Improving knowledge management through the support of image examination and data annotation using DICOM structured reporting

[EN] An important effort has been invested on improving the image diagnosis process in different medical areas using information technologies. The field of medical imaging involves two main data types: medical imaging and reports. Developments based on the DICOM standard have demonstrated to be a convenient and widespread solution among the medical community. The main objective of this work is to design a Web application prototype that will be able to improve diagnosis and follow-on of breast cancer patients. It is based on TRENCADIS middleware, which provides a knowledge-oriented storage model composed by federated repositories of DICOM image studies and DICOM-SR medical reports. The full structure and contents of the diagnosis reports are used as metadata for indexing images. The TRENCADIS infrastructure takes full advantage of Grid technologies by deploying multi-resource grid services that enable multiple views (reports schemes) of the knowledge database. The paper presents a real deployment of such Web application prototype in the Dr. Peset Hospital providing radiologists with a tool to create, store and search diagnostic reports based on breast cancer explorations (mammography, magnetic resonance, ultrasound, pre-surgery biopsy and post-surgery biopsy), improving support for diagnostics decisions. A technical details for use cases (outlining enhanced multi-resource grid services communication and processing steps) and interactions between actors and the deployed prototype are described. As a result, information is more structured, the logic is clearer, network messages have been reduced and, in general, the system is more resistant to failures.The authors wish to thank the financial support received from The Spanish Ministry of Education and Science to develop the project "CodeCloud", with reference TIN2010-17804.Salavert Torres, J.; Segrelles Quilis, JD.; Blanquer Espert, I.; Hernández García, V. (2012). Improving knowledge management through the support of image examination and data annotation using DICOM structured reporting. Journal of Biomedical Informatics. 45(6):1066-1074. https://doi.org/10.1016/j.jbi.2012.07.004S1066107445

Prototypes for Content-Based Image Retrieval in Clinical Practice

Content-based image retrieval (CBIR) has been proposed as key technology for computer-aided diagnostics (CAD). This paper reviews the state of the art and future challenges in CBIR for CAD applied to clinical practice

Sistemas interativos e distribuídos para telemedicina

doutoramento Ciências da ComputaçãoDurante as últimas décadas, as organizações de saúde têm vindo a adotar continuadamente as tecnologias de informação para melhorar o funcionamento dos seus serviços. Recentemente, em parte devido à crise financeira, algumas reformas no sector de saúde incentivaram o aparecimento de novas soluções de telemedicina para otimizar a utilização de recursos humanos e de equipamentos. Algumas tecnologias como a computação em nuvem, a computação móvel e os sistemas Web, têm sido importantes para o sucesso destas novas aplicações de telemedicina. As funcionalidades emergentes de computação distribuída facilitam a ligação de comunidades médicas, promovem serviços de telemedicina e a colaboração em tempo real. Também são evidentes algumas vantagens que os dispositivos móveis podem introduzir, tais como facilitar o trabalho remoto a qualquer hora e em qualquer lugar. Por outro lado, muitas funcionalidades que se tornaram comuns nas redes sociais, tais como a partilha de dados, a troca de mensagens, os fóruns de discussão e a videoconferência, têm o potencial para promover a colaboração no sector da saúde. Esta tese teve como objetivo principal investigar soluções computacionais mais ágeis que permitam promover a partilha de dados clínicos e facilitar a criação de fluxos de trabalho colaborativos em radiologia. Através da exploração das atuais tecnologias Web e de computação móvel, concebemos uma solução ubíqua para a visualização de imagens médicas e desenvolvemos um sistema colaborativo para a área de radiologia, baseado na tecnologia da computação em nuvem. Neste percurso, foram investigadas metodologias de mineração de texto, de representação semântica e de recuperação de informação baseada no conteúdo da imagem. Para garantir a privacidade dos pacientes e agilizar o processo de partilha de dados em ambientes colaborativos, propomos ainda uma metodologia que usa aprendizagem automática para anonimizar as imagens médicasDuring the last decades, healthcare organizations have been increasingly relying on information technologies to improve their services. At the same time, the optimization of resources, both professionals and equipment, have promoted the emergence of telemedicine solutions. Some technologies including cloud computing, mobile computing, web systems and distributed computing can be used to facilitate the creation of medical communities, and the promotion of telemedicine services and real-time collaboration. On the other hand, many features that have become commonplace in social networks, such as data sharing, message exchange, discussion forums, and a videoconference, have also the potential to foster collaboration in the health sector. The main objective of this research work was to investigate computational solutions that allow us to promote the sharing of clinical data and to facilitate the creation of collaborative workflows in radiology. By exploring computing and mobile computing technologies, we have designed a solution for medical imaging visualization, and developed a collaborative system for radiology, based on cloud computing technology. To extract more information from data, we investigated several methodologies such as text mining, semantic representation, content-based information retrieval. Finally, to ensure patient privacy and to streamline the data sharing in collaborative environments, we propose a machine learning methodology to anonymize medical images

A viewpoint-based case-based reasoning approach utilising an enterprise architecture ontology for experience management

The accessibility of project knowledge obtained from experiences is an important and crucial issue in enterprises. This information need about project knowledge can be different from one person to another depending on the different roles he or she has. Therefore, a new ontology-based case-based reasoning (OBCBR) approach that utilises an enterprise ontology is introduced in this article to improve the accessibility of this project knowledge. Utilising an enterprise ontology improves the case-based reasoning (CBR) system through the systematic inclusion of enterprisespecific knowledge. This enterprise-specific knowledge is captured using the overall structure given by the enterprise ontology named ArchiMEO, which is a partial ontological realisation of the enterprise architecture framework (EAF) ArchiMate. This ontological representation, containing historical cases and specific enterprise domain knowledge, is applied in a new OBCBR approach. To support the different information needs of different stakeholders, this OBCBR approach has been built in such a way that different views, viewpoints, concerns and stakeholders can be considered. This is realised using a case viewpoint model derived from the ISO/IEC/IEEE 42010 standard. The introduced approach was implemented as a demonstrator and evaluated using an application case that has been elicited from a business partner in the Swiss research project.This work was supported in part by the Commission for Technology and Innovation (CTI) of the Swiss Confederation under Grant 14575.1 PFES-ES and the ELO Digital Office CH AG.http://www.tandfonline.com/loi/teis202018-04-30hb2017Information Scienc

Identification of novel miRNAs as diagnostic molecules for detection of breast cancer using in silico approaches

Magister Scientiae - MScBreast cancer (BC) is the most common cancer in women worldwide, and is the second most common cancer in the world, responsible for more than 500 000 deaths annually. Estimates are that 1 in 8 women will develop BC in their lifetime. In South Africa, BC in women affects about 16.6 % of the population and could see a 78 % increase in cases by 2030. The failure of conventional diagnostic tools to detect BC from an early onset has revealed the need for diagnostic tools that would enable early diagnosis of BC. The current diagnostic tools include breast self-examination, mammography magnetic resonance imaging, ultrasonography and serum biomarkers; BRACA1, BRACA2, HER2. These conventional methods lack sensitivity, specificity and positive predictive value, and some of these diagnostic tools may be expensive and quite invasive. Therefore, novel diagnostic tools such as microRNAs which address the short comings of current methods are required for early diagnosis as well as BC management. MicroRNAs are a class of non-coding RNA molecules, which are important in RNA stability and gene expression. Various methodologies have been employed to identify novel microRNAs for diagnostics such as bioinformatics, also referred to as in silico analysis. The aim of this study is to identify novel microRNAs that can potentially detect BC at its earliest stage

Bioinformatics and Machine Learning for Cancer Biology

Cancer is a leading cause of death worldwide, claiming millions of lives each year. Cancer biology is an essential research field to understand how cancer develops, evolves, and responds to therapy. By taking advantage of a series of “omics” technologies (e.g., genomics, transcriptomics, and epigenomics), computational methods in bioinformatics and machine learning can help scientists and researchers to decipher the complexity of cancer heterogeneity, tumorigenesis, and anticancer drug discovery. Particularly, bioinformatics enables the systematic interrogation and analysis of cancer from various perspectives, including genetics, epigenetics, signaling networks, cellular behavior, clinical manifestation, and epidemiology. Moreover, thanks to the influx of next-generation sequencing (NGS) data in the postgenomic era and multiple landmark cancer-focused projects, such as The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), machine learning has a uniquely advantageous role in boosting data-driven cancer research and unraveling novel methods for the prognosis, prediction, and treatment of cancer

Platelet Diagnostics:A novel liquid biomarker

The aim of this thesis is to find a novel liquid biomarker for the detection of cancer and to optimize treatment. The first chapter gives an introduction to the oncology biomarker field and focuses on platelets and their role in cancer. In part 1, we evaluate extracellular vesicles (EVs). EVs are small vesicles released by all types of cells, including tumor cells, into the circulation. They carry protein kinases and can be isolated from plasma. We demonstrate that AKT and ERK kinase protein levels in EVs reflect the cellular expression levels and treatment with kinase inhibitors alters their concentration, depending on the clinical response to the drug. Therefore, EVs may provide a promising biomarker biosource for monitoring of treatment responses. Part 2 starts with reviews describing the function and role of platelets in greater depth. Chapter 3 focusses on thrombocytogenesis and several biological processes in which platelets play a role. Furthermore, the RNA processing machineries harboured by platelets are discussed. Both chapter 3 and 4 evaluate the change platelets undergo after being exposed to tumor and its environment. The exchange of biomolecules with tumor cells results in educated platelets, so-called tumor educated platelets (TEPs). TEPs play a role in several hallmarks of cancer and have the ability to respond to systemic alterations making them an interesting biomarker. In chapter 5 the diagnostic potential of platelets is first discussed. We determine their potential by sequencing the RNA of 283 platelet samples, of which 228 are patients with cancer, and 55 are healthy controls. We reach an accuracy of 96%. Furthermore, we are able to pinpoint the location of the primary tumor with an accuracy of 71%. In part 3, our developed thromboSeq platform is taken to the next level. Several potential confounding factors are taken into account such as age and comorbidity. We show that particle-swarm optimization (PSO)-enhanced algorithms enable efficient selection of RNA biomarker panels. In a validation cohort we apply these algorithms to non-small-cell lung cancer and reach an accuracy of 88% in late stage (n=518) and early-stage 81% accuracy. Finally, in chapter 7 we describe our wet- and dry-lab protocols in detail. This includes platelet RNA isolation, mRNA amplification, and preparation for next-generation sequencing. The dry-lab protocol describes the automated FASTQ file pre-processing to quantified gene counts, quality controls, data normalization and correction, and swarm intelligence-enhanced support vector machine (SVM) algorithm development. Part 4 focuses on central nervous system (CNS) malignancies especially on glioblastoma. Chapter 8 gives an overview of the different liquid biomarkers for diffuse glioma, the most common primary CNS malignancy. In chapter 9 we assess the specificity of the platelet education due to glioblastoma by comparing the RNA profile of TEPs from glioblastoma patients with a neuroinflammatory disease and brain metastasis patients. This results in a detection accuracy of 80%. Secondly, analysis of patients with glioblastoma versus healthy controls in an independent validation series provide a detection accuracy of 95%. Furthermore, we describe the potential value of platelets as a monitoring biomarker for patients with glioma, distinguishing pseudoprogression from real tumor progression. In part 5 thromboSeq is applied to breast cancer diagnostics both as a screening tool in the general population and in a high risk population, BRCA mutated women. In chapter 11 we first apply our technique to an inflammatory condition, multiple sclerosis (MS). Platelet RNA is used as input for the development of a diagnostic MS classifier capable of detecting MS with 80% accuracy in the independent validation series. In the final part we conclude this thesis with a general discussion of the main findings and suggestions for future research

3rd EGEE User Forum

We have organized this book in a sequence of chapters, each chapter associated with an application or technical theme introduced by an overview of the contents, and a summary of the main conclusions coming from the Forum for the chapter topic. The first chapter gathers all the plenary session keynote addresses, and following this there is a sequence of chapters covering the application flavoured sessions. These are followed by chapters with the flavour of Computer Science and Grid Technology. The final chapter covers the important number of practical demonstrations and posters exhibited at the Forum. Much of the work presented has a direct link to specific areas of Science, and so we have created a Science Index, presented below. In addition, at the end of this book, we provide a complete list of the institutes and countries involved in the User Forum