Exchanging Data for Breast Cancer Diagnosis on Heterogeneous Grid Platforms

This article describes the process of defining and implementing new components to exchange data between two real GRID-based platforms for breast cancer diagnosis. This highly collaborative work in development phase pretends to allow communication between middleware, namely TRENCADIS and DRI, in different virtual organizations. On the one hand, TRENCADIS is a Service-Oriented Architecture in which the usage of resources is represented with Grid services based on the Open Grid Service Architecture specification (OGSA).On the other hand, DRI is a software platform aimed at reducing the cost of hosting digital repositories of arbitrary nature on Grid infrastructures. TRENCADIS has been deployed in the Dr. Peset Hospital (Valencia, Spain) and DRI has been deployed in the S ao Jo ao Hospital (Porto, Portugal). The final objective of this work in progress is to share medical images and its associated metadata among geographically distributed research institutions, while maintaining confidentiality and privacy of data

On the Use of XML in Medical Imaging Web-Based Applications

The rapid growth of digital technology in medical fields over recent years has increased the need for applications able to manage patient medical records, imaging data, and chart information. Web-based applications are implemented with the purpose to link digital databases, storage and transmission protocols, management of large volumes of data and security concepts, allowing the possibility to read, analyze, and even diagnose remotely from the medical center where the information was acquired. The objective of this paper is to analyze the use of the Extensible Markup Language (XML) language in web-based applications that aid in diagnosis or treatment of patients, considering how this protocol allows indexing and exchanging the huge amount of information associated with each medical case. The purpose of this paper is to point out the main advantages and drawbacks of the XML technology in order to provide key ideas for future web-based applicationsPeer ReviewedPostprint (author's final draft

A Systematic Approach for Using DICOM Structured Reports in Clinical Processes: Focus on Breast Cancer

The final publication is available at Springer via http://dx.doi.org/10.1007/s10278-014-9728-6.This paper describes a methodology for redesigning the clinical processes to manage diagnosis, follow-up, and response to treatment episodes of breast cancer. This methodology includes three fundamental elements: (1) identification of similar and contrasting cases that may be of clinical relevance based upon a target study, (2) codification of reports with standard medical terminologies, and (3) linking and indexing the structured reports obtained with different techniques in a common system. The combination of these elements should lead to improvements in the clinical management of breast cancer patients. The motivation for this work is the adaptation of the clinical processes for breast cancer created by the Valencian Community health authorities to the new techniques available for data processing. To achieve this adaptation, it was necessary to design nine Digital Imaging and Communications in Medicine (DICOM) structured report templates: six diagnosis templates and three summary templates that combine reports from clinical episodes. A prototype system is also described that links the lesion to the reports. Preliminary tests of the prototype have shown that the interoperability among the report templates allows correlating parameters from different reports. Further work is in progress to improve the methodology in order that it can be applied to clinical practice.We thank the subject matter experts for sharing their insights through this study. We are especially appreciative of the efforts of the Radiology Unit and Medical Oncology Unit teams at the University Hospital Dr. Peset. This work was partially supported by the Vicerectorat d'Investigacio de la Universitat Politecnica de Valencia (UPVLC) to develop the project "Mejora del proceso diagnostico del cancer de mama" with reference UPV-FE-2013-8.Medina, R.; Torres Serrano, E.; Segrelles Quilis, JD.; Blanquer Espert, I.; Martí Bonmatí, L.; Almenar-Cubells, D. (2015). A Systematic Approach for Using DICOM Structured Reports in Clinical Processes: Focus on Breast Cancer. Journal of Digital Imaging. 28(2):132-145. doi:10.1007/s10278-014-9728-6S132145282Ratib O: Imaging informatics: From image management to image navigation. Yearb Med Inform 2009; 167–172Oakley J. Digital Imaging: A Primer for Radiographers, Radiologists and Health Care Professionals. Cambridge University Press, 2003.Prokosch HU, Dudeck J: Hospital information systems: Design and development characteristics, impact and future architecture. Elsevier health sciences, 1995Foster I, Kesselman C, Tuecke S. 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A graphical user interface for the retrieval of hierarchically structured documents. Inf Process Manag 2004; 40(2):269–289.Weiss DL, Langlotz CP. Structured reporting: Patient care enhancement or productivity nightmare? Radiology 2008. 249(3):739–747.Yen PY, Bakken S. Review of health information technology usability study methodologies. J Am Med Inform Assoc 2012; 19(3):413–422.Patrick R, Julien G, Christian L, Antoine G. Automatic medical encoding with SNOMED categories. BMC Med Inform Decis Mak 2008; 8(Suppl 1): S1–S6.Lopez-Garcia P, Boeker M, Illarramendi A, Schulz S. Usability-driven pruning of large ontologies: The case of SNOMED CT, J Am Med Inform Assoc 2012; 19:e102-e109.World Health Organization. International Statistical Classification of Diseases and Related Health Problems 10th Revision. http://apps.who.int/classifications/apps/icd/icd10online/ (accessed 29 Jan 2013)American College of Radiology (ACR) Breast Imaging Reporting and Data System Atlas (BI-RADS® Atlas)World Health Organization. International Classification of Diseases for Oncology, 3rd Edition (ICD-O-3). http://www.who.int/classifications/icd/adaptations/oncology/en/index.html (accessed 29 Jan 2013)Greene FL. TNM: Our language of cancer. CA Cancer J Clin 2004; 54(3):129–130.American Joint Committee of Cancer (AJCC). AJCC Cancer Staging Manual. Seventh Edition. Springer, 2010Hussein R, Engelmann U, Schroeter A, Meinzer HP. DICOM structured reporting: Part 1. Overview and characteristics, Radiographics 2004; 24(3):891–896.Sluis D, Lee KP, Mankovich N. DICOM SR - integrating structured data into clinical information systems. Medicamundi 2002; 46(2):31–36.Percha B, Nassif H, Lipson J, Burnside E, Rubin D. Automatic classification of mammography reports by BI-RADS breast tissue composition class. J Am Med Inform Assoc 2012; 19(5):913–916.Ciatto S, Houssami N, Apruzzese A, Bassetti E, Brancato B, Carozzi F, Catarzi S, Lamberini MP, Marcelli G, Pellizzoni R, Pesce B, Risso G, Russo F, Scorsolini A. Reader variability in reporting breast imaging according to BI-RADS assessment categories (the Florence experience). Breast 2006; 15(1):44–51.National Electrical Manufacturers Association (NEMA). Digital Imaging and Communications in Medicine (DICOM). Part 16: Content Mapping Resource. http://medical.nema.org/dicom/2004/04_16PU.PDF (accessed 29 Jan 2013)Dolin RH, Alschuler L, Boyer S, Beebe C, Behlen FM, Biron PV, Shvo AS. HL7 clinical document architecture, release 2. J Am Med Inform Assoc 2006; 13:30–39.Blanquer I, Hernández V, Meseguer JE, Segrelles D. Content-based organisation of virtual repositories of DICOM objects. Future Gener Comput Syst 2009; 25(6):627–637.Blanquer I, Hernández V, Segrelles D, Torres E. Enhancing privacy and authorization control scalability in the grid through ontologies. IEEE Trans Inf Technol Biomed 2009; 12(1):16–24.Salavert J, Maestre C, Segrelles D, Blanquer I, Hernández V, Medina R, Martí L: Grid prototype to support cancer of breast diagnostics in clinic practice. Proc of the 4th. Iberian Grid Infrastructure Conf. Netbiblo, 2010Segrelles D, Franco JM, Medina R, Blanquer I, Salavert J, Hernandez V, Martí L, Díaz G, Ramos R, Guevara MA, González N, Loureiro J, Ramos I. Exchanging data for breast cancer diagnosis on heterogeneous grid platforms. Computing and Informatics 2012; 31(1):3–15.Ali MS, Consens M, Lalmas M. Extended structural relevance framework: A framework for evaluating structured document retrieval. Inf Retrieval 2012; 15:558–590.Welter P, Riesmeier J, Fischer B, Grouls C, Kuhl C, Deserno, TM. Bridging the integration gap between imaging and information systems: A uniform data concept for content-based image retrieval in computer-aided diagnosis. J Am Med Inform Assoc 2011; 18:506–510.Jenkins CW. Application prototyping: A case study. Perform Eval Rev 1981; 10(1):21–27.Generalitat Valenciana. Conselleria de Sanitat. Oncoguía de Cáncer de Mama Comunidad Valenciana. http://publicaciones.san.gva.es/publicaciones/documentos/V.2478-2006.pdf (accessed 29 Jan 2013)Maestre C, Segrelles-Quilis JD, Torres E, Blanquer I, Medina R, Hernández V, Martí L. Assessing the usability of a science gateway for medical knowledge bases with TRENCADIS. J Grid Computing 2012; 10:665–688.Lewis J. IBM computer usability satisfaction questionnaires: Psychometric evaluation and instructions for use. Int J Hum-Comput Interact 1995; 7(1):57–78.Lewis JR. Psychometric evaluation of the PSSUQ using data from five years of usability studies. 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Nanoinformatics knowledge infrastructures: bringing efficient information management to nanomedical research

Nanotechnology represents an area of particular promise and significant opportunity across multiple scientific disciplines. Ongoing nanotechnology research ranges from the characterization of nanoparticles and nanomaterials to the analysis and processing of experimental data seeking correlations between nanoparticles and their functionalities and side effects. Due to their special properties, nanoparticles are suitable for cellular-level diagnostics and therapy, offering numerous applications in medicine, e.g. development of biomedical devices, tissue repair, drug delivery systems and biosensors. In nanomedicine, recent studies are producing large amounts of structural and property data, highlighting the role for computational approaches in information management. While in vitro and in vivo assays are expensive, the cost of computing is falling. Furthermore, improvements in the accuracy of computational methods (e.g. data mining, knowledge discovery, modeling and simulation) have enabled effective tools to automate the extraction, management and storage of these vast data volumes. Since this information is widely distributed, one major issue is how to locate and access data where it resides (which also poses data-sharing limitations). The novel discipline of nanoinformatics addresses the information challenges related to nanotechnology research. In this paper, we summarize the needs and challenges in the field and present an overview of extant initiatives and efforts

A FRAMEWORK FOR BIOPROFILE ANALYSIS OVER GRID

An important trend in modern medicine is towards individualisation of healthcare to tailor care to the needs of the individual. This makes it possible, for example, to personalise diagnosis and treatment to improve outcome. However, the benefits of this can only be fully realised if healthcare and ICT resources are exploited (e.g. to provide access to relevant data, analysis algorithms, knowledge and expertise). Potentially, grid can play an important role in this by allowing sharing of resources and expertise to improve the quality of care. The integration of grid and the new concept of bioprofile represents a new topic in the healthgrid for individualisation of healthcare. A bioprofile represents a personal dynamic "fingerprint" that fuses together a person's current and past bio-history, biopatterns and prognosis. It combines not just data, but also analysis and predictions of future or likely susceptibility to disease, such as brain diseases and cancer. The creation and use of bioprofile require the support of a number of healthcare and ICT technologies and techniques, such as medical imaging and electrophysiology and related facilities, analysis tools, data storage and computation clusters. The need to share clinical data, storage and computation resources between different bioprofile centres creates not only local problems, but also global problems. Existing ICT technologies are inappropriate for bioprofiling because of the difficulties in the use and management of heterogeneous IT resources at different bioprofile centres. Grid as an emerging resource sharing concept fulfils the needs of bioprofile in several aspects, including discovery, access, monitoring and allocation of distributed bioprofile databases, computation resoiuces, bioprofile knowledge bases, etc. However, the challenge of how to integrate the grid and bioprofile technologies together in order to offer an advanced distributed bioprofile environment to support individualized healthcare remains. The aim of this project is to develop a framework for one of the key meta-level bioprofile applications: bioprofile analysis over grid to support individualised healthcare. Bioprofile analysis is a critical part of bioprofiling (i.e. the creation, use and update of bioprofiles). Analysis makes it possible, for example, to extract markers from data for diagnosis and to assess individual's health status. The framework provides a basis for a "grid-based" solution to the challenge of "distributed bioprofile analysis" in bioprofiling. The main contributions of the thesis are fourfold: A. An architecture for bioprofile analysis over grid. The design of a suitable aichitecture is fundamental to the development of any ICT systems. The architecture creates a meaiis for categorisation, determination and organisation of core grid components to support the development and use of grid for bioprofile analysis; B. A service model for bioprofile analysis over grid. The service model proposes a service design principle, a service architecture for bioprofile analysis over grid, and a distributed EEG analysis service model. The service design principle addresses the main service design considerations behind the service model, in the aspects of usability, flexibility, extensibility, reusability, etc. The service architecture identifies the main categories of services and outlines an approach in organising services to realise certain functionalities required by distributed bioprofile analysis applications. The EEG analysis service model demonstrates the utilisation and development of services to enable bioprofile analysis over grid; C. Two grid test-beds and a practical implementation of EEG analysis over grid. The two grid test-beds: the BIOPATTERN grid and PlymGRID are built based on existing grid middleware tools. They provide essential experimental platforms for research in bioprofiling over grid. The work here demonstrates how resources, grid middleware and services can be utilised, organised and implemented to support distributed EEG analysis for early detection of dementia. The distributed Electroencephalography (EEG) analysis environment can be used to support a variety of research activities in EEG analysis; D. A scheme for organising multiple (heterogeneous) descriptions of individual grid entities for knowledge representation of grid. The scheme solves the compatibility and adaptability problems in managing heterogeneous descriptions (i.e. descriptions using different languages and schemas/ontologies) for collaborated representation of a grid environment in different scales. It underpins the concept of bioprofile analysis over grid in the aspect of knowledge-based global coordination between components of bioprofile analysis over grid