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Gibaud B, Brenet M, Pasquier G, Gil AV, Bardiès M, Stratakis J, Damilakis J, Van Dooren N, Spaltenstein J, Ratib O. A semantic database for integrated management of image and dosimetric data in low radiation dose research in medical imaging. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2021; 2020:492-501. [PMID: 33936422 PMCID: PMC8075532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Medical ionizing radiation procedures and especially medical imaging are a non negligible source of exposure to patients. Whereas the biological effects of high absorbed doses are relatively well known, the effects of low absorbed doses are still debated. This work presents the development of a computer platform called Image and Radiation Dose BioBank (IRDBB) to manage research data produced in the context of the MEDIRAD project, a European project focusing on research on low doses in the context of medical procedures. More precisely, the paper describes a semantic database linking dosimetric data (such as absorbed doses to organs) to the images corresponding to X-rays exposure (such as CT images) or scintigraphic images (such as SPECT or PET images) that allow measuring the distribution of a radiopharmaceutical. The main contributions of this work are: 1) the implementation of the semantic database of the IRDBB system and 2) an ontology called OntoMEDIRAD covering the domain of discourse involved in MEDIRAD research data, especially many concepts from the DICOM standard modelled according to a realist approach.
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Affiliation(s)
| | | | | | - Alex Vergara Gil
- Centre Recherche en Cancérologie de Toulouse, Toulouse, France
- UMR 1037, INSERM, Université Toulouse III Paul Sabatier, Toulouse, France
| | - Manuel Bardiès
- Centre Recherche en Cancérologie de Toulouse, Toulouse, France
- UMR 1037, INSERM, Université Toulouse III Paul Sabatier, Toulouse, France
| | - John Stratakis
- Medical Physics Department, School of Medicine, University of Crete, Heraklion, Greece
| | - John Damilakis
- Medical Physics Department, School of Medicine, University of Crete, Heraklion, Greece
| | | | | | - Osman Ratib
- Institute of Translational Molecular Imaging, Genève
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Meenan C, Erickson B, Knight N, Fossett J, Olsen E, Mohod P, Chen J, Langer SG. Workflow Lexicons in Healthcare: Validation of the SWIM Lexicon. J Digit Imaging 2017; 30:255-266. [DOI: 10.1007/s10278-016-9935-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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Smith B, Arabandi S, Brochhausen M, Calhoun M, Ciccarese P, Doyle S, Gibaud B, Goldberg I, Kahn CE, Overton J, Tomaszewski J, Gurcan M. Biomedical imaging ontologies: A survey and proposal for future work. J Pathol Inform 2015; 6:37. [PMID: 26167381 PMCID: PMC4485195 DOI: 10.4103/2153-3539.159214] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2015] [Accepted: 04/30/2015] [Indexed: 12/24/2022] Open
Abstract
Background: Ontology is one strategy for promoting interoperability of heterogeneous data through consistent tagging. An ontology is a controlled structured vocabulary consisting of general terms (such as “cell” or “image” or “tissue” or “microscope”) that form the basis for such tagging. These terms are designed to represent the types of entities in the domain of reality that the ontology has been devised to capture; the terms are provided with logical definitions thereby also supporting reasoning over the tagged data. Aim: This paper provides a survey of the biomedical imaging ontologies that have been developed thus far. It outlines the challenges, particularly faced by ontologies in the fields of histopathological imaging and image analysis, and suggests a strategy for addressing these challenges in the example domain of quantitative histopathology imaging. Results and Conclusions: The ultimate goal is to support the multiscale understanding of disease that comes from using interoperable ontologies to integrate imaging data with clinical and genomics data.
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Affiliation(s)
- Barry Smith
- Department of Philosophy, The State University of New York at Buffalo, Buffalo, NY 14260, USA
| | | | - Mathias Brochhausen
- Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
| | - Michael Calhoun
- Department of Health and Human Performance, Elon University, Elon, NC 27244, USA
| | - Paolo Ciccarese
- Harvard Medical School, Massachusetts General Hospital, PerkinElmer Innovation Labs, Boston, MA 02115, USA
| | - Scott Doyle
- Department of Pathology and Anatomical Sciences, University at Buffalo, The State University of New York, Buffalo, NY 14214, USA
| | - Bernard Gibaud
- Laboratoire du Traitement du Signal et de l'Image (LTSI), Inserm Unit 1099, University of Rennes 1, Rennes, France
| | - Ilya Goldberg
- National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA
| | - Charles E Kahn
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | | | - John Tomaszewski
- Department of Pathology and Anatomical Sciences, University at Buffalo, The State University of New York, Buffalo, NY 14214, USA
| | - Metin Gurcan
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
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Ontology-assisted analysis of Web queries to determine the knowledge radiologists seek. J Digit Imaging 2011; 24:160-4. [PMID: 20354755 DOI: 10.1007/s10278-010-9289-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
Abstract
Radiologists frequently search the Web to find information they need to improve their practice, and knowing the types of information they seek could be useful for evaluating Web resources. Our goal was to develop an automated method to categorize unstructured user queries using a controlled terminology and to infer the type of information users seek. We obtained the query logs from two commonly used Web resources for radiology. We created a computer algorithm to associate RadLex-controlled vocabulary terms with the user queries. Using the RadLex hierarchy, we determined the high-level category associated with each RadLex term to infer the type of information users were seeking. To test the hypothesis that the term category assignments to user queries are non-random, we compared the distributions of the term categories in RadLex with those in user queries using the chi square test. Of the 29,669 unique search terms found in user queries, 15,445 (52%) could be mapped to one or more RadLex terms by our algorithm. Each query contained an average of one to two RadLex terms, and the dominant categories of RadLex terms in user queries were diseases and anatomy. While the same types of RadLex terms were predominant in both RadLex itself and user queries, the distribution of types of terms in user queries and RadLex were significantly different (p < 0.0001). We conclude that RadLex can enable processing and categorization of user queries of Web resources and enable understanding the types of information users seek from radiology knowledge resources on the Web.
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Predicting Radiological Panel Opinions Using a Panel of Machine Learning Classifiers. ALGORITHMS 2009. [DOI: 10.3390/a2041473] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Rubin DL. Creating and curating a terminology for radiology: ontology modeling and analysis. J Digit Imaging 2007; 21:355-62. [PMID: 17874267 PMCID: PMC3043845 DOI: 10.1007/s10278-007-9073-0] [Citation(s) in RCA: 80] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2007] [Revised: 08/06/2007] [Accepted: 08/23/2007] [Indexed: 10/22/2022] Open
Abstract
The radiology community has recognized the need to create a standard terminology to improve the clarity of reports, to reduce radiologist variation, to enable access to imaging information, and to improve the quality of practice. This need has recently led to the development of RadLex, a controlled terminology for radiology. The creation of RadLex has proved challenging in several respects: It has been difficult for users to peruse the large RadLex taxonomies and for curators to navigate the complex terminology structure to check it for errors and omissions. In this work, we demonstrate that the RadLex terminology can be translated into an ontology, a representation of terminologies that is both human-browsable and machine-processable. We also show that creating this ontology permits computational analysis of RadLex and enables its use in a variety of computer applications. We believe that adopting an ontology representation of RadLex will permit more widespread use of the terminology and make it easier to collect feedback from the community that will ultimately lead to improving RadLex.
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Affiliation(s)
- Daniel L Rubin
- Section of Medical Informatics, Stanford University, MSOB X-215, 251 Campus Drive, Stanford, CA 94305, USA.
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Rubin DL, Noy NF, Musen MA. Protégé: a tool for managing and using terminology in radiology applications. J Digit Imaging 2007; 20 Suppl 1:34-46. [PMID: 17687607 PMCID: PMC2039856 DOI: 10.1007/s10278-007-9065-0] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2007] [Revised: 07/05/2007] [Accepted: 07/12/2007] [Indexed: 11/24/2022] Open
Abstract
The development of standard terminologies such as RadLex is becoming important in radiology applications, such as structured reporting, teaching file authoring, report indexing, and text mining. The development and maintenance of these terminologies are challenging, however, because there are few specialized tools to help developers to browse, visualize, and edit large taxonomies. Protégé (http://protege.stanford.edu) is an open-source tool that allows developers to create and to manage terminologies and ontologies. It is more than a terminology-editing tool, as it also provides a platform for developers to use the terminologies in end-user applications. There are more than 70,000 registered users of Protégé who are using the system to manage terminologies and ontologies in many different domains. The RadLex project has recently adopted Protégé for managing its radiology terminology. Protégé provides several features particularly useful to managing radiology terminologies: an intuitive graphical user interface for navigating large taxonomies, visualization components for viewing complex term relationships, and a programming interface so developers can create terminology-driven radiology applications. In addition, Protégé has an extensible plug-in architecture, and its large user community has contributed a rich library of components and extensions that provide much additional useful functionalities. In this report, we describe Protégé’s features and its particular advantages in the radiology domain in the creation, maintenance, and use of radiology terminology.
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Affiliation(s)
- Daniel L Rubin
- Section of Medical Informatics, Stanford University, MSOB X-215, 251 Campus Drive, Stanford, CA 94305, USA.
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