A multi-technique hierarchical X-ray phase-based approach for the characterization and quantification of the effects of novel radiotherapies

Cancer is the first or second leading cause of premature deaths worldwide with an overall rapidly growing burden. Standard cancer therapies include surgery, chemotherapy and radiotherapy (RT) and often a combination of the three is applied to improve the probability of tumour control. Standard therapy protocols have been established for many types of cancers and new approaches are under study especially for treating radio-resistant tumours associated to an overall poor prognosis, as for brain and lung cancers. Follow up techniques able to monitor and investigate the effects of therapies are important for surveying the efficacy of conventionally applied treatments and are key for accessing the curing capabilities and the onset of acute and late adverse effects of new therapies. In this framework, this doctoral Thesis proposes the X-ray Phase Contrast Im-aging - Computed Tomography (XPCI-CT) technique as an imaging-based tool to study and quantify the effects of novel RTs, namely Microbeam and Minibeam Radiation therapy (MRT and MB), and to compare them to the standard Broad Beam (BB) induced effects on brain and lungs. MRT and MB are novel radiotherapies that deliver an array of spatially fractionated X-ray beamlets issued from a synchrotron radiation source, with widths of tens or hundreds of micrometres, respectively. MRT and MB exploit the so-called dose-volume effect: hundreds of Grays are well tolerated by healthy tissues and show a preferential effect on tumour cells and vasculature when delivered in a micrometric sized micro-plane, while induce lethal effects if applied over larger uniform irradiation fields. Such highly collimated X-ray beams need a high-resolution and a full-organ approach that can visualize, with high sensitivity, the effects of the treatment along and outside the beamlets path. XPCI-CT is here suggested and proven as a powerful imaging technique able to determine and quantify the effects of the radiation on normal and tumour-bearing tissues. Moreover, it is shown as an effective technique to complement, with 3D information, the histology findings in the follow-up of the RT treatments. Using a multi-scale and multi-technique X-ray-based approach, I have visualized and analysed the effects of RT delivery on healthy and glioblastoma multiforme (GBM)-bearing rat brains as well as on healthy rat lungs. Ex-vivo XPCI-CT datasets acquired with isotropic voxel sizes in the range 3.253 – 0.653 μm3 could distinguish, with high sensitivity, the idiopathic effects of MRT, MB and BB therapies. Histology, immunohistochemistry, Small- and Wide-Angle X-ray Scattering and X-ray Fluorescence experiments were also carried out to accurately interpret and complement the XPCI-CT findings as well as to obtain a detailed structural and chemical characterization of the detected pathological features. Overall, this multi-technique approach could detect: i) a different radio-sensitivity for the MRT-treated brain areas; ii) Ca and Fe deposits, hydroxyapatite crystals formation; iii) extended and isolated fibrotic contents. Full-organ XPCI-CT datasets allowed for the quantification of tumour and mi-crocalcifications’ volumes in treated brains and the amount of scarring tissue in irradiated lungs. Herein, the role of XPCI-CT as a 3D virtual histology technique for the follow-up of ex-vivo RT effects has been assessed as a complementary method for an accurate volumetric investigation of normal and pathological states in brains and lungs, in a small animal model. Moreover, the technique is proposed as a guidance and auxiliary tool for conventional histology, which is the gold standard for pathological evaluations, owing to its 3D capabilities and the possibility of virtually navigating within samples. This puts a landmark for XPCI-CT inclusion in the pre-clinical studies pipeline and for advancing towards in-vivo XPCI-CT imaging of treated organs.Weltweit gilt Krebs als häufigste bzw. zweithäufigste Ursache eines zu früh erfolgenden Todes, wobei die Zahlen rasch ansteigen. Standardmäßige Krebstherapien umfassen chirurgische Eingriffe, Chemotherapie und Strahlentherapie (radiotherapy, RT); oft kommt eine Kombination daraus zur Anwendung, um die Wahrscheinlichkeit der Tumorkontrolle zu erhöhen. Es wurden Standardtherapieprotokolle für zahlreiche Krebsarten eingerichtet und es wird vor allem in der Behandlung von strahlenresistenten Tumoren mit allgemein schlechter Prognose wie bei Hirn- und Lungentumoren an neuen Ansätzen geforscht. Nachverfolgungstechniken, welche die Auswirkungen von Therapien überwachen und ermitteln, sind zur Überwachung der Wirksamkeit herkömmlich angewandter Behandlungen wichtig und auch maßgeblich am Zugang zu den Fähigkeiten zur Heilung sowie zum Auftreten akuter und verzögerter Nebenwirkungen neuer Therapien beteiligt. In diesem Rahmenwerk unterbreitet diese Doktorarbeit die Technik der Röntgen-Phasenkontrast-Bildgebung über Computertomographie (X-ray Phase Contrast Imaging - Computed Tomography, XPCI‑CT) als bildverarbeitungs-basiertes Tool zur Untersuchung und Quantifizierung der Auswirkungen neuartiger Strahlentherapien, nämlich der Mikrobeam- und Minibeam-Strahlentherapie (MRT und MB), sowie zum Vergleich derselben mit den herkömmlichen durch Breitstrahlen (Broad Beam, BB) erzielten Auswirkungen auf Gehirn und Lunge. MRT und MB sind neuartige Strahlentherapien, die ein Array räumlich aufgeteilter Röntgenstrahlenbeamlets aus einer synchrotronen Strahlenquelle mit einer Breite von Zehnteln bzw. Hundersteln Mikrometern abgeben. MRT und MB nutzen den sogenannten Dosis-Volumen-Effekt: Hunderte Gray werden von gesundem Gewebe gut vertragen und wirken bei der Abgabe in einer Mikroebene im Mikrometerbereich vorrangig auf Tumorzellen und Blutgefäße, während sie bei einer Anwendung über größere gleichförmige Strahlungsfelder letale Auswirkungen aufweisen. Solche hoch kollimierten Röntgenstrahlen erfordern eine hohe Auflösung und einen Zugang zum gesamten Organ, bei dem die Auswirkungen der Behandlung entlang und außerhalb der Beamletpfade mit hoher Empfindlichkeit visualisiert werden können. Hier empfiehlt und bewährt sich die XPCI‑CT als leistungsstarke Bildverarbeitungstechnik, welche die Auswirkungen der Strahlung auf normale und tumortragende Gewebe feststellen und quantifizieren kann. Außerdem hat sich gezeigt, dass sie durch 3‑D-Informationen eine effektive Technik zur Ergänzung der histologischen Erkenntnisse in der Nachverfolgung der Strahlenbehandlung ist. Anhand eines mehrstufigen und multitechnischen röntgenbasierten Ansatzes habe ich die Auswirkungen der Strahlentherapie auf gesunde und von Glioblastomen (GBM) befallene Rattenhirne sowie auf gesunde Rattenlungen visualisiert und analysiert. Mit isotropen Voxelgrößen im Bereich von 3,53 bis 0,653 μm3 erfasste Ex-vivo-XPCI-CT-Datensätze konnten die idiopathischen Auswirkungen der MRT-, MB- und BB‑Behandlung mit hoher Empfindlichkeit unterscheiden. Es wurden auch Experimente zu Histologie, Immunhistochemie, Röntgenklein- und ‑weitwinkelstreuung und Röntgenfluoreszenz durchgeführt, um die XPCI‑CT-Erkenntnisse präzise zu interpretieren und zu ergänzen sowie eine detaillierte strukturelle und chemische Charakterisierung der nachgewiesenen pathologischen Merkmale zu erhalten. Im Allgemeinen wurde durch diesen multitechnischen Ansatz Folgendes ermittelt: i) eine un-terschiedliche Strahlenempfindlichkeit der mit MRT behandelten Gehirnbereiche; ii) Ca- und Fe-Ablagerungen und die Bildung von Hydroxylapatitkristallen; iii) ein ausgedehnter und isolierter Fibrosegehalt. XPCI‑CT-Datensätze des gesamten Organs ermöglichten die Quantifizierung der Volume von Tumoren und Mikroverkalkungen in den behandelten Gehirnen und der Menge des Narbengewebes in bestrahlten Lungen. Dabei wurde die Rolle der XPCI‑CT als virtuelle 3‑D-Histologietechnik für die Nachverfolgung von Ex-vivo-RT‑Auswirkungen als ergänzende Methode für eine präzise volumetrische Untersuchung des normalen und pathologischen Zustands von Gehirnen und Lungen im Kleintiermodell untersucht. Darüber hinaus wird die Technik aufgrund ihrer 3‑D-Fähigkeiten und der Möglichkeit zur virtuellen Navigation in den Proben als Leitfaden und Hilfstool für die herkömmliche Histologie vorgeschlagen, die der Goldstandard für die pathologische Evaluierung ist. Dies markiert einen Meilenstein für die Übernahme der XPCI‑CT in die Pipeline präklinischer Studien und für den Übergang zur In-vivo-XPCI‑CT von behandelten Organen

Network-driven strategies to integrate and exploit biomedical data

[eng] In the quest for understanding complex biological systems, the scientific community has been delving into protein, chemical and disease biology, populating biomedical databases with a wealth of data and knowledge. Currently, the field of biomedicine has entered a Big Data era, in which computational-driven research can largely benefit from existing knowledge to better understand and characterize biological and chemical entities. And yet, the heterogeneity and complexity of biomedical data trigger the need for a proper integration and representation of this knowledge, so that it can be effectively and efficiently exploited. In this thesis, we aim at developing new strategies to leverage the current biomedical knowledge, so that meaningful information can be extracted and fused into downstream applications. To this goal, we have capitalized on network analysis algorithms to integrate and exploit biomedical data in a wide variety of scenarios, providing a better understanding of pharmacoomics experiments while helping accelerate the drug discovery process. More specifically, we have (i) devised an approach to identify functional gene sets associated with drug response mechanisms of action, (ii) created a resource of biomedical descriptors able to anticipate cellular drug response and identify new drug repurposing opportunities, (iii) designed a tool to annotate biomedical support for a given set of experimental observations, and (iv) reviewed different chemical and biological descriptors relevant for drug discovery, illustrating how they can be used to provide solutions to current challenges in biomedicine.[cat] En la cerca d’una millor comprensió dels sistemes biològics complexos, la comunitat científica ha estat aprofundint en la biologia de les proteïnes, fàrmacs i malalties, poblant les bases de dades biomèdiques amb un gran volum de dades i coneixement. En l’actualitat, el camp de la biomedicina es troba en una era de “dades massives” (Big Data), on la investigació duta a terme per ordinadors se’n pot beneficiar per entendre i caracteritzar millor les entitats químiques i biològiques. No obstant, la heterogeneïtat i complexitat de les dades biomèdiques requereix que aquestes s’integrin i es representin d’una manera idònia, permetent així explotar aquesta informació d’una manera efectiva i eficient. L’objectiu d’aquesta tesis doctoral és desenvolupar noves estratègies que permetin explotar el coneixement biomèdic actual i així extreure informació rellevant per aplicacions biomèdiques futures. Per aquesta finalitat, em fet servir algoritmes de xarxes per tal d’integrar i explotar el coneixement biomèdic en diferents tasques, proporcionant un millor enteniment dels experiments farmacoòmics per tal d’ajudar accelerar el procés de descobriment de nous fàrmacs. Com a resultat, en aquesta tesi hem (i) dissenyat una estratègia per identificar grups funcionals de gens associats a la resposta de línies cel·lulars als fàrmacs, (ii) creat una col·lecció de descriptors biomèdics capaços, entre altres coses, d’anticipar com les cèl·lules responen als fàrmacs o trobar nous usos per fàrmacs existents, (iii) desenvolupat una eina per descobrir quins contextos biològics corresponen a una associació biològica observada experimentalment i, finalment, (iv) hem explorat diferents descriptors químics i biològics rellevants pel procés de descobriment de nous fàrmacs, mostrant com aquests poden ser utilitzats per trobar solucions a reptes actuals dins el camp de la biomedicina

Medical Image Retrieval Using Multimodal Semantic Indexing

Large collections of medical images have become a valuable source of knowledge, taking an important role in education, medical research and clinical decision making. An important unsolved issue that is actively investigated is the efficient and effective access to these repositories. This work addresses the problem of information retrieval in large collections of biomedical images, allowing to use sample images as alternative queries to the classic keywords. The proposed approach takes advantage of both modalities: text and visual information. The main drawback of the multimodal strategies is that the associated algorithms are memory and computation intensive. So, an important challenge addressed in this work is the design of scalable strategies, that can be applied efficiently and effectively in large medical image collections. The experimental evaluation shows that the proposed multimodal strategies are useful to improve the image retrieval performance, and are fully applicable to large image repositories.Maestrí

Deep Open Representative Learning for Image and Text Classification

Title from PDF of title page viewed November 5, 2020Dissertation advisor: Yugyung LeeVitaIncludes bibliographical references (pages 257-289)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2020An essential goal of artificial intelligence is to support the knowledge discovery process from data to the knowledge that is useful in decision making. The challenges in the knowledge discovery process are typically due to the following reasons: First, the real-world data are typically noise, sparse, or derived from heterogeneous sources. Second, it is neither easy to build robust predictive models nor to validate them with such real-world data. Third, the `black-box' approach to deep learning models makes it hard to interpret what they produce. It is essential to bridge the gap between the models and their support in decisions with something potentially understandable and interpretable. To address the gap, we focus on designing critical representatives of the discovery process from data to the knowledge that can be used to perform reasoning. In this dissertation, a novel model named Class Representative Learning (CRL) is proposed, a class-based classifier designed with the following unique contributions in machine learning, specifically for image and text classification, i) The unique design of a latent feature vector, i.e., class representative, represents the abstract embedding space projects with the features extracted from a deep neural network learned from either images or text, ii) Parallel ZSL algorithms with class representative learning; iii) A novel projection-based inferencing method uses the vector space model to reconcile the dominant difference between the seen classes and unseen classes; iv) The relationships between CRs (Class Representatives) are represented as a CR Graph where a node represents a CR, and an edge represents the similarity between two CRs.Furthermore, we designed the CR-Graph model that aims to make the models explainable that is crucial for decision-making. Although this CR-Graph does not have full reasoning capability, it is equipped with the class representatives and their inter-dependent network formed through similar neighboring classes. Additionally, semantic information and external information are added to CR-Graph to make the decision more capable of dealing with real-world data. The automated semantic information's ability to the graph is illustrated with a case study of biomedical research through the ontology generation from text and ontology-to-ontology mapping.Introduction -- CRL: Class Representative Learning for Image Classification -- Class Representatives for Zero-shot Learning using Purely Visual Data -- MCDD: Multi-class Distribution Model for Large Scale Classification -- Zero Shot Learning for Text Classification using Class Representative Learning -- Visual Context Learning with Big Data Analytics -- Transformation from Publications to Ontology using Topic-based Assertion Discovery -- Ontology Mapping Framework with Feature Extraction and Semantic Embeddings -- Conclusion -- Appendix A. A Comparative Evaluation with Different Similarity Measure

ENDOMET database – A means to identify novel diagnostic and prognostic tools for endometriosis

Endometriosis is a common benign hormone reliant inflammatory gynecological disease that affects fertile aged women and has a considerable economic impact on healthcare systems. Symptoms include intense menstrual pain, persistent pelvic pain, and infertility. It is defined by the existence of endometrium-like tissue developing in ectopic locations outside the uterine cavity and inflammation in the peritoneal cavity. Endometriosis presents with multifactorial etiology, and despite extensive research the etiology is still poorly understood. Diagnostic delay from the onset of the disease to when a conclusive diagnosis is reached is between 7–12 years. There is no known cure, although symptoms can be improved with hormonal medications (which often have multiple side effects and prevent pregnancy), or through surgery which carries its own risk. Current non-invasive tools for diagnosis are not sufficiently dependable, and a definite diagnosis is achieved through laparoscopy or laparotomy. This study was based on two prospective cohorts: The ENDOMET study, including 137 endometriosis patients scheduled for surgery and 62 healthy women, and PROENDO that included 138 endometriosis patients and 33 healthy women. Our long-term goal with the current study was to support the discovery of innovative new tools for efficient diagnosis of endometriosis as well as tools to further understand the etiology and pathogenesis of the disease. We set about achieving this goal by creating a database, EndometDB, based on a relational data model, implemented with PostgreSQL programming language. The database allows e.g., for the exploration of global genome-wide expression patterns in the peritoneum, endometrium, and in endometriosis lesions of endometriosis patients as well as in the peritoneum and endometrium of healthy control women of reproductive age. The data collected in the EndometDB was also used for the development and validation of a symptom and biomarker-based predictive model designed for risk evaluation and early prediction of endometriosis without invasive diagnostic methods. Using the data in the EndometDB we discovered that compared with the eutopic endometrium, the WNT- signaling pathway is one of the molecular pathways that undergo strong changes in endometriosis. We then evaluated the potential role for secreted frizzled-related protein 2 (SFRP-2, a WNT-signaling pathway modulator), in improving endometriosis lesion border detection. The SFRP-2 expression visualizes the lesion better than previously used markers and can be used to better define lesion size and that the surgical excision of the lesions is complete.ENDOMET tietokanta – Keino tunnistaa uusi diagnostinen ja ennustava työkalu endometrioosille Endometrioosi on yleinen hyvänlaatuinen, hormoneista riippuvainen tulehduksellinen lisääntymisikäisten naisten gynekologinen sairaus, joka kuormittaa terveydenhuoltojärjestelmää merkittävästi. Endometrioositaudin oireita ovat mm. voimakas kuukautiskipu, jatkuva lantion alueen kipu ja hedelmättömyys. Sairaus määritellään kohdun limakalvon kaltaisen kudoksen esiintymisenä kohdun ulkopuolella sekä siihen liittyvänä vatsakalvon tulehduksena. Endometrioosin etiologia on monitahoinen, ja laajasta tutkimuksesta huolimatta edelleen huonosti tunnettu. Kesto taudin puhkeamisesta lopullisen diagnoosin saamiseen on usein jopa 7–12 vuotta. Sairauteen ei tunneta parannuskeinoa, mutta oireita voidaan lievittää esimerkiksi hormonaalisilla lääkkeillä (joilla on usein monia sivuvaikutuksia ja jotka estävät raskauden) tai leikkauksella, johon liittyy omat tunnetut riskit. Nykyiset ei-invasiiviset diagnoosityökalut eivät ole riittävän luotettavia sairauden tunnistamiseen, ja varma endometrioosin diagnoosi saavutetaan laparoskopian tai laparotomian avulla. Tämä tutkimus perustui kahteen prospektiiviseen kohorttiin: ENDOMET-tutkimuk-seen, johon osallistui 137 endometrioosipotilasta ja 62 terveellistä naista, sekä PROENDO-tutkimukseen, johon osallistui 138 endometrioosipotilasta ja 33 terveellistä naista. Tässä tutkimuksessa pitkän aikavälin tavoitteemme oli löytää uusia työkalujen endometrioosin diagnosointiin, sekä ymmärtää endometrioosin etiologiaa ja patogeneesiä. Ensimmäisessä vaiheessa loimme EndometDB –tietokannan PostgreSQL-ohjelmointi-kielellä. Tämän osittain avoimeen käyttöön vapautetun tietokannan avulla voidaan tutkia genomin, esimerkiksi kaikkien tunnettujen geenien ilmentymistä peritoneumissa, endo-metriumissa ja endometrioosipotilaiden endometrioosileesioissa EndometDB-tietokantaan kerättyjä tietoja käytettiin oireiden ja biomarkkeripohjaisen ennustemallin kehittämiseen ja validointiin. Malli tuottaa riskinarvioinnin endometrioositaudin varhaiseen ennustamiseen ilman laparoskopiaa. Käyttäen EndometDB-tietokannan tietoja havaitsimme, että endo-metrioositautikudoksessa tapahtui voimakkaita geeni-ilmentymisen muutoksia erityisesti geeneissä, jotka liittyvät WNT-signalointireitin säätelyyn. Keskeisin löydös oli, että SFRP-2 proteiinin ilmentyminen oli huomattavasti koholla endometrioosikudoksessa ja SFRP-2 proteiinin immunohistokemiallinen värjäys erottaa endometrioosin tautikudoksen terveestä kudoksesta aiempia merkkiaineita paremmin. Löydetyllä menetelmällä voidaan siten selvittää tautikudoksen laajuus ja tarvittaessa osoittaa, että leikkauksella on kyetty poistamaan koko sairas kudos

A longitudinal study of the experiences and psychological well-being of Indian surrogates

Study question: What is the psychological well-being of Indian surrogates during and after the surrogacy pregnancy? Summary answer: Surrogates were similar to a matched group of expectant mothers on anxiety and stress. However, they scored higher on depression during and after pregnancy. What is known already: The recent ban on trans-national commercial surrogacy in India has led to urgent policy discussions regarding surrogacy. Whilst previous studies have reported the motivations and experiences of Indian surrogates no studies have systematically examined the psychological well-being of Indian surrogates, especially from a longitudinal perspective. Previous research has shown that Indian surrogates are motivated by financial payment and may face criticism from their family and community due to negative social stigma attached to surrogacy. Indian surrogates often recruited by agencies and mainly live together in a “surrogacy house.” Study design, size, duration: A longitudinal study was conducted comparing surrogates to a matched group of expectant mothers over two time points: (a) during pregnancy (Phase1: 50 surrogates, 70 expectant mothers) and (b) 4–6 months after delivery (Phase 2: 45 surrogates, 49 expectant mothers). The Surrogates were recruited from a fertility clinic in Mumbai and the matched comparison group was recruited from four public hospitals in Mumbai and Delhi. Data collection was completed over 2 years. Participants/materials, setting, methods: Surrogates and expectant mothers were aged between 23 and 36 years. All participants were from a low socio-economic background and had left school before 12–13 years of age. In-depth faceto-face semi-structured interviews and a psychological questionnaire assessing anxiety, stress and depression were administered in Hindi to both groups. Interviews took place in a private setting. Audio recordings of surrogate interviews were later translated and transcribed into English. Main results and the role of chance: Stress and anxiety levels did not significantly differ between the two groups for both phases of the study. For depression, surrogates were found to be significantly more depressed than expectant mothers at phase 1 (p = 0.012) and phase 2 (p = 0.017). Within the surrogacy group, stress and depression did not change during and after pregnancy. However, a non-significant trend was found showing that anxiety decreased after delivery (p = 0.086). No participants reported being coerced into surrogacy, however nearly all kept it a secret from their wider family and community and hence did not face criticism. Surrogates lived at the surrogate house for different durations. During pregnancy, 66% (N = 33/50) reported their experiences of the surrogate house as positive, 24% (N = 12/50) as negative and 10% (N = 5/50) as neutral. After delivery, most surrogates (66%, N = 30/45) reported their experiences of surrogacy to be positive, with the remainder viewing it as neutral (28%) or negative (4%). In addition, most (66%, N = 30/45) reported that they had felt “socially supported and loved” during the surrogacy arrangement by friends in the surrogate hostel, clinic staff or family. Most surrogates did not meet the intending parents (49%, N = 22/45) or the resultant child (75%, N = 34/45). Limitations, reasons for caution: Since the surrogates were recruited from only one clinic, the findings may not be representative of all Indian surrogates. Some were lost to follow-up which may have produced sampling bias. Wider implications of the findings: This is the first study to examine the psychological well-being of surrogates in India. This research is of relevance to current policy discussions in India regarding legislation on surrogacy. Moreover, the findings are of relevance to clinicians, counselors and other professionals involved in surrogacy. Trial registration number: N/A