently emerging. The present meta-analysis will hopefully contribute to a delineation of their diagnostic, therapeutic, and prognostic profiles. However, the available data do not provide a solid basis to define the optimum treatment approach. Hence, a central register should be established.

Near field imaging using microwave in medical applications has gain much attention recently as various researches show its high ability and accuracy in illuminating object comparing to the well-known screening tools such as Magnetic Resonance Imaging (MRI), digital mammography, ultrasound etc. This has encourage and motivate scientists continue to exploit the potential of microwave imaging so that a better and more powerful sensing tools can be developed. This thesis documents the development of antenna design for microwave imaging application such as breast cancer detection. The application is similar to the concept of Ground Penetrating Radar (GPR) but operating at higher frequency band. In these systems a short pulse is transmitted from an antenna to the medium and the backscattered response is investigated for diagnose. In order to accommodate such a short pulse, a very wideband antenna with a minimal internal reflection is required. Printed monopole and planar metal plate antenna is implemented to achieve the necessary operating wide bandwidth. The development of new compact printed planar metal plate ultra wide bandwidth antenna is presented. A generalized parametric study is carried out using two well-known software packages to achieve optimum antenna performance. The Prototype antennas are tested and analysed experimentally, in which a reasonable agreement was achieved with the simulations. The antennas present an excellent relative wide bandwidth of 67% with acceptable range of power gain between 3.5 to 7 dBi. A new compact size air-dielectric microstrip patch-antenna designs proposed for breast cancer detection are presented. The antennas consist of a radiating patch mounted on two vertical plates, fed by coaxial cable. The antennas show a wide bandwidth that were verified by the simulations and also confirmed experimentally. The prototype antennas show excellent performance in terms the input impedance and radiation performance over the target range bandwidth from 4 GHz to 8 GHz. A mono-static model with a homogeneous dielectric box having similar properties to human tissue is used to study the interaction of the antenna with tissue. The numerical results in terms the matching required of new optimised antennas were promising. An experimental setup of sensor array for early-stage breast-cancer detection is developed. The arrangement of two elements separated by short distance that confined equivalent medium of breast tissues were modelled and implemented. The operation performances due to several orientations of the antennas locations were performed to determine the sensitivity limits with and without small size equivalent cancer cells model. In addition, a resistively loaded bow tie antenna, intended for applications in breast cancer detection, is adaptively modified through modelling and genetic optimisation is presented. The required wideband operating characteristic is achieved through manipulating the resistive loading of the antenna structure, the number of wires, and their angular separation within the equivalent wire assembly. The results show an acceptable impedance bandwidth of 100.75 %, with a VSWR < 2, over the interval from 3.3 GHz to 10.0 GHz. Feasibility studies were made on the antenna sensitivity for operation in a tissue equivalent dielectric medium. The simulated and measured results are all in close agreement.

Near field imaging using microwaves in medical applications is of great current interest for its capability and accuracy in identifying features of interest, in comparison with other known screening tools. Many imaging methods have been developed over the past two decades showing the potential of microwave imaging in medical applications such as early breast cancer detection, analysis of cardiac tissues, soft tissues and bones. Microwave imaging uses non-ionizing ultra wideband (UWB) electromagnetic signals and utilises tissue-dependent dielectric contrast to reconstruct signals and images using radar-based or tomographic imaging techniques. Microwave imaging offers low health risk, low operational cost, ease of use and user-friendliness. This study documents microwave imaging experiments for early breast cancer detection and bone fracture detection using radar approach. An actively tuned UWB patch antenna and a UWB Vivaldi antenna are designed and utilised as sensing elements in the aforementioned applications. Both UWB antennas were developed over a range of frequency spectrum, and then characteristics were tested against their ability for microwave imaging applications by reconstructing the 3D Inversion Algorithm. An experiment was conducted using patch antenna to test the detection of variable sizes of cancer tissues based on a simple phantom consisting of a plastic container with a low dielectric material emulating fatty tissue and high dielectric constant object emulating a tumour, is scanned between 4 to 8 GHz with the patch antenna. A 2-D image of the tumour is constructed using the reflected signal response to visualize the location and size of the tumour. A Vivaldi antenna is designed covering 3.1 to 10.6 GHz. The antenna is tested via simulation for detecting bone fractures of various sizes and 2-D images are generated using reflected pulses to show the size of fracture. The Vivaldi antenna is optimised for early breast cancer detection and detailed simulated study is carried out using different breast phantoms and tumour sizes. Simulations are backed with the experimental investigation with the test setup used for patch antenna. Generated images for simulations and experimental investigation show good agreement, and show the presence of tumour with good location accuracy. Measurements indicate that both prototype microwave sensors are good candidates for tested imaging applications.

Breast cancer continues to be a major cause of mortality among women. In recent years, machine learning has emerged as a potential tool in detecting and grading cancer. Using machine learning techniques in computational pathology has the potential to improve precision medicine, enabling more personalized and more accurate treatment plans. The machine learning models can even detect structures that cannot be seen with human eyes. The first step is often to identify tissue areas with cancerous cells using machine learning models. Those models often rely solely on Haematoxylin and Eosin slides for training due to the time-consuming and costly nature of annotations by pathologists. Because of that, valuable information for training might be lost since some cancerous cells are more visible in the immunohistochemistry slides. In this thesis, Haematoxylin and Eosin slide annotations are registered to immunohistochemistry slides for training singlestain and multi-stain models. The registration of the annotations is not straightforward since the tissue of the slides is not necessarily from consecutive cuts, and they are sometimes applied to the slide at different angles. An algorithm evaluated during the ACROBAT challenge was used to register the slides. Using the transferred annotations, individual models are trained for each stain (K167, HER2, PGR, ER). Of the single-stain model, the HER2 stain model is showing the most promising results. As a second step, a multistain model is trained using all stains. The multi-stain model performs equally well as the single-stain models specializing in individual stains. This shows that there is no need to train specialized single-stain models. Thus being able to train one model for four different stains makes it possible to detect cancer in whole slide images stained with one of those four stains without the need to train a specialized model and only needing annotations in one stain. While the multi-stain model is a nice addition this thesis shows that it is possible to reuse annotations, which reduces the amount of manual labour from pathologists and allows for training models on immunohistochemistry slides with only having annotations from one stain. / Bröstcancer fortsätter att vara en vanlig orsak till dödlighet bland kvinnor. På senare år har maskininlärning visat sig vara ett värdefullt verktyg för att upptäcka och gradera cancer. Att använda maskininlärningstekniker inom beräkningspatologi har potential att förbättra precisionsmedicinen och möjliggöra mer individanpassade och exakta behandlingsplaner. Maskininlärningsmodellerna kan till och med upptäcka strukturer som inte kan ses med mänskliga ögon. Det första steget är ofta att identifiera vävnadsområden med cancerceller med hjälp av maskininlärningsmodeller. Dessa modeller är ofta helt beroende av hematoxylin- och eosin-slidebilder för träning eftersom det är tidsödande och kostsamt för patologer att göra annoteringar. På grund av detta kan värdefull information för träning gå förlorad eftersom vissa cancerceller är mer synliga på immunohistokemiska objektglas. I den här avhandlingen registreras annoteringar från objektglas med hematoxylin och eosin på immunohistokemiska objektglas för träning av modeller med en och flera infärgningar. Registreringen av annoteringarna är inte okomplicerad eftersom vävnaden på objektglasen inte nödvändigtvis kommer från på varandra följande snitt, och de appliceras ibland på objektglaset i olika vinklar. En algoritm som utvecklades under ACROBAT-utmaningen användes för att registrera bilderna. Med hjälp av de registrerade objektglasen tränas individuella modeller för varje infärgning (K167, HER2, PGR, ER). Av modellerna för enstaka infärgningar visar modellen för HER2-infärgning de mest lovande resultaten. I ett andra steg tränas en modell med flera infärgningar med hjälp av alla infärgningar. Multi-stain-modellen presterar lika bra som single-stain-modellerna som är specialiserade på enskilda infärgningar. Detta visar att det inte finns något behov av att träna specialiserade modeller för enstaka infärgningar. Att kunna träna en modell för fyra olika färgämnen gör det alltså möjligt att upptäcka cancer i hela objektglasbilder som färgats med ett av dessa fyra färgämnen utan att behöva träna en specialiserad modell och utan att behöva göra annoteringar. Möjligheten att endast använda en modell för att förutsäga fyra olika immunohistokemiska helbilder minskade datorkostnaderna för träning och underhåll av modellen.